# Intelligent Autonomous Intersection Management

arXiv: https://arxiv.org/abs/2202.04224 · HTML: https://mlsanity.com/paper/2202.04224/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 9 Feb 2022.

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

- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Acme: A Research Framework for Distributed Reinforcement Learning](https://mlsanity.com/paper/2006.00979/) (2020)
- [Hyperbolic Discounting and Learning over Multiple Horizons](https://mlsanity.com/paper/1902.06865/) (2019)
- [Urban Driving with Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1811.08586/) (2018)
- [Polling-systems-based Autonomous Vehicle Coordination in Traffic Intersections with No Traffic Signals](https://mlsanity.com/paper/1607.07896/) (2016)
- [Decentralized Optimal Control for Connected Automated Vehicles at Intersections Including Left and Right Turns](https://mlsanity.com/paper/1703.06956/) (2017)

## Built on by (most cited first, 1 of 1)

- [Adaptive Discounting of Training Time Attacks](https://mlsanity.com/paper/2401.02652/) (2024)

## Abstract (arXiv)

Connected Autonomous Vehicles will make autonomous intersection management a reality replacing traditional traffic signal control. Autonomous intersection management requires time and speed adjustment of vehicles arriving at an intersection for collision-free passing through the intersection. Due to its computational complexity, this problem has been studied only when vehicle arrival times towards the vicinity of the intersection are known beforehand, which limits the applicability of these solutions for real-time deployment. To solve the real-time autonomous traffic intersection management problem, we propose a reinforcement learning (RL) based multiagent architecture and a novel RL algorithm coined multi-discount Q-learning. In multi-discount Q-learning, we introduce a simple yet effective way to solve a Markov Decision Process by preserving both short-term and long-term goals, which is crucial for collision-free speed control. Our empirical results show that our RL-based multiagent solution can achieve near-optimal performance efficiently when minimizing the travel time through an intersection.