# Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization

arXiv: https://arxiv.org/abs/1904.08784 · HTML: https://mlsanity.com/paper/1904.08784/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 18 Apr 2019.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 7 of 7)

- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers](https://mlsanity.com/paper/1804.07871/) (2018)
- [Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search](https://mlsanity.com/paper/1509.06791/) (2015)
- [A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning](https://mlsanity.com/paper/1707.02515/) (2017)
- [Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision](https://mlsanity.com/paper/1808.10393/) (2018)
- [Learning a Lattice Planner Control Set for Autonomous Vehicles](https://mlsanity.com/paper/1903.02044/) (2019)
- [A Data Driven Method of Optimizing Feedforward Compensator for Autonomous Vehicle](https://mlsanity.com/paper/1901.11212/) (2019)

## Built on by (most cited first, 2 of 2)

- [Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic](https://mlsanity.com/paper/2111.06318/) (2021)
- [SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees](https://mlsanity.com/paper/2407.16857/) (2024)

## Abstract (arXiv)

Intelligent motion planning is one of the core components in automated vehicles, which has received extensive interests. Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability. Sampling based methods cannot guarantee the optimality of the generated trajectories. Whereas the optimization-based methods are not able to perform motion planning in real-time, and limited by the simplified formalization. In this work, we propose a learning-based approach to handle those shortcomings. Mixed Integer Quadratic Problem based optimization (MIQP) is used to generate the optimal lane-change trajectories which served as the training dataset for learning-based action generation algorithms. A hierarchical supervised learning model is devised to make the fast lane-change decision. Numerous experiments have been conducted to evaluate the optimality, efficiency, and generalization capability of the proposed approach. The experimental results indicate that the proposed model outperforms several commonly used motion planning baselines.