Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
This addresses the problem of safe and agile lane changes for autonomous driving systems, though it appears incremental as it builds on existing deep reinforcement learning techniques.
The paper tackled autonomous lane change decision-making in dynamic and uncertain highway environments by developing a deep reinforcement learning agent, which performed significantly better in noisy scenarios compared to heuristic-based methods.
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.