# No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehicles

arXiv: https://arxiv.org/abs/2212.04156 · HTML: https://mlsanity.com/paper/2212.04156/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2022.

## Built on by (most cited first, 10 of 10)

- [Real-time Accident Anticipation for Autonomous Driving Through Monocular Depth-Enhanced 3D Modeling](https://mlsanity.com/paper/2409.01256/) (2024)
- [World Engine: Towards the Era of Post-Training for Autonomous Driving](https://mlsanity.com/paper/2606.19836/) (2026)
- [Argus: Resilience-Oriented Safety Assurance Framework for End-to-End ADSs](https://mlsanity.com/paper/2511.09032/) (2025)
- [An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving](https://mlsanity.com/paper/2409.06607/) (2024)
- [Get Global Guarantees: On the Probabilistic Nature of Perturbation Robustness](https://mlsanity.com/paper/2508.19183/) (2025)
- [Integrating Legal and Logical Specifications in Perception, Prediction, and Planning for Automated Driving: A Survey of Methods](https://mlsanity.com/paper/2510.25386/) (2025)
- [Towards Lawful Autonomous Driving: Deriving Scenario-Aware Driving Requirements from Traffic Laws and Regulations](https://mlsanity.com/paper/2604.24562/) (2026)
- [Learning social norms enhances compatibility in dynamic human-AI coordination](https://mlsanity.com/paper/2607.07021/) (2026)
- [Self-Aware Active Learning Enables Continual Improvement in Autonomous Driving](https://mlsanity.com/paper/2608.29772/) (2026)
- [EEG-Driven Decoding Framework for Passenger Hazard Perception in Highly Automated Vehicles](https://mlsanity.com/paper/2609.07128/) (2026)

## Abstract (arXiv)

Defined traffic laws must be respected by all vehicles. However, it is essential to know which behaviors violate the current laws, especially when a responsibility issue is involved in an accident. This brings challenges of digitizing human-driver-oriented traffic laws and monitoring vehicles' behaviors continuously. To address these challenges, this paper aims to digitize traffic law comprehensively and provide an application for online monitoring of legal driving behavior for autonomous vehicles. This paper introduces a layered trigger domain-based traffic law digitization architecture with digitization-classified discussions and detailed atomic propositions for online monitoring. The principal laws on a highway and at an intersection are taken as examples, and the corresponding logic and atomic propositions are introduced in detail. Finally, the digitized traffic laws are verified on the Chinese highway and intersection datasets, and defined thresholds are further discussed according to the driving behaviors in the considered dataset. This study can help manufacturers and the government in defining specifications and laws and can also be used as a useful reference in traffic laws compliance decision-making. Source code is available on https://github.com/SOTIF-AVLab/DOTL.