Formalizing Traffic Rules for Machine Interpretability
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Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 5; 2025: 3; 2026: 2.
12 papers.
- 2024Driving Everywhere with Large Language Model Policy Adaptationcited by 53
- 2022Model Predictive Robustness of Signal Temporal Logic Predicatescited by 3
- 2024Traffic-Rule-Compliant Trajectory Repair via Satisfiability Modulo Theories and Reachability Analysiscited by 3
- 2024Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Seacited by 2
- 2024RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shiftscited by 2
- 2021Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Drivingcited by 1
- 2024Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Frameworkcited by 1
- 2025Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Surveycited by 1
- 2026Towards Neuro-symbolic Causal Rule Synthesis, Verification, and Evaluation Grounded in Legal and Safety Principlescited by 1
- 2025Integrating Legal and Logical Specifications in Perception, Prediction, and Planning for Automated Driving: A Survey of Methodscited by 0
- 2025Context-aware, Ante-hoc Explanations of Driving Behaviourcited by 0
- 2026Structured Four-Stage Legal Translation: From Natural-Language Traffic Rules to PROLOGcited by 0
- 2026Structured Four-Stage Legal Translation: From Natural-Language Traffic Rules to PROLOG17 Sep 2026
- 2026Towards Neuro-symbolic Causal Rule Synthesis, Verification, and Evaluation Grounded in Legal and Safety Principles30 Apr 2026
- 2025Context-aware, Ante-hoc Explanations of Driving Behaviour18 Nov 2025
- 2025Integrating Legal and Logical Specifications in Perception, Prediction, and Planning for Automated Driving: A Survey of Methods29 Oct 2025
- 2025Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey13 Feb 2025
- 2024Traffic-Rule-Compliant Trajectory Repair via Satisfiability Modulo Theories and Reachability Analysis20 Dec 2024
- 2024Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework1 Jul 2024
- 2024RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts18 May 2024
- 2024Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea13 Feb 2024
- 2024Driving Everywhere with Large Language Model Policy Adaptation8 Feb 2024
- 2022Model Predictive Robustness of Signal Temporal Logic Predicates16 Sep 2022
- 2021Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving5 Feb 2021