# A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers

arXiv: https://arxiv.org/abs/1804.07871 · HTML: https://mlsanity.com/paper/1804.07871/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2018.

## Built on by (most cited first, 30 of 46)

- [Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research](https://mlsanity.com/paper/2310.08710/) (2023)
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- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence](https://mlsanity.com/paper/2103.10290/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors](https://mlsanity.com/paper/2305.11239/) (2023)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Driving](https://mlsanity.com/paper/2104.07246/) (2021)
- [DistillDrive: End-to-End Multi-Mode Autonomous Driving Distillation by Isomorphic Hetero-Source Planning Model](https://mlsanity.com/paper/2508.05402/) (2025)
- [Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation](https://mlsanity.com/paper/2112.09012/) (2021)
- [Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation](https://mlsanity.com/paper/2210.03209/) (2022)
- [Runtime Safety Assurance for Learning-enabled Control of Autonomous Driving Vehicles](https://mlsanity.com/paper/2109.13446/) (2021)
- [Modified DDPG car-following model with a real-world human driving experience with CARLA simulator](https://mlsanity.com/paper/2112.14602/) (2021)
- [Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios](https://mlsanity.com/paper/2107.04538/) (2021)
- [A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving](https://mlsanity.com/paper/2304.08235/) (2023)
- [Discretionary Lane-Change Decision and Control via Parameterized Soft Actor-Critic for Hybrid Action Space](https://mlsanity.com/paper/2402.15790/) (2024)
- [Encoding Distributional Soft Actor-Critic for Autonomous Driving in Multi-lane Scenarios](https://mlsanity.com/paper/2109.05540/) (2021)
- [Tackling Real-World Autonomous Driving using Deep Reinforcement Learning](https://mlsanity.com/paper/2207.02162/) (2022)
- [Formulation and validation of a car-following model based on deep reinforcement learning](https://mlsanity.com/paper/2109.14268/) (2021)
- [Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model](https://mlsanity.com/paper/2203.04749/) (2022)
- [Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning](https://mlsanity.com/paper/2203.10949/) (2022)
- [Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning](https://mlsanity.com/paper/2401.08661/) (2023)
- [An Intelligent Self-driving Truck System For Highway Transportation](https://mlsanity.com/paper/2112.15304/) (2021)
- [Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning](https://mlsanity.com/paper/2212.07594/) (2022)
- [Residual Policy Learning for Powertrain Control](https://mlsanity.com/paper/2212.07611/) (2022)
- [Developing Driving Strategies Efficiently: A Skill-Based Hierarchical Reinforcement Learning Approach](https://mlsanity.com/paper/2302.02179/) (2023)
- [Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2306.11465/) (2023)
- [Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2501.14992/) (2025)
- [Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization](https://mlsanity.com/paper/1904.08784/) (2019)

## Abstract (arXiv)

Lane change is a crucial vehicle maneuver which needs coordination with surrounding vehicles. Automated lane changing functions built on rule-based models may perform well under pre-defined operating conditions, but they may be prone to failure when unexpected situations are encountered. In our study, we proposed a Reinforcement Learning based approach to train the vehicle agent to learn an automated lane change behavior such that it can intelligently make a lane change under diverse and even unforeseen scenarios. Particularly, we treated both state space and action space as continuous, and designed a Q-function approximator that has a closed- form greedy policy, which contributes to the computation efficiency of our deep Q-learning algorithm. Extensive simulations are conducted for training the algorithm, and the results illustrate that the Reinforcement Learning based vehicle agent is capable of learning a smooth and efficient driving policy for lane change maneuvers.