This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
A Dynamic Trajectory Planning for Automatic Vehicles Based on Improved Discrete Optimization Method
Technical Paper
2020-01-0120
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
The dynamic trajectory planning problem for automatic vehicles in complex traffic scenarios is investigated in this paper. A hierarchical motion planning framework is developed to complete the complex planning task. An improved dangerous potential field in the curvilinear coordinate system is constructed to describe the collision risk of automatic vehicles accurately instead of the discrete Gaussian convolution algorithm. At the same time, the driving comfort is also considered in order to generate an optimal, smooth, collision-free and feasible path in dynamics. The optimal path can be mapped into the Cartesian coordinate system simply and conveniently. Furthermore, a velocity profile considering practical vehicle dynamics is also presented to improve the safety and the comfort in driving. The effectiveness of the proposed dynamic trajectory planning is verified by numerical simulation for several typical traffic scenarios.
Recommended Content
Topic
Citation
Zeng, P. and Ling, Z., "A Dynamic Trajectory Planning for Automatic Vehicles Based on Improved Discrete Optimization Method," SAE Technical Paper 2020-01-0120, 2020, https://doi.org/10.4271/2020-01-0120.Also In
References
- Bevly , D. et al. Lane Change and Merge Maneuvers for Connected and Automated Vehicles: A Survey IEEE Transactions on Intelligent Vehicles 1 105 120 2016
- Gonzalez , D. et al. A Review of Motion Planning Techniques for Automated Vehicles IEEE Transactions on Intelligent Transportation Systems 17 4 1 11 2015
- Le , V. , Scott , A.Z. , and Polak , J. Autonomous Cars: The Tension between Occupant Experience and Intersection Capacity Transportation Research Part C: Emerging Technologies 52 1 14 2015
- Ziegler , J. et al. Making Bertha Drive-An Autonomous Journey on a Historic Route IEEE Intelligent Transportation Systems Magazine 6 2 8 20 2014
- Li , S.E. , Gao , F. et al. Robust Longitudinal Control of Multi-Vehicle Systems-A Distributed H-Infinity Method IEEE Transactions on Intelligent Transportation Systems 19 9 2779 2788 2018
- Xia , Q. , Gao , F. et al. Decoupled H∞ Control of Automated Vehicular Platoons with Complex Interaction Topologies IET Intelligent Transport Systems 11 2 92 101 2017
- Schwarting , W. , Alonso-Mora , J. , and Rus , D. Planning and Decision-Making for Autonomous Vehicles Annual Review of Control, Robotics, and Autonomous Systems 1 187 210 2018
- Zuo , L. et al. A Hierarchical Path Planning Approach Based on a ∗ and Least-Squares Policy Iteration for Mobile Robots Neurocomputing 170 257 266 2015
- Kala , R. and Warwick , K. Multi-Level Planning for Semi-Autonomous Vehicles in Traffic Scenarios Based on Separation Maximization Journal of Intelligent and Robotic Systems 72 3-4 559 590 2013
- Xuemin , H. , Long , C. et al. Dynamic Path Planning for Autonomous Driving on Various Roads with Avoidance of Static and Moving Obstacles Mechanical Systems and Signal Processing 100 482 500 2018
- Xiaohui , L. , Zhenping , S. et al. Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications IEEE/ASME Transactions on mechatronics 21 2 740 753 2016