# Intent-Aware Autonomous Driving: A Case Study on Highway Merging Scenarios

arXiv: https://arxiv.org/abs/2309.13206 · HTML: https://mlsanity.com/paper/2309.13206/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Social Coordination and Altruism in Autonomous Driving](https://mlsanity.com/paper/2107.00200/) (2021)
- [Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic](https://mlsanity.com/paper/1906.11021/) (2019)
- [Towards Learning Multi-agent Negotiations via Self-Play](https://mlsanity.com/paper/2001.10208/) (2020)
- [Intent-aware Multi-agent Reinforcement Learning](https://mlsanity.com/paper/1803.02018/) (2018)
- [Spatial Intention Maps for Multi-Agent Mobile Manipulation](https://mlsanity.com/paper/2103.12710/) (2021)
- [Autonomous Highway Merging in Mixed Traffic Using Reinforcement Learning and Motion Predictive Safety Controller](https://mlsanity.com/paper/2204.01024/) (2022)
- [Intent Communication between Autonomous Vehicles and Pedestrians](https://mlsanity.com/paper/1708.07123/) (2017)

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

- [A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions](https://mlsanity.com/paper/2402.01968/) (2024)
- [Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions](https://mlsanity.com/paper/2504.20004/) (2025)

## Abstract (arXiv)

In this work, we use the communication of intent as a means to facilitate cooperation between autonomous vehicle agents. Generally speaking, intents can be any reliable information about its future behavior that a vehicle communicates with another vehicle. We implement this as an intent-sharing task atop the merging environment in the simulator of highway-env, which provides a collection of environments for learning decision-making strategies for autonomous vehicles. Under a simple setting between two agents, we carefully investigate how intent-sharing can aid the receiving vehicle in adjusting its behavior in highway merging scenarios.