# Cooperative Lane Changing via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1906.08662 · HTML: https://mlsanity.com/paper/1906.08662/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

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

- [V2V Spatiotemporal Interactive Pattern Recognition and Risk Analysis in Lane Changes](https://mlsanity.com/paper/2105.10688/) (2021)
- [CauTraj: A Causal-Knowledge-Guided Framework for Lane-Changing Trajectory Planning of Autonomous Vehicles](https://mlsanity.com/paper/2512.18703/) (2025)

## Abstract (arXiv)

In this paper, we study how to learn an appropriate lane changing strategy for autonomous vehicles by using deep reinforcement learning. We show that the reward of the system should consider the overall traffic efficiency instead of the travel efficiency of an individual vehicle. In summary, cooperation leads to a more harmonic and efficient traffic system rather than competition