Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning
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 vehicle-type-specific pedestrian crash avoidance behavior, we develop a Smooth-Mamba Deep Determinis
The increasing deployment of autonomous vehicles (AVs) on public roads necessitates deeper understanding of human-AV interactions to ensure safety, making this research timely.
This study is critical for the safe and ethical integration of AVs into existing transportation systems, directly impacting public perception and regulatory frameworks.
Our understanding of pedestrian behavior in safety-critical interactions with different vehicle types is enhanced, allowing for more robust AV development and safety protocols.
- · Autonomous Vehicle Developers
- · Smart City Planners
- · Pedestrian Safety Advocates
- · Companies with lagging AV safety research
- · Transportation systems with high pedestrian accident rates
Improved prediction and avoidance systems for AVs in complex urban environments.
Faster regulatory approval and broader public acceptance of autonomous driving technologies.
The development of new urban planning strategies that optimize for mixed human-AV traffic flows.
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Read at arXiv cs.AI