When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems
Yash Tandon, Giovanni Tapia Lopez, Marcus Blennemann, Mohan Trivedi, Ross Greer
Why It Matters
What makes this one worth your time
Understanding and improving AV interaction with human agents and infrastructure is crucial for the safe and effective deployment of autonomous vehicles in urban settings.
The paper calls for a shift from passive to human-interactive autonomy in AVs for safer urban deployment.
Summary
The paper analyzes incidents involving autonomous vehicle (AV) stopping behaviors and human-AV interaction failures, categorizing them by limitations in perception, planning, and control. It identifies gaps in current safety paradigms and suggests augmenting AV frameworks with human-interactive capabilities for better integration into urban environments.
Key contributions
- Analysis of publicly documented AV incidents involving stopping behavior.
- Taxonomy of limitations in perception, planning, and control in AV architectures.
- Review of emerging research directions for human-interactive AV capabilities.
Notable insights
- Identifying the lack of mechanisms for interpreting human authority and responding to multimodal instructions in current AV systems.
- Highlighting the need for AVs to adapt to dynamic, socially regulated traffic conditions.
Possible limitations
- Not stated in the abstract
Abstract
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 with emergency response operations, and create accessibility challenges for passengers and pedestrians. This paper presents an analysis of publicly documented incidents involving AV stopping behavior and human-AV interaction failures. We categorize these incidents according to limitations in perception, planning, and control within current AV architectures. Using this taxonomy, we identify key gaps in existing safety paradigms, particularly the lack of mechanisms for interpreting human authority, responding to multimodal instructions, and adapting to dynamic, socially regulated traffic conditions. We then review emerging research directions that support human-interactive perception, language-grounded and accessibility-aware planning, and assisted control through remote guidance and teleoperation. The analysis highlights the need to augment current AV safety frameworks with capabilities that enable cooperative interaction with human agents and infrastructure. These findings suggest that reliable urban deployment of AVs requires moving beyond passive fallback strategies toward human-interactive autonomy.