Citation

Wang P, Gao S, Li L, Cheng S, Zhao H. Arch. Transp. 2020; 53(1): 21-36.

Copyright

(Copyright © 2020, Warsaw University of Technology)

DOI

10.5604/01.3001.0014.1740

PMID

unavailable

Abstract

Autonomous driving vehicle could increase driving efficiency, reduce traffic congestion and improve driving safety, it is considered as the solution of current traffic problems. Decision making systems for autonomous driving vehicles have significant effects on driving performance. The performance of decision making system is affected by its framework and decision making model. In real traffic scenarios, the driving condition of autonomous driving vehicle faced is random and time-varying, the performance of current decision making system is unable to meet the full scene autonomous driving requirements. For autonomous driving vehicle, the division between different driving behaviors needs clear boundary conditions. Typically, in lane change scenario, multiple reasonable driving behavior choices cause conflict of driving state. The fundamental cause of conflict lies in overlapping boundary conditions. To design a decision making system for autonomous driving vehicles, firstly, based on the decomposition of human driver operation process, five basic driving behavior modes are constructed, a driving behavior decision making framework for autonomous driving vehicle based on finite state machine is proposed. Then, to achieve lane change decision making for autonomous driving vehicle, lane change behavior characteristics of human driver lane change maneuver are analyzed and extracted. Based on the analysis, multiple attributes such as driving efficiency and safety are considered, all attributes benefits are quantified and the driving behavior benefit evaluation model is established. By evaluating the benefits of all alternative driving behaviors, the optimal driving behavior for current driving scenario is output. Finally, to verify the performances of the proposed decision making model, a series of real vehicle tests are implemented in different scenarios, the real time performance, effectiveness, and feasibility performance of the proposed method is accessed. The results show that the proposed driving behavior decision making model has good feasibility, real-time performance and multi-choice filtering performance in dynamic traffic scenarios.

Language: en