# Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network

arXiv: https://arxiv.org/abs/2105.00153 · HTML: https://mlsanity.com/paper/2105.00153/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 1 May 2021.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 13 of 13)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Safe Reinforcement Learning on Autonomous Vehicles](https://mlsanity.com/paper/1910.00399/) (2019)
- [Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/1910.11689/) (2019)
- [Deep Reinforcement Learning for General Video Game AI](https://mlsanity.com/paper/1806.02448/) (2018)
- [Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/1705.01196/) (2017)
- [Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments](https://mlsanity.com/paper/1904.11483/) (2019)
- [Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning](https://mlsanity.com/paper/1810.10469/) (2018)
- [Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network](https://mlsanity.com/paper/2010.13407/) (2020)
- [Pedestrian Dominance Modeling for Socially-Aware Robot Navigation](https://mlsanity.com/paper/1810.06613/) (2018)
- [Decision-making at Unsignalized Intersection for Autonomous Vehicles: Left-turn Maneuver with Deep Reinforcement Learning](https://mlsanity.com/paper/2008.06595/) (2020)

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

- [Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/2110.05205/) (2021)
- [Don't Get Yourself into Trouble! Risk-aware Decision-Making for Autonomous Vehicles](https://mlsanity.com/paper/2106.04625/) (2021)

## Abstract (arXiv)

Prior research has extensively explored Autonomous Vehicle (AV) navigation in the presence of other vehicles, however, navigation among pedestrians, who are the most vulnerable element in urban environments, has been less examined. This paper explores AV navigation in crowded, unsignalized intersections. We compare the performance of different deep reinforcement learning methods trained on our reward function and state representation. The performance of these methods and a standard rule-based approach were evaluated in two ways, first at the unsignalized intersection on which the methods were trained, and secondly at an unknown unsignalized intersection with a different topology. For both scenarios, the rule-based method achieves less than 40% collision-free episodes, whereas our methods result in a performance of approximately 100%. Of the three methods used, DDQN/PER outperforms the other two methods while it also shows the smallest average intersection crossing time, the greatest average speed, and the greatest distance from the closest pedestrian.