Traffic Scenario Logic: A Spatial-Temporal Logic for Modeling and Reasoning of Urban Traffic Scenarios

2024-05-22

arXiv.org

AI Summary

Existing approaches struggle to model and reason about complex urban traffic scenarios, particularly due to restrictive assumptions—such as highway-only or simplified intersection topologies—and widespread reliance on discrete-time/space approximations that compromise spatiotemporal fidelity. To address this, we propose Traffic Scenario Logic (TSL), the first continuous spatiotemporal formal language tailored for pedestrian-free urban road networks. TSL enables automatic, non-discretized, topology-agnostic scenario modeling directly from high-definition OpenDRIVE maps. Its formal semantics are integrated with the Answer Set Programming-based temporal solver Telingo, supporting precise specification and automated reasoning. We empirically validate TSL’s effectiveness in generating diverse, realistic test scenarios across multiple real-world road network configurations. Furthermore, we release an open-source reasoning toolkit. This work establishes a novel paradigm for formal safety verification of autonomous driving decision-making and control modules.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Formal representations of traffic scenarios can be used to generate test cases for the safety verification of autonomous driving. However, most existing methods are limited to highway or highly simplified intersection scenarios due to the intricacy and diversity of traffic scenarios. In response, we propose Traffic Scenario Logic (TSL), which is a spatial-temporal logic designed for modeling and reasoning of urban pedestrian-free traffic scenarios. TSL provides a formal representation of the urban road network that can be derived from OpenDRIVE, i.e., the de facto industry standard of high-definition maps for autonomous driving, enabling the representation of a broad range of traffic scenarios without discretization approximations. We implemented the reasoning of TSL using Telingo, i.e., a solver for temporal programs based on Answer Set Programming, and tested it on different urban road layouts. Demonstrations show the effectiveness of TSL in test scenario generation and its potential value in areas like decision-making and control verification of autonomous driving. The code for TSL reasoning has been open-sourced.

Problem

• Modeling urban traffic scenarios formally
• Generating test cases for autonomous driving
• Reasoning without discretization approximations

Innovation

• Spatial-temporal logic modeling
• OpenDRIVE map integration
• Telingo solver implementation