Title
Trajectory Planning and Safety Assessment of Autonomous Vehicles Based on Motion Prediction and Model Predictive Control
Abstract
Security problem is a fundamental issue for autonomous vehicles. Trajectory planning is a significant component of autonomous vehicle system, which directly influences the automated traffic safety. In this paper, the motion prediction of other traffic participants is considered. We use Monte Carlo simulation to predict the probabilistic occupancy of the object and give a map from probability statistics to actual scenarios. The non-time-based reference trajectory can be obtained by using high-definition map and lane detection. Then model predictive control is utilized to optimize the reference trajectory according to the current state of autonomous vehicle. Different prediction horizons and coordinate transformation are adopted to optimize the planning. By doing so, the constraint conditions can be easily involved and the result is more intuitive. The probabilistic occupancy of other traffic participants are computed offline and then the obtained results are used in real-time application. Therefore, the real-time computational burden is reduced. The crash probability is put forward to verify the feasibility of real-time trajectory in safety assessment module. Two typical scenarios are analyzed: lane change on the straight road and turning at the intersection. The simulation results illustrate the efficiency of our method.
Year
DOI
Venue
2019
10.1109/TVT.2019.2930684
IEEE Transactions on Vehicular Technology
Keywords
Field
DocType
Trajectory,Autonomous vehicles,Planning,Safety,Roads,Acceleration,Accidents
Coordinate system,Crash,Probability and statistics,Monte Carlo method,Computer science,Control theory,Model predictive control,Electronic engineering,Acceleration,Probabilistic logic,Trajectory
Journal
Volume
Issue
ISSN
68
9
0018-9545
Citations 
PageRank 
References 
3
0.40
0
Authors
6
Name
Order
Citations
PageRank
Yijing Wang128926.87
Zhengxuan Liu230.40
Zhiqiang Zuo333436.94
Zheng Li440.75
Li Wang5113.06
Xiao-Yuan Luo627133.54