Autonomous Driving at Intersections: A Behavior-Oriented Critical-Turning-Point Approach for Decision Making
Keqi Shu, Huilong Yu, Xingxin Chen, Shen Li, Long Chen, Qi Wang, Li Li, Dongpu Cao
Abstract
Left turning at unsignalized intersectionis one of the most challenging tasks for urban automated driving, due to the various shapes of intersections and rapidly changing nature of the driving scenarios. This article addresses the challenges of decision making at highly uncertain intersections of different shapes by proposing a generalized critical turning point (CTP)-based hierarchical decision-making and planning method. The high-level planner takes the road map information and generates CTPs using a parameterized extraction model that is proposed and verified by naturalistic driving data. The CTPs are used to generate behavior-oriented paths that could be adapted to various intersections. These modifications help to assure high searching efficiency of the planning process. The low-level planner makes real-time, 2-D planning using a partially observable Markov decision process solver, which can handle the uncertainties of the intersections and make less conservative yet safe actions. With proper modifications, our proposed method can make commute-efficient 2-D planning decisions at unsignalized intersections of various shapes in real time.