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A Trajectory Planning Method for Different Drivers in the Curve Condition
Technical Paper
2021-01-7006
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Lane Centering Control System (LCCS) is a lateral Advanced Driving Assistance System (ADAS) with low acceptance. One of the main reasons is that the centering trajectory can’t satisfy different drivers, which is more obvious in the curve condition. So LCCS adaptive to different drivers needs to be designed. The trajectory planning module is an important part for LCCS. It generates trajectory according to the road information for the vehicle control module to track. This paper uses road information obtained from the scenario established in Prescan, and the trajectory planning method proposed can generate trajectories for different drivers in the curve condition. To achieve the goal, this paper proposes a trajectory planning method which contains lateral path planning and longitudinal speed planning. Firstly, the overall strategy of “road equidistant segments division” is used to describe the road information. Secondly, the path planning method establishes objective functions for four typical driving modes respectively, including shortest length, minimum curvature, centering driving, and minimum heading angle deviation, which solved by quadratic programming. Then the mixed mode path is obtained by weighting four different paths. Thirdly, a speed planning method is proposed. The speed profile is described by the natural cubic spline and it’s based on the generated path which considers different constraints of longitudinal and lateral acceleration of different drivers and the trajectory is generated by coupling speed profile with the path. Finally, the co-simulation of Matlab/Simulink and Prescan is carried out in different scenarios, and the simulation result is analyzed. The simulation result shows that the trajectory planning method proposed in this paper can generate trajectories for different drivers under the constraints of the tire dynamics, and the feasibility of the algorithm is verified.
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Citation
Pan, W., Chen, H., Ran, W., Xia, T. et al., "A Trajectory Planning Method for Different Drivers in the Curve Condition," SAE Technical Paper 2021-01-7006, 2021, https://doi.org/10.4271/2021-01-7006.Also In
References
- Becker , C. , Yount , L. , Rosen-Levy , S. , and Brewer , J. Functional Safety Assessment of an Automated Lane Centering System NHTSA August, 2018
- Walter , M. , Nitzsche , N. , and Odenthal , D. Lateral Vehicle Guidance Control for Autonomous and Cooperative Driving European Control Conference (ECC) 2014 10.1109/ECC.2014.6862387
- Fleming , J.M. , Allison , C.K. , Yan , X. , Santon , N.A. et al. Adaptive Driver Modelling in ADAS to Improve User Acceptance: A Study Using Naturalistic Data Safety Science 2018 119 10.1016/j.ssci.2018.08.023
- Peter , S. Track Behavior in Curve Areas: Attempt at Typology Journal of Transportation Engineering 13 9 2012 669
- Zhao , B. , Chen , H. , Ran , W. , Yosuke , N. et al. Verification of Adaptive Demand of Lane Centering Control System Automobile Technology 3 2021 1 6
- Johannes , B. , Stefan , F. , and Sabine , S. Sublinear Search Spaces for Shortest Path Planning in Grid and Road Networks Journal of Combinatorial Optimization 42 2021 231 257 10.1007/s10878-201-00777-3
- Cao , H. , Song , X. , and Li , M. A Time-optimal Trajectory Planning and Tracking Method for Autonomous Race Car SAECCE 2018 10.1080/00423114.2018.1497185
- Xu , J. , Zhao , J. , Shao , Y. et al. Predicting the Vehicle’s Trajectory on Complex Roads Considering the Complicated Interaction of Driver-Vehicle-Road System Engineering-Theory & Pratice 34 5 2014
- Lefevre , S. , Carvalho , A. , and Gao , Y. Driver Models for Personalized Driving Assistance Vehicle System Dynamics. 53 12 2015 1705 1720 10.1080/00423114.2015.1062899
- Lefevre , S. , Gao , Y. , Vasquez , D. Lane Keeping Assistance with Learning-Based Driver Model and Model Predictive Control 12th International Symposium on Advanced Vehicle Control 2014
- Alexander , H. , Alexander , W. , Leonhard , H. , Johannes , B. et al. Minimum Curvature Trajectory Planning and Control an Autonomous Race Car Vehicle System Dynamics 58 10 2020 10.1080/00423114.2019.1631455
- Hossein , T. , Mikio , S. , Takasi , O. Adaptive Lane Change and Lane Keeping for Safe and Comfortable Driving Second International Symposium on Future Active Safety Technology 2013
- Cao , H. , Zhao , S. , Song , X. , Bao , S. et al. An Optimal Hierarchical Framework of the Trajectory Following by Convex Optimization for Highly Automated Driving Vehicles Vehicle System Dynamics 57 9 2018 10.1080/00423114.2018.1497185
- Xu , G. , Zhao , H. , and Huang , Z. Optimization method and its MATLAB implementation Beijing Beihang University P
- Bryan , N. and Alonzo , K. Trajectory Generation for Car-like Robots Using Cubic Curvature Polynomials Field and Service Robots 2001