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From ambiguity to precision: Digitized traffic rules for autonomous driving.

Ruolin Shi1, Xuesong Wang1, Meixin Zhu2

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Summary

This study introduces a novel framework to optimize traffic rules for automated vehicles (AVs), significantly improving safety by over 90% in mixed human-AV traffic. The approach enhances AVs

Keywords:
Automated vehiclesRules digitizationTraffic rulesTrajectory dataTrajectory planning

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Area of Science:

  • Intelligent Transportation Systems
  • Robotics and Control Systems
  • Traffic Engineering

Background:

  • Human-driven and automated vehicles (AVs) interact in mixed traffic, necessitating AVs to understand and follow human driving rules.
  • Existing traffic rules often use ambiguous language (e.g., 'not impede'), posing challenges for AV implementation and safety.
  • Accurate inference and compliance with traffic rules are critical for AV safety in complex driving scenarios.

Purpose of the Study:

  • To develop and validate a rule optimization framework for AVs that bridges the gap between ambiguous natural language traffic rules and precise AV implementation.
  • To enhance the safety, operational efficiency, and ride comfort of AVs in mixed traffic environments.
  • To ensure AV behavior aligns with human driving norms and traffic regulations.

Main Methods:

  • A hybrid approach combining knowledge-based reasoning and data-driven optimization using safety-critical event (SCE) data from real-world intersections.
  • Semantic classification of traffic rules, formalization using Metric Temporal Logic (MTL), and parameter supplementation from regulatory documents.
  • Calibration of optimal parameters using a genetic algorithm within simulation environments reconstructed from SCE data.

Main Results:

  • The rule-integrated planner demonstrated a safety performance enhancement of over 90% compared to human drivers, while maintaining operational efficiency and ride comfort.
  • Optimized rules integrated into existing planners on CommonRoad and INTERACTION benchmarks consistently reduced collision risks, showing strong generalizability.
  • Key performance drivers identified as parameters controlling turn ranges, temporal safety thresholds, and longitudinal distances.

Conclusions:

  • The proposed framework offers an interpretable and transferable solution for optimizing traffic rules, enhancing AV safety and alignment with human driving.
  • This research addresses the challenge of implementing ambiguous traffic rules in AVs, paving the way for safer mixed-traffic environments.
  • The findings highlight the importance of parameter calibration for achieving robust and safe AV behavior in diverse driving conditions.