Safe and Personalizable Logical Guidance for Trajectory Planning of Autonomous Driving

TL;DR

This paper introduces the Logical Guidance Layer (LGL), a novel component for autonomous vehicle trajectory planning that ensures safety, efficiency, and personalization by integrating formal safety guarantees with user preferences in highway scenarios.

Contribution

The paper presents the LGL, a new framework that seamlessly integrates safety guarantees with user preferences in trajectory planning for autonomous driving.

Findings

LGL effectively balances safety and efficiency in highway scenarios.

LGL accommodates diverse user preferences through logical formulae.

Experimental results validate the formal safety guarantees of LGL.

Abstract

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.