Robust AI Driving Strategy for Autonomous Vehicles

TL;DR

This paper presents a robust AI driving strategy for autonomous vehicles that integrates reinforcement learning, vehicle control, and safety mechanisms to improve decision-making across diverse traffic scenarios.

Contribution

It introduces a novel integration of reinforcement learning with control barrier functions for safe, adaptive autonomous driving on highways.

Findings

Enhanced decision-making robustness in varied traffic conditions

Successful integration of reinforcement learning with control barrier functions

Improved safety and adaptability in autonomous highway driving

Abstract

There has been significant progress in sensing, perception, and localization for automated driving, However, due to the wide spectrum of traffic/road structure scenarios and the long tail distribution of human driver behavior, it has remained an open challenge for an intelligent vehicle to always know how to make and execute the best decision on road given available sensing / perception / localization information. In this chapter, we talk about how artificial intelligence and more specifically, reinforcement learning, can take advantage of operational knowledge and safety reflex to make strategical and tactical decisions. We discuss some challenging problems related to the robustness of reinforcement learning solutions and their implications to the practical design of driving strategies for autonomous vehicles. We focus on automated driving on highway and the integration of reinforcement learning, vehicle motion control, and control barrier function, leading to a robust AI driving strategy that can learn and adapt safely.