Data-Driven State-Increment Statistical Model and Its Application in Autonomous Driving

The aim of trajectory planning is to generate a feasible, collision-free trajectory to guide an autonomous vehicle from the initial state to the goal state safely. However, it is difficult to guarantee that the trajectory is feasible for the vehicle and the real path of the vehicle is collision-free when the vehicle follows the trajectory. In this paper, a state-increment statistical model (SISM) is proposed to describe the kinodynamic constraints of a vehicle by modeling the controller, the actuator, and the vehicle model jointly. The SISM consists of Gaussian distributions of lateral error increments in all state subspaces which are composed of the curvature radius, the velocity, and the lateral error. It is a data-driven modeling approach that can improve the SISM via increasing the number of samples of the increment-state, which is composed of the state and its corresponding increment of the lateral error. According to the SISM, the experience cost functions are designed to evaluate the trajectories for searching the best one with the lowest cost, and the real path can be predicted directly according to the planned trajectory and the vehicle state. The predicted path can be utilized effectually to evaluate the safety of the vehicle motion.

• Record URL:
  • https://doi.org/10.1109/TITS.2018.2797308
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Ma, Chao
  • Xue, Jianru
  • Liu, Yuehu
  • Yang, Jing
  • Li, Yongqiang
  • Zheng, Nanning
• Publication Date: 2018-12

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 3872-3882
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 19
  • Issue Number: 12
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Intelligent vehicles; Mathematical models; Simulation; Statistical analysis; Trajectory control; Vehicle trajectories
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01690437
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Dec 31 2018 9:08AM