Don't Get Yourself into Trouble! Risk-aware Decision-Making for Autonomous Vehicles

TL;DR

This paper presents a risk-aware decision-making framework for autonomous vehicles that combines high-level risk-based path planning with deep reinforcement learning-based control, aiming to enhance safety in complex environments.

Contribution

It introduces a novel integrated framework that combines risk assessment with reinforcement learning for autonomous vehicle decision-making.

Findings

Effective risk-based path planning demonstrated in CARLA simulation.

Improved safety by reacting to risky situations.

Integration of high-level planning with low-level control enhances robustness.

Abstract

Risk is traditionally described as the expected likelihood of an undesirable outcome, such as collisions for autonomous vehicles. Accurately predicting risk or potentially risky situations is critical for the safe operation of autonomous vehicles. In our previous work, we showed that risk could be characterized by two components: 1) the probability of an undesirable outcome and 2) an estimate of how undesirable the outcome is (loss). This paper is an extension to our previous work. In this paper, using our trained deep reinforcement learning model for navigating around crowds, we developed a risk-based decision-making framework for the autonomous vehicle that integrates the high-level risk-based path planning with the reinforcement learning-based low-level control. We evaluated our method in a high-fidelity simulation such as CARLA. This work can improve safety by allowing an autonomous vehicle to one day avoid and react to risky situations.