# Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network

arXiv: https://arxiv.org/abs/1909.05665 · HTML: https://mlsanity.com/paper/1909.05665/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

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

- [Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2109.12764/) (2021)
- [Injecting Knowledge in Data-driven Vehicle Trajectory Predictors](https://mlsanity.com/paper/2103.04854/) (2021)
- [Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework](https://mlsanity.com/paper/2409.12812/) (2024)
- [Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach](https://mlsanity.com/paper/2302.00171/) (2023)
- [Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2201.06539/) (2022)
- [Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios](https://mlsanity.com/paper/2107.04538/) (2021)
- [Predictive Planner for Autonomous Driving with Consistency Models](https://mlsanity.com/paper/2502.08033/) (2025)
- [Interaction-Aware Trajectory Planning for Autonomous Vehicles with Analytic Integration of Neural Networks into Model Predictive Control](https://mlsanity.com/paper/2301.05393/) (2023)
- [Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation](https://mlsanity.com/paper/2404.01746/) (2024)
- [Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms](https://mlsanity.com/paper/2204.10807/) (2022)
- [RCMS: Risk-Aware Crash Mitigation System for Autonomous Vehicles](https://mlsanity.com/paper/2309.12531/) (2023)
- [Trajectory Prediction for Autonomous Driving using Agent-Interaction Graph Embedding](https://mlsanity.com/paper/2410.23298/) (2024)
- [Risk-Aware Lane Selection on Highway with Dynamic Obstacles](https://mlsanity.com/paper/2104.04105/) (2021)
- [V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario](https://mlsanity.com/paper/2410.22987/) (2024)

## Abstract (arXiv)

This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers. This paper focuses on heavy traffic where vehicles cannot change lanes without cooperating with other drivers. In this case, classical robust controls may not apply since there is no safe area to merge to without interacting with the other drivers. That said, modeling complex and interactive human behaviors is highly non-trivial from the perspective of control engineers. We propose a mathematical control framework based on Model Predictive Control (MPC) encompassing a state-of-the-art Recurrent Neural network (RNN) architecture. In particular, RNN predicts interactive motions of other drivers in response to potential actions of the autonomous vehicle, which are then systematically evaluated in safety constraints. We also propose a real-time heuristic algorithm to find locally optimal control inputs. Finally, quantitative and qualitative analysis on simulation studies are presented to illustrate the benefits of the proposed framework.