# Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks

arXiv: https://arxiv.org/abs/2403.19633 · HTML: https://mlsanity.com/paper/2403.19633/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2024.

## Built on by (most cited first, 5 of 5)

- [Predictive Planner for Autonomous Driving with Consistency Models](https://mlsanity.com/paper/2502.08033/) (2025)
- [Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation](https://mlsanity.com/paper/2404.01746/) (2024)
- [Bench4Merge: A Comprehensive Benchmark for Merging in Realistic Dense Traffic with Micro-Interactive Vehicles](https://mlsanity.com/paper/2410.15912/) (2024)
- [Frenet Corridor Planner: An Optimal Local Path Planning Framework for Autonomous Driving](https://mlsanity.com/paper/2505.03695/) (2025)
- [Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach](https://mlsanity.com/paper/2509.25751/) (2025)

## Abstract (arXiv)

This paper presents an online smooth-path lane-change control framework. We focus on dense traffic where inter-vehicle space gaps are narrow, and cooperation with surrounding drivers is essential to achieve the lane-change maneuver. We propose a two-stage control framework that harmonizes Model Predictive Control (MPC) with Generative Adversarial Networks (GAN) by utilizing driving intentions to generate smooth lane-change maneuvers. To improve performance in practice, the system is augmented with an adaptive safety boundary and a Kalman Filter to mitigate sensor noise. Simulation studies are investigated in different levels of traffic density and cooperativeness of other drivers. The simulation results support the effectiveness, driving comfort, and safety of the proposed method.