Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (12): 2852-2863.doi: 10.13229/j.cnki.jdxbgxb20210457

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Optimization⁃based lane changing trajectory planning approach for autonomous vehicles on two⁃lane road

Hao-nan PENG1(),Ming-huan TANG1,Qi-wen ZHA1,Wei-zhong WANG1(),Wei-da WANG2,Chang-le XIANG2,Yu-long LIU3   

  1. 1.China Academy of Industrial Internet，Beijing 100102，China
    2.School of Mechanical Engineering，Beijing Institute of Technology，Beijing 100081，China
    3.School of Vehicle and Mobility，Tsinghua University，Beijing 100084，China
  • Received:2021-05-23 Online:2022-12-01 Published:2022-12-08
  • Contact: Wei-zhong WANG E-mail:penghaonanteddy@126.com;wangweizhong@china-aii.com

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Abstract:

For two lane traffic scenarios， a decision-making and optimization-based lane changing trajectory planning method for autonomous vehicles was proposed. Firstly， a risk assessment method based on the Bayesian probability theory was designed to obtain the conditional probability of the lane safety in the current scenario； then， a behavior decision-making method based on the safety utility was designed. According to the risk assessment Bayesian network and decision graph， the behavior decision of lane keeping or lane changing was made. An optimization-based trajectory planning method based on the nonlinear MPC was proposed at the trajectory planning layer， which imitates the excellent driver to give the weight coefficient of each optimized objective function to solve the optimal desired lane changing trajectory. At last，The effectiveness of the decision-making and trajectory planning method was verified by the simulation. The simulation results show that the risk assessment， behavior decision-making and optimization-based trajectory planning method can make the safe behavior decision and plan the optimal lane changing trajectory for autonomous vehicles in different risk scenarios， so that the autonomous vehicle can change the lane safely and quickly.

Key words: autonomous vehicles, lane changing risk assessment, decision making, trajectory planning, nonlinear model predictive control （MPC）

CLC Number: 

  • U461.1

Fig.1

Schematic diagram of two lane traffic scenario"

Fig.2

Overall architecture diagram"

Fig.3

Bayesian flow chart of risk assessment and behavior decision"

Fig.4

Schematic diagram of vehicle kinematicspredicted model"

Fig.5

Schematic diagram of vehicle safety region"

Table 1

Change rule of the optimized objective function weight coefficient of excellent drivers facing different risk scenarios"

权重系数场景类别
低风险场景高风险场景
目标横向位置减小增大

防撞安全距离

纵向巡航速度

控制输入及其导数

高阶动力学特性

不变

减小

增大

增大

不变

增大

减小

减小

Fig.6

Desired trajectory figure of the autonomous vehicle in the general risk scenario"

Fig.7

Desired velocity figure of the autonomous vehicle in the general risk scenario"

Fig.8

Desired velocity figure of the autonomous vehicle in the general risk scenario"

Fig.9

Yaw angle figure of the autonomous vehicle in the general risk scenario"

Fig.10

Front steering angle figure of the autonomous vehicle in the general risk scenario"

Fig.11

Desired trajectory figure of the autonomous vehicle in the high risk scenario"

Fig.12

Desired velocity figure of the autonomous vehicle in the high risk scenario"

Fig.13

Desired velocity figure of the autonomous vehicle in the high risk scenario"

Fig.14

Yaw angle figure of the autonomous vehicle in the high risk scenario"

Fig.15

Front steering angle figure of the autonomous vehicle in the high risk scenario"

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