# Autonomous and Semi-Autonomous Intersection Management: A Survey

arXiv: https://arxiv.org/abs/2006.13133 · HTML: https://mlsanity.com/paper/2006.13133/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Cooperative Behavior Planning for Automated Driving using Graph Neural Networks](https://mlsanity.com/paper/2202.11376/) (2022)
- [SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigation](https://mlsanity.com/paper/2210.08390/) (2022)
- [An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management](https://mlsanity.com/paper/2207.08655/) (2022)
- [Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2301.12717/) (2023)
- [Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving](https://mlsanity.com/paper/2304.08280/) (2023)
- [Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization](https://mlsanity.com/paper/2306.08815/) (2023)

## Abstract (arXiv)

Intersection is a major source of traffic delays and accidents within modern transportation systems. Compared to signalized intersection management, autonomous intersection management (AIM) coordinates the intersection crossing at an individual vehicle level with additional flexibility. AIM can potentially eliminate stopping in intersection crossing due to traffic lights while maintaining a safe separation among conflicting movements. In this paper, the state-of-the-art AIM research among various disciplines (e.g., traffic engineering, control engineering) is surveyed from the perspective of three hierarchical layers: corridor coordination layer, intersection management layer, and vehicle control layer. The key aspects of AIM designs are discussed in details, including conflict detection schemes, priority rules, control centralization, computation complexity, etc. The potential improvements for AIM evaluation with the emphasis of realistic scenarios are provided. This survey serves as a comprehensive review of AIM design and provides promising directions for future research.