Precise Decision-Making Learning for Automated Vehicles in Lane-Change Scenario Based on Parameter Description

Abstract:

To promote safety and fully consider human drivers' acceptance， precise decision-making is realized for automated vehicles under the lane-change scenario in this paper. More specifically， automated vehicles not only decide to change lanes or not but also decide specific microcosmic behaviors， such as lane-change time and expected acceleration. Thus， precise decisions for lane-change are described with three parameters and learned by reinforcement learning. The rationality of such parameter-based precise decisions is shown in two aspects. First， different values of decision parameters will notably influence the planned trajectory， which means other microcosmic behaviors will be a significant uncertainty when they are not precisely decided in the decision-making layer. Secondly， based on the analysis of real traffic data， NGSIM， changeable lane-change time， and expected acceleration are revealed in lane-change behaviors， which is seldom explicitly considered in the decision-making layer of current researches. The decision parameters that include lane-change time and expected acceleration are learned with kernel-based least-squares policy iteration reinforcement learning （KLSPI）. Safety， current driver's willingness， and average human driving style are considered in the reward function. Simulation results demonstrate that using reinforcement learning （RL） to learn decision parameters can realize more precise decisions， promote safety performance， and imitate human drivers' behaviors in the lane-change scenario.