Formalizing Traffic Rules for Machine Interpretability

Abstract

Autonomous vehicles need to be designed to abide by the same rules that humans follow. This is challenging, because traffic rules are fuzzy and not well defined, making them incomprehensible to machines. Satisfaction cannot be incorporated in a planning component without proper formalization, nor can it be monitored and verified during simulation or testing. However, no research work has provided a consistent set of machine-interpretable traffic rules for a given operational driving domain. In this paper, we propose a methodology for the legal study and formalization of traffic rules in a formal language. We use Linear Temporal Logic as a formal specification language to describe temporal behaviors, capable of capturing a wide range of traffic rules. We contribute a formalized set of traffic rules for dual carriageways and evaluate the effectiveness of our formalized rules on a public dataset.

arXiv commentPublished at IEEE Connected and Automated Vehicles Symposium (CAVS), 2020

References in corpus (4)

• On a Formal Model of Safe and Scalable Self-driving Cars
• Search-based optimal motion planning for automated driving
• BARK: Open Behavior Benchmarking in Multi-Agent Environments
• From Specifications to Behavior: Maneuver Verification in a Semantic State Space

Cited by in corpus (4)

• No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehicles
• Model Predictive Robustness of Signal Temporal Logic Predicates
• Extending Urban Multi-Lane Spatial Logic to Formalise Road Junction Rules
• Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea