Intention estimation and controllable behaviour models for traffic merges

Abstract

This work focuses on decision making for automated driving vehicles in interaction rich scenarios like traffic merges in a flexibly assertive yet safe manner. We propose a Q-learning based approach, that takes in active intention inferences as additional inputs besides the directly observed state inputs. The outputs of Q-function are processed to select a decision by a modulation function, which can control how assertively or defensively the agent behaves.