| 1. |
- Bergman, Kristoffer, 1990-, et al.
(författare)
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Improved Path Planning by Tightly Combining Lattice-Based Path Planning and Optimal Control
- 2021
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2379-8858 .- 2379-8904. ; 6:1, s. 57-66
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a unified optimization-based path planning approach to efficiently compute locally optimal solutions to optimal path planning problems in unstructured environments. The approach is motivated by showing that a lattice-based planner can be cast and analyzed as a bilevel optimization problem. This insight is used to integrate a lattice-based planner and an optimal control-based method in a novel way. The lattice-based planner is applied to the problem in a first step using a discretized search space. In a second step, an optimal control-based method is applied using the lattice-based solution as an initial iterate. In contrast to prior work, the system dynamics and objective function used in the first step are chosen to coincide with those used in the second step. As an important consequence, the lattice planner provides a solution which is highly suitable as a warm-start to the optimal control step. This proposed combination makes, in a structured way, benefit of sampling-based methods ability to solve combinatorial parts of the problem and optimal control-based methods ability to obtain locally optimal solutions. Compared to previous work, the proposed approach is shown in simulations to provide significant improvements in terms of computation time, numerical reliability and objective function value.
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| 2. |
- Dandapat, Jyotirindra, et al.
(författare)
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Service Time Maximization for Data Collection in Multi-UAV-Aided Networks
- 2024
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2379-8858 .- 2379-8904. ; 9:1, s. 328-337
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Tidskriftsartikel (refereegranskat)abstract
- Unmanned aerial vehicles (UAVs) have been enormously gaining attention to offload traffic or collect data in wireless networks due to their key attributes, such as mobility, flexibility, and cost-effective deployment. However, the limited onboard energy inhibits the UAV from serving for a longer duration. Therefore, this article studies a UAV-aided network where multiple UAVs are launched to collect data from the mobile nodes. In particular, we aim to maximize the service time of the UAVs by jointly optimizing the three-dimensional (3D) trajectory of the UAVs and resources allocated to each node by the UAVs such that each mobile node receives a minimum specified data rate. To facilitate a solution, we construct an equivalent problem that considers the UAV's energy consumption. In particular, we minimize the maximum energy consumed by the UAVs in each time slot. To solve the problem, an iterative approach is presented that decouples the problem into two sub-problems. The optimal location of the UAVs is computed in the first sub-problem, while resource allocation is carried out in the second sub-problem. These two sub-problems are solved in an iterative manner using the alternating optimization approach. We show that the proposed approach improves the service time of the network by 20% on average compared to the existing approaches.
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| 3. |
- Dhar, Abhishek, et al.
(författare)
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Disturbance-Parametrized Robust Lattice-based Motion Planning
- 2023
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858 .- 2379-8904.
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Tidskriftsartikel (refereegranskat)abstract
- This paper introduces a disturbance-parametrized (DP) robust lattice-based motion-planning framework for nonlinear systems affected by bounded disturbances. A key idea in this work is to rigorously exploit the available knowledge about the disturbance, starting already offline at the time when a library of DP motion primitives is computed and ending not before the motion has been executed online. Given an up-to-date-estimate of the disturbance, the lattice-based motion planner performs a graph search online, to non-conservatively compute a disturbance aware optimal motion plan with formally motivated margins to obstacles. This is done utilizing the DP motion primitives, around which tubes are generated utilizing a suitably designed robust controller. The sizes of the tubes are dependent on the upper bounds of the disturbance appearing in the error between the actual system trajectory and the DP nominal trajectory, which in turn along with the overall optimality of the plan is dependant on the user-selected resolution of the available disturbance estimates. Increasing the resolution of the disturbance parameter results in smaller sizes of tubes around the motion primitives and can significantly reduce the conservativeness compared to traditional approaches, thus increasing the performance of the computed motion plans. The proposed strategy is implemented on an Euler-Lagrange-based ship model which is affected by a significant wind disturbance and the efficiency of the strategy is validated through a suitable simulation example.
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| 4. |
- Fors, Victor, 1990-, et al.
(författare)
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Autonomous Wary Collision Avoidance
- 2021
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; 6:2, s. 353-365
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Tidskriftsartikel (refereegranskat)abstract
- Handling of critical situations is an important part in the architecture of an autonomous vehicle. A controller for autonomous collision avoidance is developed based on a wary strategy that assumes the least tireroad friction for which the maneuver is still feasible. Should the friction be greater, the controller makes use of this and performs better. The controller uses an acceleration-vector reference obtained from optimal control of a friction-limited particle, whose applicability is verified by using numerical optimization on a full vehicle model. By employing an analytical tire model of the tireroad friction limit, to determine slip references for steering and body-slip control, the result is a controller where the computation of its output is explicit and independent of the actual tire-road friction. When evaluated in real-time on a high-fidelity simulation model, the developed controller performs close to that achieved by offline numerical optimization.
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| 5. |
- Fors, Victor, 1990-, et al.
(författare)
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Resilient Branching MPC for Multi-Vehicle Traffic Scenarios Using Adversarial Disturbance Sequences
- 2022
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2379-8858 .- 2379-8904. ; 7:4, s. 838-848
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Tidskriftsartikel (refereegranskat)abstract
- An approach to resilient planning and control of autonomous vehicles in multi-vehicle traffic scenarios is proposed. The proposed method is based on model predictive control (MPC), where alternative predictions of the surrounding traffic are determined automatically such that they are intentionally adversarial to the ego vehicle. This provides robustness against the inherent uncertainty in traffic predictions. To reduce conservatism, an assumption that other agents are of no ill intent is formalized. Simulation results from highway driving scenarios show that the proposed method in real-time negotiates traffic situations out of scope for a nominal MPC approach and performs favorably to state-of-the-art reinforcement-learning approaches without requiring prior training. The results also show that the proposed method performs effectively, with the ability to prune disturbance sequences with a lower risk for the ego vehicle.
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| 6. |
- Kang, Yue, 1989, et al.
(författare)
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Test your self-driving algorithm: An overview of publicly available driving datasets and virtual testing environments
- 2019
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858 .- 2379-8904. ; 4:2, s. 171-185
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Tidskriftsartikel (refereegranskat)abstract
- Many companies aim for delivering systems for autonomous driving reaching out for SAE Level 5. As these systems run much more complex software than typical premium cars of today, a thorough testing strategy is needed. Early prototyping of such systems can be supported using recorded data from on-board and surrounding sensors as long as open-loop testing is applicable; later, though, closed-loop testing is necessary-either by testing on the real vehicle or by using a virtual testing environment. This paper is a substantial extension of our work presented at the 2017 IEEE International Conference on Intelligent Transportation Systems (ITSC) that was surveying the area of publicly available driving datasets. Our previous results are extended by additional datasets and complemented with a summary of publicly available virtual testing environments to support closed-loop testing. As such, a steadily growing number of 37 datasets for open-loop testing and 22 virtual testing environments for closed-loop testing have been surveyed in detailed. Thus, conducting research toward autonomous driving is significantly supported from complementary community efforts: A growing number of publicly accessible datasets allow for experiments with perception approaches or training and testing machine-learning-based algorithms, while virtual testing environments enable end-to-end simulations.
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| 7. |
- Lima, Pedro F., 1990-, et al.
(författare)
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Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck
- 2017
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; 2:4, s. 238-250
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present an algorithm for lateral control of a vehicle – a smooth and accurate model predictive controller. The fundamental difference compared to a standard MPC is that the driving smoothness is directly addressed in the cost function. The controller objective is based on the minimization of the first- and second-order spatial derivatives of the curvature. By doing so, jerky commands to the steering wheel, which could lead to permanent damage on the steering components and vehicle structure, are avoided. A good path tracking accuracy is ensured by adding constraints to avoid deviations from the reference path. Finally, the controller is experimentally tested and evaluated on a Scania construction truck. The evaluation is performed at Scania’s facilities near So ̈derta ̈lje, Sweden via two different paths: a precision track that resembles a mining scenario and a high-speed test track that resembles a highway situation. Even using a linearized kinematic vehicle to predict the vehicle motion, the performance of the proposed controller is encouraging, since the deviation from the path never exceeds 30 cm. It clearly outperforms an industrial pure-pursuit controller in terms of path accuracy and a standard MPC in terms of driving smoothness.
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| 8. |
- Liu, Tong, PhD Candidate, et al.
(författare)
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Computationally Efficient Energy Management for a Parallel Hybrid Electric Vehicle Using Adaptive Dynamic Programming
- 2024
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; 9:2, s. 4085-4099
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Tidskriftsartikel (refereegranskat)abstract
- Hybrid electric vehicles (HEVs) rely on energy management strategies (EMSs) to achieve optimal fuel economy. However, both model- and learning-based EMSs have their respective limitations which negatively affect their performances in online applications. This paper presents a computationally efficient adaptive dynamic programming (ADP) approach that can not only rapidly calculate optimal control actions but also iteratively update the approximated value function (AVF) according to the actual fuel and electricity consumption with limited computation resources. Exploiting the AVF, the engine on/off switch and torque split problems are solved by one-step lookahead approximation and Pontryagin's minimum principle (PMP), respectively. To raise the training speed and reduce the memory space, the tabular value function (VF) is approximated by carefully selected piecewise polynomials via the parametric approximation. The advantages of the proposed EMS are threefold and verified by processor-in-the-loop (PIL) Monte Carlo simulations. First, the fuel efficiency of the proposed EMS is higher than that of an adaptive PMP and close to the theoretical optimum. Second, the new method can adapt to the changed driving conditions after a small number of learning iterations and thus has higher fuel efficiency than a non-adaptive dynamic programming (DP) controller. Third, the computation efficiencies of the proposed AVF and a tabular VF are compared. The concise data structure of the AVF enables faster convergence and saves at least 70% of onboard memory space without obviously increasing the average CPU utilization.
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| 9. |
- Manzinger, Stefanie, et al.
(författare)
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Using Reachable Sets for Trajectory Planning of Automated Vehicles
- 2021
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; 6:2, s. 232-248
-
Tidskriftsartikel (refereegranskat)abstract
- The computational effort of trajectory planning for automated vehicles often increases with the complexity of the traffic situation. This is particularly problematic in safety-critical situations, in which the vehicle must react in a timely manner. We present a novel motion planning approach for automated vehicles, which combines set-based reachability analysis with convex optimization to address this issue. This combination makes it possible to find driving maneuvers even in small and convoluted solution spaces. In contrast to existing work, the computation time of our approach typically decreases, the more complex situations become. We demonstrate the benefits of our motion planner in scenarios from the CommonRoad benchmark suite and validate the approach on a real test vehicle.
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| 10. |
- Mohseni, Fatemeh, 1984-, et al.
(författare)
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Distributed Cooperative MPC for Autonomous Driving in Different Traffic Scenarios
- 2021
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2379-8858 .- 2379-8904. ; 6:2, s. 299-309
-
Tidskriftsartikel (refereegranskat)abstract
- A cooperative control approach for autonomous vehicles is developed in order to perform different complex traffic maneuvers, e.g., double lane-switching or intersection situations. The problem is formulated as a distributed optimal control problem for a system of multiple autonomous vehicles and then solved using a nonlinear Model Predictive Control (MPC) technique, where the distributed approach is used to make the problem computationally feasible in real-time. To provide safety, a collision avoidance constraint is introduced, also in a distributed way. In the proposed method, each vehicle computes its own control inputs using estimated states of neighboring vehicles. In addition, a compatibility constraint is defined that takes collision avoidance into account but also ensures that each vehicle does not deviate significantly from what is expected by neighboring vehicles. The method allows us to construct a cost function for several different traffic scenarios. The asymptotic convergence of the system to the desired destination is proven, in the absence of uncertainty and disturbances, for a sufficiently small MPC control horizon. Simulation results show that the distributed algorithm scales well with increasing number of vehicles.
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| 11. |
- Morsali, Mahdi, 1990-, et al.
(författare)
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Spatio-Temporal Planning in Multi-Vehicle Scenarios for Autonomous Vehicle Using Support Vector Machines
- 2021
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; 6:4, s. 611-621
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Tidskriftsartikel (refereegranskat)abstract
- Efficient trajectory planning of autonomous vehiclesin complex traffic scenarios is of interest both academically andin automotive industry. Time efficiency and safety are of keyimportance and here a two-step procedure is proposed. First, aconvex optimization problem is solved, formulated as a supportvector machine (SVM), in order to represent the surroundingenvironment of the ego vehicle and classify the search spaceas obstacles or obstacle free. This gives a reduced complexitysearch space and an A* algorithm is used in a state space latticein 4 dimensions including position, heading angle and velocityfor simultaneous path and velocity planning. Further, a heuristicderived from the SVM formulation is used in the A* search anda pruning technique is introduced to significantly improve searchefficiency. Solutions from the proposed planner is compared tooptimal solutions computed using optimal control techniques.Three traffic scenarios, a roundabout scenario and two complextakeover maneuvers, with multiple moving obstacles, are used toillustrate the general applicability of the proposed method.
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| 12. |
- Nielsen, Kristin, 1986-, et al.
(författare)
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Multi-Hypothesis SLAM for Non-Static Environments with Reoccurring Landmarks
- 2023
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; 8:4, s. 3191-3203
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Tidskriftsartikel (refereegranskat)abstract
- A static world assumption is often used when considering the simultaneous localization and mapping (SLAM) problem. In reality, especially when long-term autonomy is the objective, this is not valid. This paper studies a scenario where uniquely identifiable landmarks can attend multiple discrete positions, not known a priori . Based on a feature based multi-hypothesis map representation, a multi-hypothesis SLAM algorithm is developed inspired by target tracking theory. The creation of such a map is merged into the SLAM framework allowing any available SLAM method to solve the underlying mapping and localization problem for each hypothesis. A recursively updated hypothesis score allows for hypothesis rejection and prevents exponential growth in the number of hypotheses. The developed method is evaluated in an underground mine application, where physical barriers can be moved in between multiple distinct positions. Simulations are conducted in this environment showing the benefits of the multi-hypothesis approach compared to executing a standard SLAM algorithm. Practical considerations as well as suitable approximations are elaborated upon and experiments on real data further validates the simulated results and show that the multi-hypothesis approach has similar performance in reality as in simulation.
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| 13. |
- Parseh, Masoumeh, 1989-, et al.
(författare)
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A Data-Driven Method Towards Minimizing Collision Severity for Highly Automated Vehicles
- 2021
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; 6:4, s. 723-735
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Tidskriftsartikel (refereegranskat)abstract
- The deployment of autonomous vehicles on public roads calls for the development of methods that are reliably able to mitigate injury severity in case of unavoidable collisions. This study proposes a data-driven motion planning method capable of minimizing injury severity for vehicle occupants in unavoidable collisions. The method is based on establishing a metric that models the relationship between impact location and injury severity using real accident data, and subsequently including it in the cost function of a motion planning framework. The vehicle dynamics and associated constraints are considered through a precomputed trajectory library, which is generated by solving an optimal control problem. This allows for efficient computation as well as an accurate representation of the vehicle. The proposed motion planning approach is evaluated by simulation, and it is shown that the trajectory associated with the minimum cost mitigates the collision severity for occupants of passenger vehicles involved in the collision.
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| 14. |
- Qu, Zhiguo, et al.
(författare)
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QEPP : A Quantum Efficient Privacy Protection Protocol in 6G-Quantum Internet of Vehicles
- 2024
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Ingår i: IEEE Transactions on Intelligent Vehicles. - Piscataway, NJ : IEEE. - 2379-8858 .- 2379-8904. ; 9:1, s. 905-916
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Tidskriftsartikel (refereegranskat)abstract
- The increasing popularity of 6G communication within the Internet of Vehicles (IoV) ecosystem is expected to induce a surge in both user numbers and data volumes. This expansion will cause substantial challenges in ensuring network security and privacy protection, as well as in addressing the associated issue of inadequate cloud computing resources. In this article, we propose a Quantum Efficient Privacy Protection (QEPP) protocol that leverages reversible information hiding in quantum point clouds. This protocol utilizes quantum communication technology in edge-to-cloud communication of the IoV to transmit sensitive information embedded in quantum state data, thereby ensuring privacy protection. It employs quantum error-correction coding and efficient coding techniques to extract information and recover the carriers. In addition, the protocol utilizes an improved quantum Grover algorithm in the cloud to accelerate the processing speed of quantum data. By addressing security vulnerabilities and improving cloud-computing capabilities, the QEPP can effectively accommodate critical requirements, including precision, timeliness, and robust privacy protection. © IEEE
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| 15. |
- Qu, Zhiguo, et al.
(författare)
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QFSM : A Novel Quantum Federated Learning Algorithm for Speech Emotion Recognition With Minimal Gated Unit in 5G IoV
- 2024
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - Piscataway, NJ : IEEE. - 2379-8858 .- 2379-8904.
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Tidskriftsartikel (refereegranskat)abstract
- The technology of speech emotion recognition (SER) has been widely applied in the field of human-computer interaction within the Internet of Vehicles (IoV). The incorporation of emerging technologies such as artificial intelligence and big data has accelerated the advancement of SER technology. However, this reveals challenges such as limited computational resources, data processing inefficiency, and security and privacy concerns. In recent years, quantum machine learning has been applied to the field of intelligent transportation, which has demonstrated its various advantages, including high prediction accuracy, robust noise resistance, and strong security. This study first integrates quantum federated learning (QFL) into 5G IoV using a quantum minimal gated unit (QMGU) recurrent neural network for local training. Then, it proposes a novel quantum federated learning algorithm, QFSM, to further enhance computational efficiency and privacy protection. Experimental results demonstrate that compared to existing algorithms using quantum long short-term memory network or quantum gated recurrent unit models, the QFSM algorithm has a higher recognition accuracy and faster training convergence rate. It also performs better in terms of privacy protection and noise robustness, enhancing its applicability and practicality. © IEEE
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| 16. |
- Renganathan, Vishnu, et al.
(författare)
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Enhancing the Security of Automotive Systems Using Attackability Index
- 2024
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2379-8858 .- 2379-8904. ; 9:1, s. 315-327
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Tidskriftsartikel (refereegranskat)abstract
- Security risk analysis and quantification for automotive systems is a challenging task. This challenge is exacerbated when physical systems are integrated with computation and communication networks to form Cyber-Physical Systems (CPS). The complexity arises from the multitude of attack possibilities within the overall system. This work proposes an attack index based on redundancy in the system and the computational sequence of residual generators. This work considers a nonlinear dynamic model of an automotive system with a communication network. The approach involves using system dynamics to model attack vectors, which are based on the vulnerabilities in the system that are exploited through open network components (like On-Board-Diagnosis (OBD-II)), network segmentation (due to improper gateway implementation), and sensors that are susceptible to adversarial attacks. The redundant and non-redundant parts of the system are identified by considering the sensor configuration and unknown variables. Then, an attack index is derived by analyzing the placement of attack vectors in relation to the redundant and non-redundant parts, using the canonical decomposition of the structural model. The security implications of the residuals are determined by analyzing the computational sequence and the placement of the sensors. Thus, this work promotes the notion of security by design by proposing sensor placement strategies to enhance the overall security index. Finally, it is verified how the proposed attack index and its analysis could be used to enhance automotive security using Model-In-Loop (MIL) simulations.
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| 17. |
- Sidorenko, Galina, 1985-, et al.
(författare)
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Towards a Complete Safety Framework for Longitudinal Driving
- 2022
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Ingår i: IEEE Transactions on Intelligent Vehicles. - Piscataway, NJ : IEEE. - 2379-8858 .- 2379-8904. ; 7:4, s. 809-814
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Tidskriftsartikel (refereegranskat)abstract
- Formal models for the safety validation of autonomous vehicles have become increasingly important. To this end, we present a safety framework for longitudinal automated driving. This framework allows calculating minimum safe inter-vehicular distances for arbitrary ego vehicle control policies. We use this framework to enhance the Responsibility-Sensitive Safety (RSS) model and models based on it, which fail to cover situations where the ego vehicle has a higher decelerating capacity than its preceding vehicle. For arbitrary ego vehicle control policies, we show how our framework can be applied by substituting real (possibly computationally intractable) controllers with upper bounding functions. This comprises a general approach for longitudinal safety, where safety guarantees for the upper-bounded system are equivalent to those for the original system but come at the expense of larger inter-vehicular distances.
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| 18. |
- Tahmasebi, Kaveh Nazem, et al.
(författare)
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A Condition-Aware Stochastic Dynamic Control Strategy for Safe Automated Driving
- 2024
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; , s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Condition-awareness regarding electrical and electronic components is not only significant for predictive maintenance of automotive vehicles but also plays a crucial role in ensuring the operational safety by supporting the detection of anomalies, faults, and degradations over lifetime. In this paper, we present a novel control strategy that combines stochastic dynamic control method with condition-awareness for safe automated driving. In particular, the effectiveness of condition-awareness is supported by two distinct condition-monitoring functions. The first function involves the monitoring of a vehicle's internal health condition using model-based approaches. The second function involves the monitoring of a vehicle's external surrounding conditions, using machine learning and artificial intelligence approaches. For the quantification of current conditions, the results from these monitoring functions are used to create system health indices, which are then utilized by a safety control function for dynamic behavior regulation. The design of this safety control function is based on a chance-constrained model predictive control model, combined with a control barrier function for ensuring safe operation. The novelty of the proposed method lies in a systematic integration of monitored external and internal conditions, estimated component degradation, and remaining useful life, with the controller's dynamic responsiveness. The efficacy of the proposed strategy is evaluated with adaptive cruise control in the presence of various sensory uncertainties.
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| 19. |
- Teng, Siyu, et al.
(författare)
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Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving
- 2023
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; 8:1, s. 673-683
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Tidskriftsartikel (refereegranskat)abstract
- End-to-end autonomous driving provides a simple and efficient framework for autonomous driving systems, which can directly obtain control commands from raw perception data. However, it fails to address stability and interpretability problems in complex urban scenarios. In this paper, we construct a two-stage end-to-end autonomous driving model for complex urban scenarios, named HIIL (Hierarchical Interpretable Imitation Learning), which integrates interpretable BEV mask and steering angle to solve the problems shown above. In Stage One, we propose a pretrained Bird's Eye View (BEV) model which leverages a BEV mask to present an interpretation of the surrounding environment. In Stage Two, we construct an Interpretable Imitation Learning (IIL) model that fuses BEV latent feature from Stage One with an additional steering angle from Pure-Pursuit (PP) algorithm. In the HIIL model, visual information is converted to semantic images by the semantic segmentation network, and the semantic images are encoded to extract the BEV latent feature, which are decoded to predict BEV masks and fed to the IIL as perception data. In this way, the BEV latent feature bridges the BEV and IIL models. Visual information can be supplemented by the calculated steering angle for PP algorithm, speed vector, and location information, thus it could have better performance in complex and terrible scenarios. Our HIIL model meets an urgent requirement for interpretability and robustness of autonomous driving. We validate the proposed model in the CARLA simulator with extensive experiments which show remarkable interpretability, generalization, and robustness capability in unknown scenarios for navigation tasks.
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| 20. |
- Wahlström, Johan, et al.
(författare)
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IMU-based smartphone-to-vehicle positioning
- 2016
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers Inc.. - 2379-8858 .- 2379-8904. ; 1:2, s. 139-147
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we address the problem of using inertial measurements to position a smartphone with respect to a vehiclefixed accelerometer. Using rigid body kinematics, this is cast as a nonlinear filtering problem. Unlike previous publications, we consider the complete three-dimensional kinematics, and do not approximate the angular acceleration to be zero. The accuracy of an estimator based on the unscented Kalman filter is compared with the Cramér-Rao bound. As is illustrated, the estimates can be expected to be better in the horizontal plane than in the vertical direction of the vehicle frame. Moreover, implementation issues are discussed and the system model is motivated by observability arguments. The efficiency of the method is demonstrated in a field study which shows that the horizontal RMSE is in the order of 0.5 [m]. Last, the proposed estimator is benchmarked against the state-of-the-art in left/right classification. The framework can be expected to find use in both insurance telematics and distracted driving solutions.
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| 21. |
- Wahlström, Johan, et al.
(författare)
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Map-Aided Dead-Reckoning Using only Measurements of Speed
- 2016
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; 1:3, s. 244-253
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Tidskriftsartikel (refereegranskat)abstract
- We present a particle-based framework for estimating the position of a vehicle using map information and measurements of speed. The filter propagates the particles' position estimates by means of dead-reckoning, and then updates the particle weights using two measurement functions. The first measurement function is based on the assumption that the lateral force on the vehicle does not exceed critical limits derived from physical constraints. The second is based on the assumption that the driver approaches a target speed derived from the speed limits along the upcoming trajectory. Assuming some prior knowledge of the initial position, performance evaluations of the proposed method indicate that end destinations often can be estimated with an accuracy in the order of 100, m. These results expose the sensitivity and commercial value of speed data collected in many of today's insurance telematics programs, where the data is used to adjust premiums and provide driver feedback. We end by discussing the strengths and weaknesses of different methods for anonymization and privacy preservation in telematics programs.
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| 22. |
- Ward, Erik, et al.
(författare)
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Probabilistic Model for Interaction Aware Planning in Merge Scenarios
- 2017
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; 2:2, s. 133-146
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Tidskriftsartikel (refereegranskat)abstract
- Merge scenarios confront drivers with some of the most complicated driving maneuvers in every day driving, requiring anticipatory reasoning of positions of other vehicles, and the own vehicles future trajectory. In congested traffic it might be impossible to merge without cooperation of up-stream vehicles, therefore, it is essential to gauge the effect of our own trajectory when planning a merge maneuver. For an autonomous vehicle to perform a merge maneuver in congested traffic similar capabilities are required. This includes a model describing the future evolution of the scene that allows for optimizing the autonomous vehicle's planned trajectory with respect to risk, comfort, and dynamical limitations. We present a probabilistic model that explicitly models interaction between vehicles and allows for evaluating the utility of a large number of candidate trajectories of an autonomous vehicle using a receding horizon approach in order to select an appropriate merge maneuver. The model is an extension of the intelligent driver model and the modeled behavior of other vehicles are adjusted using on-line model parameter estimation in order to give better predictions. The prediction model is evaluated using naturalistic traffic data and the merge maneuver planner is evaluated in simulation.
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| 23. |
- Westny, Theodor, 1993-, et al.
(författare)
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MTP-GO : Graph-Based Probabilistic Multi-Agent Trajectory Prediction With Neural ODEs
- 2023
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904. ; 8:9, s. 4223-4236
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Tidskriftsartikel (refereegranskat)abstract
- Enabling resilient autonomous motion planning requires robust predictions of surrounding road users’ future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.
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| 24. |
- Zhao, Lin, 1995-, et al.
(författare)
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Driving Experience and Behavior Change in Remote Driving : An Explorative Experimental Study
- 2024
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858 .- 2379-8904. ; 9:2, s. 3754-3767
-
Tidskriftsartikel (refereegranskat)abstract
- Remote driving plays an essential role in coordinating automated vehicles in some challenging situations. Due to the changed driving environment, the experiences and behaviors of remote drivers would undergo some changes compared to conventional drivers. To study this, a continuous real-life and remote driving experiment is conducted under different driving conditions. In addition, the effect of steering force feedback (SFF) on the driving experience is also investigated. In order to achieve this, three types of SFF modes are compared. According to the results, no SFF significantly worsens the driving experience in both remote and real-life driving. Additionally, less force and returnability on steering wheel are needed in remote driving, and the steering force amplitude appears to influence the steering velocity of remote drivers. Furthermore, there is an increase in lane following deviation during remote driving. Remote drivers are also prone to driving at lower speeds and have a higher steering reversal rate. They also give larger steering angle inputs when crossing the cones in a slalom manoeuvre and cause the car to experience larger lateral acceleration. These findings provide indications on how to design SFF and how driving behavior and experience change in remote driving.
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| 25. |
- Zhao, Lin, et al.
(författare)
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Remote Driving of Road Vehicles : A Survey of Driving Feedback, Latency, Support Control, and Real Applications
- 2024
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE. - 2379-8858 .- 2379-8904.
-
Tidskriftsartikel (refereegranskat)abstract
- This literature survey explores the domain of remote driving of road vehicles within autonomous vehicles, focusing on challenges and state-of-the-art solutions related to driving feedback, latency, support control, as well as remote driving platform and real applications. The advancement towards Level-5 autonomy faces challenges, including sensor reliability and diverse scenario feasibility. Currently, remote driving is identified as vital for commercialization, however, it comes with challenges like low situational awareness, latency, and a lack of comprehensive feedback mechanisms. Solutions proposed include enhancing visual feedback, developing haptic feedback, employing prediction techniques, and use control methods to support driver. This paper reviews the existing literature on remote driving in these fields, revealing research gaps and areas for future studies. Additionally, this paper reviews the industry applications of remote driving and shows the state-of-art use cases.
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| 26. |
- Zhou, Jian, et al.
(författare)
-
Interaction-Aware Motion Planning for Autonomous Vehicles With Multi-Modal Obstacle Uncertainty Predictions
- 2024
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2379-8858 .- 2379-8904. ; 9:1, s. 1305-1319
-
Tidskriftsartikel (refereegranskat)abstract
- This article proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting multiple model Kalman filter (IAIMM-KF) to predict interactive multi-modal maneuvers of surrounding vehicles, and the advantage of model predictive control (MPC) in planning an optimal trajectory in uncertain dynamic environments. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in computing the reference targets and designing the collision-avoidance constraints of MPC for resilient motion planning of the ego vehicle. The MPC achieves safety awareness by incorporating a tunable parameter to adjust the predicted obstacle occupancy in the design of the safety constraints, allowing the approach to achieve a trade-off between performance and robustness. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by MPC to follow the time-varying reference targets and avoid collisions with obstacles. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset.
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| 27. |
- Althoff, Matthias, et al.
(författare)
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Provably-Correct and Comfortable Adaptive Cruise Control
- 2020
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - : Institute of Electrical and Electronics Engineers (IEEE). - 2379-8858. ; , s. 1-1
-
Tidskriftsartikel (refereegranskat)abstract
- Adaptive cruise control is one of the most common comfort features of road vehicles. Despite its large market penetration, current systems are not safe in all driving conditions and require supervision by human drivers. While several previous works have proposed solutions for safe adaptive cruise control, none of these works considers comfort, especially in the event of cut-ins. We provide a novel solution that simultaneously meets our specifications and provides comfort in all driving conditions including cut-ins. This is achieved by an exchangeable nominal controller ensuring comfort combined with a provably correct fail-safe controller that gradually engages an emergency maneuver—this ensures comfort, since most threats are already cleared before emergency braking is fully activated. As a conse- quence, one can easily exchange the nominal controller without having to re-certify the overall system safety. We also provide the first user study for a provably correct adaptive cruise controller. It shows that even though our approach never causes an accident, passengers rate the performance as good as a state-of-the-art solution that does not ensure safety.
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| 28. |
- Andreotti, Eleonora, 1988, et al.
(författare)
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Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations
- 2021
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 6:2, s. 366-375
-
Tidskriftsartikel (refereegranskat)abstract
- This paper introduces a unified mathematical definition for describing commonly used terms encountered in systematical analysis of automated driving systems in mixed-traffic simulations. The most significant contribution of this work is in translating the terms that are clarified previously in literature into a mathematical set and function based format. Our work can be seen as an incremental step towards further formalisation of Domain-Specific-Language (DSL) for scenario representation. We also extended the previous work in the literature to allow more complex scenarios by expanding the model-incompliant information using set-theory to represent the perception capacity of the road-user agents. With this dynamic perception definition, we also support interactive scenarios and are not limited to reactive and pre-defined agent behavior. Our main focus is to give a framework to represent realistic road-user behavior to be used in simulation or computational tool to examine interaction patterns in mixed-traffic conditions. We believe that, by formalising the verbose definitions and extending the previous work in DSL, we can support automatic scenario generation and dynamic/evolving agent behavior models for simulating mixed traffic situations and scenarios. In addition, we can obtain scenarios that are realistic but also can represent rare-conditions that are difficult to extract from field-tests and real driving data repositories.
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| 29. |
- Dahl, John, 1986, et al.
(författare)
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Collision Avoidance: A Literature Review on Threat-Assessment Techniques
- 2019
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 4:1, s. 101-113
-
Tidskriftsartikel (refereegranskat)abstract
- For the last few decades, a lot of attention has been given to intelligent vehicle systems, and in particular to automated safety and collision avoidance solutions. In this paper, we present a literature review and analysis of threat-assessment methods used for collision avoidance. We will cover algorithms that are based on single-behavior threat metrics, optimization methods, formal methods, probabilistic frameworks, and data driven approaches, i.e., machine learning. The different theoretical algorithms are finally discussed in terms of computational complexity, robustness, and most suited applications.
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| 30. |
- Dahl, John, 1986, et al.
(författare)
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Prediction-Uncertainty-Aware Threat Detection for ADAS: A Case Study on Lane-Keeping Assistance
- 2023
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:4, s. 2914-2925
-
Tidskriftsartikel (refereegranskat)abstract
- Advanced driver assistance systems typically support the driver in cases where the driver is likely to fail the driving task. The challenge, from a system perspective, is to accurately detect those cases. Recently, machine learning-based prediction models that are able to estimate the prediction uncertainty in real-time have successfully been introduced for this purpose. However, very little effort has been made on using the prediction uncertainty in the decision-making logic to improve the system's robustness, especially in cases where the input data is affected by noise or anomalies that are not presented in the training data. In this work, four threat-detection methods using uncertainty estimates are proposed and evaluated using a real-world data set. The methods use different strategies for leveraging uncertainty information, where the goal is to ensure that the intervention decision is based on trustworthy predictions. The threat-detection methods' performances are evaluated, using five different learning-based prediction models, in the context of a lane-keeping assistance application.
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| 31. |
- Dogan, Daghan, et al.
(författare)
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Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation
- 2019
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 4:3, s. 486-496
-
Tidskriftsartikel (refereegranskat)abstract
- The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this paper, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this paper, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.
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| 32. |
- Fröhle, Markus, 1984, et al.
(författare)
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Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking
- 2019
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 4:4, s. 609-621
-
Tidskriftsartikel (refereegranskat)abstract
- In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-target state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.
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| 33. |
- Granström, Karl, 1981, et al.
(författare)
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Likelihood-Based Data Association for Extended Object Tracking Using Sampling Methods
- 2018
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 3:1, s. 30-45
-
Tidskriftsartikel (refereegranskat)abstract
- Environment perception is a key enabling technology in autonomous vehicles, and multiple object tracking is an important part of this. The use of high resolution sensors, such as automotive radar and lidar, leads to the extended object tracking problem, with multiple detections per tracked object. For computationally feasible multiple extended object tracking, the data association problem must be handled. Previous work has relied on a two-step approach, using clustering algorithms, together with assignment algorithms, to achieve this. In this paper, we show that it is possible to handle the data association in a single step that works directly on the desired likelihood function. Single step data association is beneficial, because it enables better use of the measurement model and the predicted multiobject density. For single step data association, we use algorithms based on stochastic sampling, and integrate them into a Poisson Multi-Bernoulli Mixture filter. In a simulation study, and in an experiment with Velodyne data acquired in an urban environment, four sampling algorithms are compared to clustering and assignment. The results from the simulations and the experiment show that single-step likelihood-based data association achieves better performance than two-step clustering and assignment data association does.
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| 34. |
- Hansson, Anders, et al.
(författare)
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Lane-Level Map Matching based on HMM
- 2021
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 6:3, s. 430-439
-
Tidskriftsartikel (refereegranskat)abstract
- Lane-level map matching is essential for autonomous driving. In this paper, we propose a Hidden Markov Model (HMM) for matching a trajectory of noisy GPS measurements to the road lanes in which the vehicle records its positions. To our knowledge, this is the first time that HMM is used for lanelevel map matching. Apart from GPS values, the model is further assisted by yaw rate data (converted to a lane change indicator signal) and visual cues in the form of the left and right lane marking types (dashed, solid, etc.). Having defined expressions for the HMM emission and transition probabilities, we evaluate our model to demonstrate that it achieves 95.1% recall and 3.3% median path length error for motorway trajectories.
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| 35. |
- Hoel, Carl-Johan, 1986, et al.
(författare)
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Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
- 2020
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 5:2, s. 294-305
-
Tidskriftsartikel (refereegranskat)abstract
- Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This article introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The method is based on the AlphaGo Zero algorithm, which is extended to a domain with a continuous state space where self-play cannot be used. The framework is applied to two different highway driving cases in a simulated environment and it is shown to perform better than a commonly used baseline method. The strength of combining planning and learning is also illustrated by a comparison to using the Monte Carlo tree search or the neural network policy separately.
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| 36. |
- Liu, Jianan, et al.
(författare)
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Deep Instance Segmentation with Automotive Radar Detection Points
- 2023
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:1, s. 84-94
-
Tidskriftsartikel (refereegranskat)abstract
- Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points. With the development of automotive radar technologies in recent years, instance segmentation becomes possible by using automotive radar. Its data contain contexts such as radar cross section and micro-Doppler effects, and sometimes can provide detection when the field of view is obscured. The outcome from instance segmentation could be potentially used as the input of trackers for tracking targets. The existing methods often utilize a clustering-based classification framework, which fits the need of real-time processing but has limited performance due to minimum information provided by sparse radar detection points. In this paper, we propose an efficient method based on clustering of estimated semantic information to achieve instance segmentation for the sparse radar detection points. In addition, we show that the performance of the proposed approach can be further enhanced by incorporating the visual multi-layer perceptron. The effectiveness of the proposed method is verified by experimental results on the popular RadarScenes dataset, achieving 89.53% mean coverage and 86.97% mean average precision with the IoU threshold of 0.5, which is superior to other approaches in the literature. More significantly, the consumed memory is around 1MB, and the inference time is less than 40ms, indicating that our proposed algorithm is storage and time efficient. These two criteria ensure the practicality of the proposed method in real-world systems.
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| 37. |
- Liu, Jianan, et al.
(författare)
-
GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles
- 2023
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:2, s. 1176-1189
-
Tidskriftsartikel (refereegranskat)abstract
- Multi-object tracking (MOT) is among crucial applications in modern advanced driver assistance systems (ADAS) and autonomous driving (AD) systems. The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry. The development of random finite set (RFS) theory facilitates a mathematically rigorous treatment of the MOT problem, and different variants of RFS-based Bayesian filters have then been proposed. However, their effectiveness in the real ADAS and AD application is still an open problem. In this paper, it is demonstrated that the latest RFS-based Bayesian tracking framework could be superior to typical random vector-based Bayesian tracking framework via a systematic comparative study of both traditional random vector-based Bayesian filters with rule-based heuristic track maintenance and RFS-based Bayesian filters on the nuScenes validation dataset. An RFS-based tracker, namely Poisson multi-Bernoulli filter using the global nearest neighbor (GNN-PMB), is proposed to LiDAR-based MOT tasks. This GNN-PMB tracker is simple to use, and it achieves competitive results on the nuScenes dataset. Specifically, the proposed GNN-PMB tracker outperforms most state-of-the-art LiDAR-only trackers and LiDAR and camera fusion-based trackers, ranking the $3^{rd}$ among all LiDAR-only trackers on nuScenes 3D tracking challenge leader board 1 1 https://bit.ly/3bQJ2CP at the time of submission. Our code is available at https://github.com/chisyliu/GnnPmbTracker .
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| 38. |
- Patel, Raj-Haresh, 1991, et al.
(författare)
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Buffer-Aided Model Predictive Controller to Mitigate Model Mismatches and Localization Errors
- 2018
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 3:4, s. 501-510
-
Tidskriftsartikel (refereegranskat)abstract
- Any vehicle needs to be aware of its localization, destination, and neighboring vehicles' state information for collision free navigation. A centralized controller computes controls for cooperative adaptive cruise control (CACC) vehicles based on the assumed behavior of manually driven vehicles (MDVs) in a mixed vehicle scenario. The assumed behavior of the MDVs may be different from the actual behavior, which gives rise to a model mismatch. The use of erroneous localization information can generate erroneous controls. The presence of a model mismatch and the use of erroneous controls could potentially result into collisions. A controller robust to issues such as localization errors and model mismatches is thus required. This paper proposes a robust model predictive controller, which accounts for localization errors and mitigates model mismatches. Future control values computed by the centralized controller are shared with CACC vehicles and are stored in a buffer. Due to large localization errors or model mismatches when control computations are infeasible, control values from the buffer are used. Simulation results show that the proposed robust controller with buffer can avoid almost the same number of collisions in a scenario impacted by localization errors as that in a scenario with no localization errors despite model mismatch.
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| 39. |
- Ranjbar, Arian, 1992, et al.
(författare)
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Safety Monitoring of Neural Networks Using Unsupervised Feature Learning and Novelty Estimation
- 2022
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 7:3, s. 711-721
-
Tidskriftsartikel (refereegranskat)abstract
- Neural networks are currently suggested to be implemented in several different driving functions of autonomous vehicles. While showing promising results the drawback lies in the difficulty of safety verification and ensuring operation as intended. The aim of this paper is to increase safety when using neural networks, by proposing a monitoring framework based on novelty estimation of incoming driving data. The idea is to use unsupervised instance discrimination to learn a similarity measure across ego-vehicle camera images. By estimating a von Mises-Fisher distribution of expected ego-camera images they can be compared with unexpected novel images. A novelty measurement is inferred through the likelihood of test frames belonging to the expected distribution. The suggested method provides competitive results to several other novelty or anomaly detection algorithms on the CIFAR-10 and CIFAR-100 datasets. It also shows promising results on real world driving scenarios by distinguishing novel driving scenes from the training data of BDD100k. Applied on the identical training-test data split, the method is also able to predict the performance profile of a segmentation network. Finally, examples are provided on how this method can be extended to find novel segments in images.
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| 40. |
- Selvaraj, Yuvaraj, 1990, et al.
(författare)
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Formal Development of Safe Automated Driving Using Differential Dynamic Logic
- 2023
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:1, s. 988-1000
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Tidskriftsartikel (refereegranskat)abstract
- The challenges in providing convincing arguments for safe and correct behavior of automated driving (AD) systems have so far hindered their widespread commercial deployment. Conventional development approaches such as testing and simulation are limited by non-exhaustive analysis, and can thus not guarantee safety in all possible scenarios. Formal methods can provide mathematical proofs that could be used to produce rigorous evidence to support the safety argument. This paper investigates the use of differential dynamic logic and the deductive verification tool KeYmaera X in the development of an AD feature. Specifically, this paper demonstrates how formal models and safety proofs of different design variants of a Decision & Control module can be used in the safety argument of an in-lane AD feature. In doing so, the assumptions and invariant conditions necessary to guarantee safety are identified, and the paper shows how such an analysis helps during the development process in requirement refinement and formulation of the operational design domain. Furthermore, it is shown how the performance of the different models is formally analyzed exhaustively, in all their allowed behaviors.
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| 41. |
- Shen, Zichao, et al.
(författare)
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Analysis of Driving Behavior in Unprotected Left Turns for Autonomous Vehicles using Ensemble Deep Clustering
- 2023
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; In Press
-
Tidskriftsartikel (refereegranskat)abstract
- The advent of autonomous driving technology offers transformative potential in mitigating traffic congestion and enhancing road safety. A particularly challenging aspect of traffic dynamics is the unprotected left turn-a scenario at an intersection where the vehicle intending to turn left does not have a dedicated traffic signal, posing a risk to traffic safety and efficiency. This study investigates the dynamics of unprotected left turns by employing data-driven techniques that analyze multi-vehicle data and trajectory patterns to decode complex interactions and behaviors that occur during this maneuver. Our research targets the subtleties of driver behavior in these situations, employing a novel Ensemble Deep Clustering algorithm that innovatively categorizes driving behaviors based on a combination of learned representations and clustering advancements. The deep clustering component involves an iterative process that refines behavioral categorization, while the ensemble technique enhances the precision of these determinations. Using the INTERACTION Dataset, the proposed model is trained and evaluated to offer a better understanding of the intricate driving behaviors in unprotected left turns at intersections. Through the quantitative analysis and comparison with the baseline, we show the superiority of the algorithm, and the results are also interpretable. This methodology can be utilized to improve the decision-making of autonomous vehicles in such scenarios, thus improving the safety of autonomous vehicles, traffic efficiency, and realizing human-robot interaction between autonomous vehicles and drivers.
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| 42. |
- Strandberg, Kim, 1980, et al.
(författare)
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A Systematic Literature Review on Automotive Digital Forensics: Challenges, Technical Solutions and Data Collection
- 2023
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:2, s. 1350-1367
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Tidskriftsartikel (refereegranskat)abstract
- A modern vehicle has a complex internal architecture and is wirelessly connected to the Internet, other vehicles, and the infrastructure. The risk of cyber attacks and other criminal incidents along with recent road accidents caused by autonomous vehicles calls for more research on automotive digital forensics. Failures in automated driving functions can be caused by hardware and software failures and cyber security issues. Thus, it is imperative to be able to determine and investigate the cause of these failures, something which requires trustable data. However, automotive digital forensics is a relatively new field for the automotive where most existing self-monitoring and diagnostic systems in vehicles only monitor safety-related events. To the best of our knowledge, our work is the first systematic literature review on the current research within this field. We identify and assess over 300 papers published between 2006 - 2021 and further map the relevant papers to different categories based on identified focus areas to give a comprehensive overview of the forensics field and the related research activities. Moreover, we identify forensically relevant data from the literature, link the data to categories, and further map them to required security properties and potential stakeholders. Our categorization makes it easy for practitioners and researchers to quickly find relevant work within a particular sub-field of digital forensics. We believe our contributions can guide digital forensic investigations in automotive and similar areas, such as cyber-physical systems and smart cities, facilitate further research, and serve as a guideline for engineers implementing forensics mechanisms.
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| 43. |
- Westny, Theodor, et al.
(författare)
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MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
- 2023
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:9, s. 4223-4236
-
Tidskriftsartikel (refereegranskat)abstract
- Enabling resilient autonomous motion planning requires robust predictions of surrounding road users' future behavior. In response to this need and the associated challenges, we introduce our model titled MTP-GO. The model encodes the scene using temporal graph neural networks to produce the inputs to an underlying motion model. The motion model is implemented using neural ordinary differential equations where the state-transition functions are learned with the rest of the model. Multimodal probabilistic predictions are obtained by combining the concept of mixture density networks and Kalman filtering. The results illustrate the predictive capabilities of the proposed model across various data sets, outperforming several state-of-the-art methods on a number of metrics.
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| 44. |
- Xiong, Weiyi, et al.
(författare)
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LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion
- 2024
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 9:1, s. 79-92
-
Tidskriftsartikel (refereegranskat)abstract
- As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On the other hand, as a new image view transformation strategy, “sampling” has been applied in a few image-based detectors and shown to outperform the widely applied “depth-based splatting” proposed in Lift-Splat-Shoot (LSS), even without image depth prediction. However, the potential of “sampling” is not fully unleashed. This paper investigates the “sampling” view transformation strategy on the camera and 4D imaging radar fusion-based 3D object detection. LiDAR Excluded Lean (LXL) model, predicted image depth distribution maps and radar 3D occupancy grids are generated from image perspective view (PV) features and radar bird's eye view (BEV) features, respectively. They are sent to the core of LXL, called “radar occupancy-assisted depth-based sampling”, to aid image view transformation. We demonstrated that more accurate view transformation can be performed by introducing image depths and radar information to enhance the “sampling” strategy. Experiments on VoD and TJ4DRadSet datasets show that the proposed method outperforms the state-of-the-art 3D object detection methods by a significant margin without bells and whistles. Ablation studies demonstrate that our method performs the best among different enhancement settings.
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| 45. |
- Zhang, Chi, et al.
(författare)
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Spatial-Temporal-Spectral LSTM: A Transferable Model for Pedestrian Trajectory Prediction
- 2023
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858.
-
Tidskriftsartikel (refereegranskat)abstract
- Predicting the trajectories of pedestrians is critical for developing safe advanced driver assistance systems and autonomous driving systems. Most existing models for pedestrian trajectory prediction focused on a single dataset without considering the transferability to other previously unseen datasets. This leads to poor performance on new unseen datasets and hinders leveraging off-the-shelf labeled datasets and models. In this paper, we propose a transferable model, namely the “Spatial-Temporal-Spectral (STS) LSTM” model, that represents the motion pattern of pedestrians with spatial, temporal, and spectral domain information. Quantitative results and visualizations indicate that our proposed spatial-temporal-spectral representation enables the model to learn generic motion patterns and improves the performance on both source and target datasets. We reveal the transferability of three commonly used network structures, including long short-term memory networks (LSTMs), convolutional neural networks (CNNs), and Transformers, and employ the LSTM structure with negative log-likelihood loss in our model since it has the best transferability. The proposed STS LSTM model demonstrates good prediction accuracy when transferring to target datasets without any prior knowledge, and has a faster inference speed compared to the state-of-the-art models. Our work addresses the gap in learning knowledge from source datasets and transferring it to target datasets in the field of pedestrian trajectory prediction, and enables the reuse of publicly available off-the-shelf datasets.
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| 46. |
- Zhou, Jian, et al.
(författare)
-
Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty Predictions
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858.
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes an interaction and safety-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates the ability of the interaction-aware interacting multiple model Kalman filter (IAIMM-KF) to predict interactive multi-modal maneuvers of surrounding vehicles, and the advantage of model predictive control (MPC) in planning an optimal trajectory in uncertain dynamic environments. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in computing the reference targets and designing the collision-avoidance constraints of MPC for resilient motion planning of the ego vehicle. The MPC achieves safety awareness by incorporating a tunable parameter to adjust the predicted obstacle occupancy in the design of the safety constraints, allowing the approach to achieve a trade-off between performance and robustness. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by MPC to follow the time-varying reference targets and avoid collisions with obstacles. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset.
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| 47. |
- Åsljung, Daniel, 1989, et al.
(författare)
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Using Extreme Value Theory for Vehicle Level Safety Validation and Implications for Autonomous Vehicles
- 2017
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 2:4, s. 288-297
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Tidskriftsartikel (refereegranskat)abstract
- Much effort is put right now into how to make autonomous vehicles as capable as possible in order to be able to replace humans as drivers. Less focus is put into how to ensure that this transition happens in a safe way that we can put trust in. The verification of the extreme dependability requirements connected to safety is expected to be one of the largest challenges to overcome in the commercialization of autonomous vehicles. Using traditional statistical methods to validate complete vehicle safety would require the vehicle to cover extreme distances to show that collisions occur rare enough. However, recent research has shown the possibility of using near-collisions in order to estimate the frequency of actual collisions using Extreme Value Theory. To use this method, there is a need for a measure related to the closeness of a collision. This paper shows that the choice of this threat measure has a significant impact on the inferences drawn from the data. With the right measure, this method can be used to validate the safety of a vehicle. This, while keeping the validity high and the data required lower than the state of the art statistical methods.
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