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

arXiv: https://arxiv.org/abs/1909.11538 · HTML: https://mlsanity.com/paper/1909.11538/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 21 of 21)

- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model](https://mlsanity.com/paper/2405.13547/) (2024)
- [SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning](https://mlsanity.com/paper/2104.06506/) (2021)
- [Discretionary Lane-Change Decision and Control via Parameterized Soft Actor-Critic for Hybrid Action Space](https://mlsanity.com/paper/2402.15790/) (2024)
- [Generalized Multi-Objective Reinforcement Learning with Envelope Updates in URLLC-enabled Vehicular Networks](https://mlsanity.com/paper/2405.11331/) (2024)
- [Prediction Based Decision Making for Autonomous Highway Driving](https://mlsanity.com/paper/2209.02106/) (2022)
- [Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms](https://mlsanity.com/paper/2210.16575/) (2022)
- [Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2403.18209/) (2024)
- [Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Driving](https://mlsanity.com/paper/2510.15505/) (2025)
- [Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions](https://mlsanity.com/paper/2103.07903/) (2021)
- [Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2210.11237/) (2022)
- [Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning](https://mlsanity.com/paper/2312.05784/) (2023)
- [DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving](https://mlsanity.com/paper/2210.16567/) (2022)
- [Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers](https://mlsanity.com/paper/2306.11466/) (2023)
- [Autonomous Slalom Maneuver Based on Expert Drivers' Behavior Using Convolutional Neural Network](https://mlsanity.com/paper/2301.07424/) (2023)
- [Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2406.09755/) (2024)
- [Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving](https://mlsanity.com/paper/2306.09055/) (2023)
- [Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change](https://mlsanity.com/paper/2403.00446/) (2024)
- [Learning to Drive Safely with Hybrid Options](https://mlsanity.com/paper/2510.24674/) (2025)
- [Structured Reinforcement Learning for Bayesian Persuasion : Application to Intelligent Interactive Driving](https://mlsanity.com/paper/2607.13576/) (2026)

## Abstract (arXiv)

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.