When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems
arXiv:2606.29115v1 Announce Type: cross Abstract: Autonomous vehicles (AVs) are increasingly deployed in urban environments, yet their safety frameworks remain primarily designed around collision avoidance and minimal risk condition (MRC) behaviors such as slowing or stopping when uncertainty arises. Although effective in reducing immediate crash risk, real-world deployments indicate that stopping alone does not guarantee safe integration into human-governed roadway systems. Incidents reported by municipalities and public records show that AV fallback behaviors can obstruct traffic, interfere
The increasing deployment of autonomous vehicles in urban environments is revealing practical limitations of current safety frameworks, necessitating a re-evaluation of established 'minimal risk condition' protocols.
This highlights a critical bottleneck in the safe and scalable integration of AI-driven autonomous systems into complex, human-centric infrastructure, impacting urban planning, regulatory frameworks, and public acceptance.
The understanding of AV safety is shifting from purely collision avoidance to a more holistic view that includes seamless integration with human traffic flow, requiring more sophisticated AI decision-making.
- · AI software developers specializing in human-interactive autonomy
- · Urban planners adopting integrated traffic management solutions
- · Companies offering advanced sensor and contextual awareness systems for AVs
- · AV manufacturers relying solely on 'stop-or-slow' safety protocols
- · Early AV deployment models that did not account for human interaction complexiti
- · Cities with rigid traffic management systems
Refined safety regulations for autonomous vehicles will emerge, emphasizing human-AV interaction and traffic flow integration.
AV development will prioritize AI systems capable of predictive human behavior analysis and adaptive driving strategies in mixed traffic.
Public perception of AVs will hinge on their ability to navigate complex urban environments smoothly, rather than just safely avoiding collisions.
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Read at arXiv cs.AI