# Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2002.02667 · HTML: https://mlsanity.com/paper/2002.02667/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 30 of 31)

- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving](https://mlsanity.com/paper/2405.01440/) (2024)
- [A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles](https://mlsanity.com/paper/2105.14218/) (2021)
- [Trustworthy Human-AI Collaboration: Reinforcement Learning with Human Feedback and Physics Knowledge for Safe Autonomous Driving](https://mlsanity.com/paper/2409.00858/) (2024)
- [SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning](https://mlsanity.com/paper/2104.06506/) (2021)
- [Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving](https://mlsanity.com/paper/2306.03220/) (2023)
- [How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies](https://mlsanity.com/paper/2203.08409/) (2022)
- [Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning](https://mlsanity.com/paper/2401.08661/) (2023)
- [Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions](https://mlsanity.com/paper/2402.04168/) (2024)
- [Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2403.18209/) (2024)
- [A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction](https://mlsanity.com/paper/2303.12274/) (2023)
- [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)
- [MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving](https://mlsanity.com/paper/2512.03795/) (2025)
- [DRNet: A Decision-Making Method for Autonomous Lane Changingwith Deep Reinforcement Learning](https://mlsanity.com/paper/2311.01602/) (2023)
- [Development and Assessment of Autonomous Vehicles in Both Fully Automated and Mixed Traffic Conditions](https://mlsanity.com/paper/2312.04805/) (2023)
- [LVLM-MPC Collaboration for Autonomous Driving: A Safety-Aware and Task-Scalable Control Architecture](https://mlsanity.com/paper/2505.04980/) (2025)
- [Autonomous Slalom Maneuver Based on Expert Drivers' Behavior Using Convolutional Neural Network](https://mlsanity.com/paper/2301.07424/) (2023)
- [Integrating Expert Guidance for Efficient Learning of Safe Overtaking in Autonomous Driving Using Deep Reinforcement Learning](https://mlsanity.com/paper/2308.09456/) (2023)
- [Scalable Decentralized Cooperative Platoon using Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2312.06858/) (2023)
- [Algorithmic collusion in a two-sided market: A rideshare example](https://mlsanity.com/paper/2405.02835/) (2024)
- [Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework](https://mlsanity.com/paper/2407.01216/) (2024)
- [Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation](https://mlsanity.com/paper/2407.06317/) (2024)
- [SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees](https://mlsanity.com/paper/2407.16857/) (2024)
- [Introduction to Reinforcement Learning](https://mlsanity.com/paper/2408.07712/) (2024)
- [Design of Reward Function on Reinforcement Learning for Automated Driving](https://mlsanity.com/paper/2503.16559/) (2025)
- [A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach](https://mlsanity.com/paper/2512.11944/) (2025)
- [Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization](https://mlsanity.com/paper/2301.04049/) (2023)
- [Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving](https://mlsanity.com/paper/2306.09055/) (2023)
- [Autonomous vehicle decision and control through reinforcement learning with traffic flow randomization](https://mlsanity.com/paper/2403.02882/) (2024)
- [LA-RL: Language Action-guided Reinforcement Learning with Safety Guarantees for Autonomous Highway Driving](https://mlsanity.com/paper/2512.05686/) (2025)

## Abstract (arXiv)

Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan, overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane change behaviors can be a major cause of traffic flow disruptions and even crashes. While many rule-based methods have been proposed to solve lane change problems for autonomous driving, they tend to exhibit limited performance due to the uncertainty and complexity of the driving environment. Machine learning-based methods offer an alternative approach, as Deep reinforcement learning (DRL) has shown promising success in many application domains including robotic manipulation, navigation, and playing video games. However, applying DRL to autonomous driving still faces many practical challenges in terms of slow learning rates, sample inefficiency, and safety concerns. In this study, we propose an automated lane change strategy using proximal policy optimization-based deep reinforcement learning, which shows great advantages in learning efficiency while still maintaining stable performance. The trained agent is able to learn a smooth, safe, and efficient driving policy to make lane-change decisions (i.e. when and how) in a challenging situation such as dense traffic scenarios. The effectiveness of the proposed policy is validated by using metrics of task success rate and collision rate. The simulation results demonstrate the lane change maneuvers can be efficiently learned and executed in a safe, smooth, and efficient manner.