Method and apparatus for determining behavior based on deep reinforcement learning for autonomous vehicle merging strategy

Abstract

A deep reinforcement learning-based vehicle action decision apparatus for merging strategy of an autonomous vehicle in an on-ramp merging zone is disclosed. The deep reinforcement learning-based vehicle action decision apparatus comprises an information observation unit for collecting observation information from a sensing module or roadside unit (RSU) of an autonomous vehicle; a policy execution unit for deciding on a current action, including acceleration control and lane change of the autonomous vehicle, based on the current observation information and policy; and a reward determination unit for determining a reward according to the current observation information, the current action, and the next observation information according to the current action, wherein reward in the reward determination unit is determined through a reward term related to speed, lane change, safety distance compliance, and an accident of the autonomous vehicle and a merge reward term related to merge of an autonomous vehicle in the on-ramp merging zone.