# Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1904.10171 · HTML: https://mlsanity.com/paper/1904.10171/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [FASIONAD++ : Integrating High-Level Instruction and Information Bottleneck in FAt-Slow fusION Systems for Enhanced Safety in Autonomous Driving with Adaptive Feedback](https://mlsanity.com/paper/2503.08162/) (2025)
- [Runtime Safety Assurance for Learning-enabled Control of Autonomous Driving Vehicles](https://mlsanity.com/paper/2109.13446/) (2021)
- [HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model](https://mlsanity.com/paper/2405.13547/) (2024)
- [Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning](https://mlsanity.com/paper/2103.14718/) (2021)
- [Improving the generalizability and robustness of large-scale traffic signal control](https://mlsanity.com/paper/2306.01925/) (2023)
- [Prediction Based Decision Making for Autonomous Highway Driving](https://mlsanity.com/paper/2209.02106/) (2022)
- [Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2501.14992/) (2025)
- [Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow](https://mlsanity.com/paper/2506.12600/) (2025)
- [SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees](https://mlsanity.com/paper/2407.16857/) (2024)
- [Performance Comparison of Deep RL Algorithms for Mixed Traffic Cooperative Lane-Changing](https://mlsanity.com/paper/2407.02521/) (2024)
- [STDA: Spatio-Temporal Dual-Encoder Network Incorporating Driver Attention to Predict Driver Behaviors Under Safety-Critical Scenarios](https://mlsanity.com/paper/2408.01774/) (2024)
- [Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving](https://mlsanity.com/paper/2509.04712/) (2025)
- [Multi-agent DRL-based Lane Change Decision Model for Cooperative Platooning in Mixed Traffic](https://mlsanity.com/paper/2601.11809/) (2026)

## Abstract (arXiv)

We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver. Furthermore, we design two similar Deep Q learning frameworks with quadratic approximator for deciding how to select a comfortable gap and just follow the preceding vehicle. Finally, a polynomial lane change trajectory is generated and Pure Pursuit Control is implemented for path tracking. We demonstrate the effectiveness of this framework in simulation, from both the decision-making and control layers. The proposed architecture also has the potential to be extended to other autonomous driving scenarios.