| 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
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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
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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. 232-248
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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
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Ingår i: IEEE Transactions on Intelligent Vehicles. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2379-8858 .- 2379-8904. ; 6:2, s. 299-309
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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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