Human-like Decision-making at Unsignalized Intersection using Social Value Orientation

TL;DR

This paper introduces a social value orientation-based decision-making approach for automated vehicles at unsignalized intersections, enabling human-like interactions and improving trajectory accuracy through reinforcement learning.

Contribution

It proposes a novel SVO-based decision-making framework that captures human-like social interactions, enhancing AV-HV cooperation at intersections.

Findings

SVO better characterizes inter-vehicle interactions than velocity.

The method accurately reproduces naturalistic driving trajectories.

SVO-based approach improves decision-making at intersections.

Abstract

With the commercial application of automated vehicles (AVs), the sharing of roads between AVs and human-driven vehicles (HVs) becomes a common occurrence in the future. While research has focused on improving the safety and reliability of autonomous driving, it's also crucial to consider collaboration between AVs and HVs. Human-like interaction is a required capability for AVs, especially at common unsignalized intersections, as human drivers of HVs expect to maintain their driving habits for inter-vehicle interactions. This paper uses the social value orientation (SVO) in the decision-making of vehicles to describe the social interaction among multiple vehicles. Specifically, we define the quantitative calculation of the conflict-involved SVO at unsignalized intersections to enhance decision-making based on the reinforcement learning method. We use naturalistic driving scenarios with highly interactive motions for performance evaluation of the proposed method. Experimental results show that SVO is more effective in characterizing inter-vehicle interactions than conventional motion state parameters like velocity, and the proposed method can accurately reproduce naturalistic driving trajectories compared to behavior cloning.