Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network
On the map: End-to-end autonomous driving → Reinforcement learning for driving
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.
Key facts. Cited by 2 papers in the map (data through 7 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.
Builds on
13 of 27 references.
References · 13
- 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
- 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
- 2016OpenAI Gymcited by 1,684
- 2015Prioritized Experience Replaycited by 1,070
- 2019Safe Reinforcement Learning on Autonomous Vehiclescited by 48
- 2019Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learningcited by 44
- 2018Deep Reinforcement Learning for General Video Game AIcited by 25
- 2017Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learningcited by 21
- 2019Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environmentscited by 18
- 2018Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learningcited by 13
- 2020Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Networkcited by 5
- 2018Pedestrian Dominance Modeling for Socially-Aware Robot Navigationcited by 4
Built on by
Citing papers per year: 2021: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2024Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions
- 2023Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation
- 2022State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections
- 2021Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance
- 2026Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning
- 2023Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLA
- 2024Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space
- 2023Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning