A Self-Driving Decision Making With Reachable Path Analysis and Interaction-Aware Speed Profiling

Abstract

This paper proposes a behavior planning algorithm for self-driving vehicles to handle lane keeping, speed control considering inter-vehicle space, and collision avoidance under uncertainty. The behavior planning approach is structured as a hierarchically organized Markov Decision Process (MDP) comprising two components: the path planning MDP and the speed profiling MDP. The path planning MDP generates multiple path candidates using lane-change path data collected from human drivers. Evaluation of each path candidate is based on reward and penalty terms. The path planning MDP spans and updates considering the predicted simulation time of the path candidate. Subsequently, the speed profiling MDP determines the optimal sequence of speeds for the host vehicle on the planned path. Evaluation of the path and speed profile utility is performed using reward and penalty terms based on the current states of vehicles and road structure. A unique aspect of this approach is the incorporation of uncertainty-aware collision risk and interaction-aware gap penalty, which account for the uncertainty of perception and traffic motion. Various cut-in scenarios are presented in simulations to demonstrate the effectiveness of the proposed algorithm.