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

Traffic Law Compliance Monitoring for Autonomous Vehicles: An Online Approach

Introduction to the Study

In the transition towards fully autonomous driving, ensuring that self-driving vehicles adhere to traffic laws is paramount for safety and legal compliance. This academic paper addresses the challenges of digitizing traffic laws tailored to human drivers and proposes an online system for monitoring autonomous vehicles' compliance with traffic laws in real-time. Drawing from datasets in Chinese highway and intersection contexts, the study devises a comprehensive framework for the digital representation of traffic laws and their enforcement on autonomous vehicles.

Methodology Overview

The paper introduces a novel trigger domain-based layered architecture for the digitization of traffic laws, aiming to represent these laws in a form understandable to autonomous vehicles. The methodology encompasses decomposing traffic laws into atomic propositions and using Metric Temporal Logic (MTL) to structure these propositions into enforceable digital codes. The system is designed to monitor compliance by analyzing the vehicle's perceived environment and decision-making outputs, thus ensuring real-time law adherence.

Key contributions include:

• A systematic approach to digitizing and classifying traffic law constraints into machine-interpretable formats.
• Development of an online monitoring system that leverages vehicle perception and decision-making data to evaluate compliance with digitized laws.
• Verification of the digitized laws and monitoring mechanisms using Chinese highway and intersection datasets, which elucidates the system's applicability and efficiency.

The methodology underscores the complexity of translating fuzzy, human-oriented traffic laws into precise, executable logics for machines, breaking new ground in the field of autonomous vehicle regulation.

Digitization of Traffic Laws

The process outlined for traffic law digitization caters to different scenarios encountered on highways and at intersections, recognizing various behaviors such as speed limits, safe following distances, and right-of-way rules. By categorizing laws based on the context—vehicle speed, distance from other vehicles or static objects, and vehicular actions—a trigger domain-based layered architecture is employed. This architecture aids in systematically activating relevant law compliance checks based on the detected scenario.

The study's granularity extends to defining atomic propositions for MTL expressions, enabling a nuanced representation of traffic laws that cater to continuous and discrete state judgments of vehicle behavior in relation to the law.

Key Results and Analysis

The application of the proposed law violation monitoring system on the Chinese highway and intersection datasets highlights several outcomes:

• The system effectively identifies instances of law violations in real-time, offering a robust mechanism for enforcing law adherence in autonomous vehicles.
• The methodology for setting compliance thresholds, based on statistical analysis of human driving behavior in the dataset, extends the precision of the monitoring system.
• Statistical results pinpoint the commonality of certain violations, such as speed limit breaches and incorrect following distances on highways, and red light violations at intersections. These insights can inform targeted improvements in autonomous driving algorithms for enhanced law compliance.

Implications and Future Directions

The research contributes significantly to the theoretical understanding and practical implementation of traffic law compliance monitoring for autonomous vehicles. The proposed system not only aids manufacturers and regulatory bodies in establishing compliance benchmarks but also promises to enhance the decision-making algorithms of autonomous driving systems for better legal adherence.

Future developments could focus on integrating a state cancel module to refine the system's flexibility and developing ontology mapping for dynamic law compliance. Moreover, evolving the system to automatically adapt to temporary traffic law adjustments and diversify its applicability across geographical regions remains an essential advancement trajectory.

Conclusion

This study marks a significant step towards resolving the complexities of monitoring and ensuring autonomous vehicles' compliance with traffic laws. By architecting a digitized framework that translates real-world legal constraints into machine-readable logics, the paper lays the groundwork for safer and legally compliant autonomous driving, paving the way for future innovations in the field.