mlsanityData to 7 Oct 2026

Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning

arXiv 2605.28552 ↗First posted 27 May 2026cs.AIcited by 0

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Abstract (arXiv)

As automated vehicles (AVs) increasingly share roadways with human-driven vehicles (HDVs), understanding how pedestrians respond to different vehicle types in safety-critical interactions is essential for the safe deployment of automated driving technologies. This study extracts safety-critical pedestrian-vehicle interactions from the Argoverse 2 dataset to capture real-world crash avoidance behaviors in encounters involving AVs and HDVs. To model vehicle-type-specific pedestrian crash avoidance behavior, we develop a Smooth-Mamba Deep Deterministic Policy Gradient framework, termed SMamba-DDPG, which integrates smooth action constraints with efficient temporal representation learning. To quantify pedestrian behavioral differences, the framework trains separate crash avoidance policies for pedestrian interactions with AVs and HDVs. Results show that SMamba-DDPG outperforms baseline reinforcement learning and supervised learning models in reproducing pedestrian crash avoidance behaviors. Reconstructed trajectories demonstrate strong behavioral realism, accurately reproducing crash avoidance kinematics in both AV and HDV scenarios. Reaction time analysis shows that the model captures human-like response delays and reveals that pedestrians respond more quickly to AVs than to HDVs. Counterfactual analysis further indicates that pedestrians adopt lower crossing speeds when interacting with AVs. Large-scale safety analysis of model-generated data revealed that pedestrian-AV interactions consistently yielded lower conflict rates and higher pedestrian yielding rates compared to pedestrian-HDV interactions. The findings highlight the importance of incorporating vehicle-type-specific pedestrian behavioral models for safer automated driving system design and more realistic traffic simulations in mixed-traffic environments.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 61 references that are in the map. First posted on arXiv 27 May 2026.

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13 of 61 references.

References · 13

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 701
  4. 2022Local-Global Context Aware Transformer for Language-Guided Video Segmentationcited by 41
  5. 2019Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challengescited by 36
  6. 2021Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approachescited by 30
  7. 2023Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Reviewcited by 18
  8. 2021Algorithmic Instabilities of Accelerated Gradient Descentcited by 16
  9. 2020Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Carscited by 7
  10. 2024Drone Data Analytics for Measuring Traffic Metrics at Intersections in High-Density Areascited by 3
  11. 2025ME3-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perceptioncited by 1
  12. 2026A Vision-and-Knowledge Enhanced Large Language Model for Generalizable Pedestrian Crossing Behavior Inferencecited by 1
1 more
  1. 2026Generating Realistic Safety-Critical Scenarios for Vehicle-Pedestrian Interactionscited by 1

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  1. 2021Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance
  2. 2026Pedestrian-Aware LLM-Driven Behavioral Planning for Autonomous Vehicles
  3. 2026Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
  4. 2021Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network
  5. 2026Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
  6. 2023Suicidal Pedestrian: Generation of Safety-Critical Scenarios for Autonomous Vehicles
  7. 2025Realistic pedestrian-driver interaction modelling using multi-agent RL with human perceptual-motor constraints
  8. 2024Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space