Traffic Scenario Logic: A Spatial-Temporal Logic for Modeling and Reasoning of Urban Traffic Scenarios
This work addresses the problem of safety verification for autonomous driving systems by providing a formal method to model intricate urban traffic scenarios, though it is incremental as it builds on existing logic and solver techniques.
The authors tackled the challenge of formally representing complex urban traffic scenarios for autonomous driving safety verification by proposing Traffic Scenario Logic (TSL), a spatial-temporal logic that models scenarios without discretization approximations and was tested on various urban road layouts, showing effectiveness in test scenario generation.
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.