| 1. |
- Kang, Yue, 1989, et al.
(författare)
-
Test your self-driving algorithm: An overview of publicly available driving datasets and virtual testing environments
- 2019
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858 .- 2379-8904. ; 4:2, s. 171-185
-
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.
|
|
| 2. |
- Yu, Yushu, 1985, et al.
(författare)
-
Formally Robust and Safe Trajectory Planning and Tracking for Autonomous Vehicles
- 2022
-
Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 23:12, s. 22971-22987
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, a safe trajectory planning and tracking algorithm for autonomous vehicles is proposed. Specially, the safety problem considers the geometric constraints including the obstacle avoiding and the road side constraints, and the non-convex input constraints defined from the sideslip angle of the wheels and input boundedness. Control barrier function (CBF) is adopted to deal with the state and input constraints and generate nominal trajectory. For this purpose, the nominal dynamics of the autonomous vehicle is defined as the virtual dynamics, from which the CBF safety certificates are derived. By constructing appropriate feedback control, the tracking error of the actual trajectory can be bounded into a tube, which guarantees the geometric safety of the actual vehicle. Two safety certificates, the bearing and the distance safety certificates are derived for multiple-obstacle avoidance. In order to deal with the non-convex input constraints, a safe braking maneuver is carefully considered. The feasible initial velocity set for safe braking is proposed as a part of state constraints. The feasibility and the safety of the overall system is proved. The algorithm to synthesis the CBF certificates for multiple-obstacle avoidance, and the input constraints is proposed. Simulation results from an autonomous vehicle including disturbances demonstrate the feasibility of the algorithm. The software implementation of the proposed algorithm was developed in C++ intended for real-world testing.
|
|
