Accelerated Labeling of Discrete Abstractions for Autonomous Driving Subject to LTL Specifications

TL;DR

This paper introduces a GPU-accelerated method for rapidly labeling high-resolution discrete abstractions in autonomous driving, significantly improving real-time planning under LTL specifications.

Contribution

It presents a novel parallel labeling approach optimized for GPU implementation, reducing computational bottlenecks in automaton-based planning for autonomous systems.

Findings

Achieves real-time labeling performance on commodity GPUs

Demonstrates improved planning efficiency in autonomous driving scenarios

Validates scalability of the approach with high-resolution abstractions

Abstract

Linear temporal logic and automaton-based run-time verification provide a powerful framework for designing task and motion planning algorithms for autonomous agents. The drawback to this approach is the computational cost of operating on high resolution discrete abstractions of continuous dynamical systems. In particular, the computational bottleneck that arises is converting perceived environment variables into a labeling function on the states of a Kripke structure or analogously the transitions of a labeled transition system. This paper presents the design and empirical evaluation of an approach to constructing the labeling function that exposes a large degree of parallelism in the operation as well as efficient memory access patterns. The approach is implemented on a commodity GPU and empirical results demonstrate the efficacy of the labeling technique for real-time planning and decision-making.