# When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems

arXiv: https://arxiv.org/abs/2606.29115 · HTML: https://mlsanity.com/paper/2606.29115/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 60 references that are in the map.
- First posted on arXiv 27 Jun 2026.

## Builds on (most cited first, 16 of 16)

- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Talk2Car: Taking Control of Your Self-Driving Car](https://mlsanity.com/paper/1909.10838/) (2019)
- [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018)
- [Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss](https://mlsanity.com/paper/2011.06679/) (2020)
- [A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications](https://mlsanity.com/paper/2006.15172/) (2020)
- [Language-Driven Active Learning for Diverse Open-Set 3D Object Detection](https://mlsanity.com/paper/2404.12856/) (2024)
- [doScenes: An Autonomous Driving Dataset with Natural Language Instruction for Human Interaction and Vision-Language Navigation](https://mlsanity.com/paper/2412.05893/) (2024)
- [Can Vision-Language Models Understand and Interpret Dynamic Gestures from Pedestrians? Pilot Datasets and Exploration Towards Instructive Nonverbal Commands for Cooperative Autonomous Vehicles](https://mlsanity.com/paper/2504.10873/) (2025)
- [Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving Safety](https://mlsanity.com/paper/2504.13399/) (2025)
- [Giving Commands to a Self-Driving Car: How to Deal with Uncertain Situations?](https://mlsanity.com/paper/2106.04232/) (2021)
- [Natural Language Instructions for Scene-Responsive Human-in-the-Loop Motion Planning in Autonomous Driving using Vision-Language-Action Models](https://mlsanity.com/paper/2602.04184/) (2026)
- [Taxonomy and Survey on Remote Human Input Systems for Driving Automation Systems](https://mlsanity.com/paper/2109.08599/) (2021)
- [RampNet: A Two-Stage Pipeline for Bootstrapping Curb Ramp Detection in Streetscape Images from Open Government Metadata](https://mlsanity.com/paper/2508.09415/) (2025)
- [The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models](https://mlsanity.com/paper/2603.16050/) (2026)
- [Driving Towards Inclusion: A Systematic Review of AI-powered Accessibility Enhancements for People with Disability in Autonomous Vehicles](https://mlsanity.com/paper/2401.14571/) (2024)
- ["It Must Be Gesturing Towards Me": Gesture-Based Interaction between Autonomous Vehicles and Pedestrians](https://mlsanity.com/paper/2402.14455/) (2024)

## Built on by (most cited first, 1 of 1)

- [doPlan: A Variable-Horizon Dataset for Multi-Stage Language-Conditioned Planning in Autonomous Driving](https://mlsanity.com/paper/2609.38028/) (2026)

## Abstract (arXiv)

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.