Safe and Personalizable Logical Guidance for Trajectory Planning of Autonomous Driving

Autonomous vehicles necessitate a delicate balance between safety, efficiency, and user preferences in trajectory planning. Existing traditional or learning-based methods face challenges in adequately addressing all these aspects. In response, this paper proposes a novel component termed the Logical Guidance Layer (LGL), designed for seamless integration into autonomous driving trajectory planning frameworks, specifically tailored for highway scenarios. The LGL guides the trajectory planning with a local target area determined through scenario reasoning, scenario evaluation, and guidance area calculation. Integrating the Responsibility-Sensitive Safety (RSS) model, the LGL ensures formal safety guarantees while accommodating various user preferences defined by logical formulae. Experimental validation demonstrates the effectiveness of the LGL in achieving a balance between safety and efficiency, and meeting user preferences in autonomous highway driving scenarios.

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

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 in 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. We implemented the reasoning of TSL using Telingo, i.e., a solver for temporal programs based on the 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.