| 1. |
- Fanani, Nolang, et al.
(författare)
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Multimodal Scale Estimation for Monocular Visual Odometry
- 2017
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Ingår i: 2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017). - : IEEE. - 9781509048045 ; , s. 1714-1721
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Konferensbidrag (refereegranskat)abstract
- Monocular visual odometry / SLAM requires the ability to deal with the scale ambiguity problem, or equivalently to transform the estimated unscaled poses into correctly scaled poses. While propagating the scale from frame to frame is possible, it is very prone to the scale drift effect. We address the problem of monocular scale estimation by proposing a multimodal mechanism of prediction, classification, and correction. Our scale correction scheme combines cues from both dense and sparse ground plane estimation; this makes the proposed method robust towards varying availability and distribution of trackable ground structure. Instead of optimizing the parameters of the ground plane related homography, we parametrize and optimize the underlying motion parameters directly. Furthermore, we employ classifiers to detect scale outliers based on various features (e.g. moments on residuals). We test our method on the challenging KITTI dataset and show that the proposed method is capable to provide scale estimates that are on par with current state-of-the-art monocular methods without using bundle adjustment or RANSAC.
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| 2. |
- Jalalmaab, M., et al.
(författare)
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Guaranteeing persistent feasibility of model predictive motion planning for autonomous vehicles
- 2017
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Ingår i: 28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11-14 June 2017. - 9781509048045 ; , s. 843-848
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Konferensbidrag (refereegranskat)abstract
- Model predictive control (MFC) approach is prone to loss of feasibility due to the limited prediction horizon for decision making. For autonomous vehicle motion planning, many of detected obstacles, which are beyond the prediction horizon, cannot be considered in the instantaneous decisions, and late consideration of them may cause infeasibility. The conditions that guarantee persistent feasibility of a model predictive motion planning scheme are studied in this paper. Maintaining the systems states in a control invariant set of the system guarantees the persistent feasibility of the corresponding MPC scheme. Therefore, the persistent feasibility concern can be expressed as the problem of computing an effective control invariant set of the system and maintaining the system states inside it. In this paper, two approaches are presented to compute control invariant sets for the motion planning problem, the linearization-convexification approach and the brute-force search approach. The control invariant sets calculated via these two approaches are numerically analyzed and compared.
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| 3. |
- Knauss, Alessia, 1983, et al.
(författare)
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Paving the roadway for safety of automated vehicles : An empirical study on testing challenges
- 2017
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Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. - New York : IEEE. - 9781509048045 ; , s. 1873-1880
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Konferensbidrag (refereegranskat)abstract
- The technology in the area of automated vehicles is gaining speed and promises many advantages. However, with the recent introduction of conditionally automated driving, we have also seen accidents. Test protocols for both, conditionally automated (e.g., on highways) and automated vehicles do not exist yet and leave researchers and practitioners with different challenges. For instance, current test procedures do not suffice for fully automated vehicles, which are supposed to be completely in charge for the driving task and have no driver as a back up. This paper presents current challenges of testing the functionality and safety of automated vehicles derived from conducting focus groups and interviews with 26 participants from five countries having a background related to testing automotive safety-related topics. We provide an overview of the state-of-practice of testing active safety features as well as challenges that needs to be addressed in the future to ensure safety for automated vehicles. The major challenges identified through the interviews and focus groups, enriched by literature on this topic are related to 1) virtual testing and simulation, 2) safety, reliability, and quality, 3) sensors and sensor models, 4)required scenario complexity and amount of test cases, and 5)handover of responsibility between the driver and the vehicle. © 2017 IEEE.
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| 4. |
- Liao, Yuan, 1991, et al.
(författare)
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Context-Adaptive support information for truck drivers: An interview study on its contents priority
- 2017
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Ingår i: 28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11-14 June 2017. - 9781509048045 ; , s. 1268-1273
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Konferensbidrag (refereegranskat)abstract
- Truck drivers is a key group to promote road safety. For them, proper priority ranking scheme of content adaptation design benefits the high system effectiveness of in-vehicle driver decision support. Taking Chinese truck drivers as an example, the present study revealed the context-Adaptive support information from the perspective of truck drivers; their perceptions of in-vehicle information contents priority in 6 typical driving contexts. Data of 19 participants from 7 logistics companies were collected using a simulation interview method. Based on qualitative summary and statistical analysis, the results are summarized in two aspects; contextual information priority and impacts of driving experience on it. From the perspective of truck driver requirement, these results provide references for the design of context-Adaptive driver decision support.
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| 5. |
- Liao, Yuan, 1991, et al.
(författare)
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Cross-regional Study on Driver Response Behaviour Patterns and System Acceptance with Triggered Forward Collision Warning
- 2017
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Ingår i: 2017 IEEE Intelligent Vehicles Symposium. - 9781509048045 ; , s. 565-570
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Konferensbidrag (refereegranskat)abstract
- Understanding complex behavior patterns in response to triggered forward collision warning system benefits localized user experience design, especially in safety critical scenarios from a cross-regional perspective. This paper studies driver response behavior patterns towards two alarm timings, in car-following scenarios with different traffic density. Data from 32 participants in China and 30 participants in Sweden were collected using a driving simulator. Differences were observedbetween China group and Sweden group regarding responsebehavior patterns, system acceptance and effectiveness. Seen from obtained results, Chinese drivers were found to steer more frequently than Swedish drivers in response to triggered alarm no matter what alarm timing or traffic density there were. Chinese drivers preferred later alarm timing than Swedish drivers. To better design regional-adaptive human machine interaction of forward collision warning system; some suggestions were produced based on obtained results.
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| 6. |
- Ljungqvist, Oskar, et al.
(författare)
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Lattice-based Motion Planning for a General 2-trailer system
- 2017
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Ingår i: 2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017). - : IEEE. - 9781509048045 ; , s. 819-824
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Konferensbidrag (refereegranskat)abstract
- Motion planning for a general 2-trailer system poses a hard problem for any motion planning algorithm and previous methods have lacked any completeness or optimality guarantees. In this work we present a lattice-based motion planning framework for a general 2-trailer system that is resolution complete and resolution optimal. The solution will satisfy both differential and obstacle imposed constraints and is intended either as a part of an autonomous system or as a driver support system to automatically plan complicated maneuvers in backward and forward motion. The proposed framework relies on a precomputing step that is performed offline to generate a finite set of kinematically feasible motion primitives. These motion primitives are then used to create a regular state lattice that can be searched for a solution using standard graph-search algorithms. To make this graph-search problem tractable for real-time applications a novel parametrization of the reachable state space is proposed where each motion primitive moves the system from and to a selected set of circular equilibrium configurations. The approach is evaluated over three different scenarios and impressive real-time performance is achieved.
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| 7. |
- Mohseni, Fatemeh, et al.
(författare)
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Fuel and Comfort Efficient Cooperative Control for Autonomous Vehicles
- 2017
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Ingår i: 2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017). - : IEEE. - 9781509048045 ; , s. 1631-1636
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Konferensbidrag (refereegranskat)abstract
- In this paper, a cooperative fuel and comfort efficient control for autonomous vehicles is presented in order to perform different traffic maneuvers. The problem is formulated as an optimal control problem in which the cost function takes into account the fuel consumption and passengers comfort, subject to safety and speed constraints. The optimal solution takes into account the comfort and fuel consumption, which is obtained by minimizing a jerk, an acceleration, and a fuel criterion. It is shown that the method can be applied to control different groups of vehicles in different traffic scenarios. Simulation results are used to illustrate the generality property and performance of the proposed approach.
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| 8. |
- Morsali, Mahdi, et al.
(författare)
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Real-time velocity planning for heavy duty truck with obstacle avoidance
- 2017
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Ingår i: 2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017). - : IEEE. - 9781509048045 ; , s. 109-114
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Konferensbidrag (refereegranskat)abstract
- A model predictive controller (MPC) including velocity and path planner is designed for real time calculation of a safe and comfortable velocity and steer angle in a heavy duty vehicle. The calculation time is reduced by finding, based on measurement data, simple roll and motion model. The roll dynamics of the truck is constructed using identification of proposed roll model and it is validated by measurements logged by a heavy duty truck and the suggested model shows good agreement with the measurement data. The safety issues such as rollover prevention and moving obstacle avoidance are taken into account. To increase comfort, acceleration, jerk, steer angle and steer angle rate are limited. The simulation and control algorithm is tested in different scenarios, where the test results show the properties of the algorithm.
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| 9. |
- Ochs, Matthias, et al.
(författare)
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Learning Rank Reduced Interpolation with Principal Component Analysis
- 2017
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Ingår i: 2017 28TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV 2017). - : IEEE. - 9781509048045 ; , s. 1126-1133
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Konferensbidrag (refereegranskat)abstract
- Most iterative optimization algorithms for motion, depth estimation or scene reconstruction, both sparse and dense, rely on a coarse and reliable dense initialization to bootstrap their optimization procedure. This makes techniques important that allow to obtain a dense but still approximative representation of a desired 2D structure (e.g., depth maps, optical flow, disparity maps) from a very sparse measurement of this structure. The method presented here exploits the complete information given by the principal component analysis (PCA), the principal basis and its prior distribution. The method is able to determine a dense reconstruction even if only a very sparse measurement is available. When facing such situations, typically the number of principal components is further reduced which results in a loss of expressiveness of the basis. We overcome this problem and inject prior knowledge in a maximum a posteriori (MAP) approach. We test our approach on the KITTI and the Virtual KITTI dataset and focus on the interpolation of depth maps for driving scenes. The evaluation of the results shows good agreement to the ground truth and is clearly superior to the results of an interpolation by the nearest neighbor method which disregards statistical information.
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| 10. |
- Pereira, Gonçalo Collares, et al.
(författare)
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Lateral Model Predictive Control for Over-Actuated Autonomous Vehicle
- 2017
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Ingår i: 2017 IEEE Intelligent Vehicles Symposium (IV). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509048045 ; , s. 310-316
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Konferensbidrag (refereegranskat)abstract
- In this paper, a lateral controller is proposed for an over-Actuated vehicle. The controller is formulated as a linear time-varying model predictive controller. The aim of the controller is to track a desired path smoothly, by making use of the vehicle crabbing capability (sideways movement) and minimizing the magnitude of curvature used. To do this, not only the error to the path is minimized, but also the error to the desired orientation and the control signals requests. The controller uses an extended kinematic model that takes into consideration the vehicle crabbing capability and is able to track not only kinematically feasible paths, but also plan and track over non-feasible discontinuous paths. Ackermann steering geometry is used to transform the control requests, curvature, and crabbing angle, to wheel angles. Finally, the controller performance is evaluated first by simulation and, after, by means of experimental tests on an over-Actuated autonomous research vehicle.
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| 11. |
- Reuter, S., et al.
(författare)
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A fast implementation of the Labeled Multi-Bernoulli filter using gibbs sampling
- 2017
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Ingår i: 28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11-14 June 2017. - 9781509048045 ; , s. 765-772
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a fast implementation of the Labeled Multi-Bernoulli (LMB) filter based on a joint prediction and update scheme. The joint calculation prevents the treatment of insignificant hypotheses, e.g. considering the disappearance of an object with high existence probability which additionally generated a precise measurement in the received measurement set. Further, a Gibbs sampling approach for generating association hypotheses is presented which drastically reduces the computational complexity compared to Murtys ranked-Assignment algorithm. The proposed Gibbs sampling implementation is compared to the standard implementation of the LMB filter using two scenarios: Tracking vehicles using a multi-sensor setup on a German highway and extended object tracking in an urban scenario using Velodyne data.
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| 12. |
- Savic, Vladimir, 1982, et al.
(författare)
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Distributed algorithm for collision avoidance at road intersections in the presence of communication failures
- 2017
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Ingår i: IEEE Intelligent Vehicles Symposium (IV). - 9781509048045 ; , s. 1005-1012
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Konferensbidrag (refereegranskat)abstract
- Vehicle-to-vehicle (V2V) communication is a crucial component of the future autonomous driving systems since it enables improved awareness of the surrounding environment, even without extensive processing of sensory information. However, V2V communication is prone to failures and delays, so a distributed fault-tolerant approach is required for safe and efficient transportation. In this paper, we focus on the intersection crossing (IC) problem with autonomous vehicles that cooperate via V2V communications, and propose a novel distributed IC algorithm that can handle an unknown and large (yet finite) number of communication failures. Our analysis shows that both safety and liveness requirements are satisfied in all realistic situations. We also found, based on a real data set, that the crossing delay is only slightly increased even in the presence of highly correlated failures.
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| 13. |
- Yin, Hang, 1982, et al.
(författare)
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Mastering data complexity for autonomous driving with adaptive point clouds for urban environments
- 2017
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Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. - New york : IEEE. - 9781509048045 ; , s. 1364-1371
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Konferensbidrag (refereegranskat)abstract
- LiDAR sensors play a crucial role in autonomous driving and advanced driver assistance systems. By firing high-rate laser beams, a LiDAR device is able to project its surroundings as 2D or 3D point cloud, which can be used for different purposes such as object detection, map generation, localization, and navigation. Autonomous vehicles are often equipped with at least one multi-layer LiDAR sensor with 360-degree coverage to include as much information as possible in the point cloud. Such a device generates enormous amount of data which poses a challenge for data storage, real-Time computation, and data transmission, as autonomous vehicles are typically resource-constrained systems. This paper proposes a lightweight and adaptive point cloud data structure to reduce the size of a 3D point cloud. The suggested data structure can be flexibly configured with different parameters to adapt for precision, distance coverage, and reflectivity resolution. The precision of the data structure is evaluated using a 16-layer Velodyne LiDAR sensor (VLP-16) to collect data in the city area of AstaZero proving ground and Gothenburg downtown. Our results show that the adaptive data structure can consume only 1/8th of the original point cloud size and hence, it is particularly suitable for applications with limited hardware resources or certain tolerance to precision of the point cloud. The suggested concept is also generalizable to other types of point cloud providing sensors.
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