Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning
📰 ArXiv cs.AI
Learn how to model pedestrian crash avoidance behavior using Smooth-Mamba Deep Reinforcement Learning for safer autonomous vehicle interactions
Action Steps
- Extract safety-critical pedestrian-vehicle interactions from datasets like Argoverse 2
- Apply Smooth-Mamba Deep Reinforcement Learning to model crash avoidance behaviors
- Train models on real-world data to capture vehicle-type-specific pedestrian responses
- Evaluate model performance on safety-critical scenarios
- Integrate models into autonomous vehicle systems for improved safety
Who Needs to Know This
Researchers and engineers working on autonomous vehicle safety can benefit from this study to improve pedestrian-vehicle interaction models
Key Insight
💡 Smooth-Mamba Deep Reinforcement Learning can effectively model vehicle-type-specific pedestrian crash avoidance behavior in safety-critical interactions
Share This
🚗💻 Model pedestrian crash avoidance behavior using Smooth-Mamba Deep Reinforcement Learning for safer #autonomousvehicles #AVsafety
Key Takeaways
Learn how to model pedestrian crash avoidance behavior using Smooth-Mamba Deep Reinforcement Learning for safer autonomous vehicle interactions
Full Article
Title: Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning
Abstract:
arXiv:2605.28552v1 Announce Type: new Abstract: 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
Abstract:
arXiv:2605.28552v1 Announce Type: new Abstract: 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
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