Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment

TL;DR

This paper presents a deep reinforcement learning approach for autonomous lane change decisions in complex, uncertain highway environments, demonstrating superior performance over heuristic methods in noisy traffic scenarios.

Contribution

The study introduces a novel simulation environment and trains a deep RL agent that effectively handles uncertainty and dynamic traffic conditions for lane changing.

Findings

Deep RL agent outperforms heuristic methods in noisy environments

Simulation environment accurately models real-world uncertainties

Approach ensures safety and efficiency in lane change decisions

Abstract

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.