Identification of Threat Regions From a Dynamic Occupancy Grid Map for Situation-Aware Environment Perception

TL;DR

This paper introduces a lightweight, online method for identifying safety-critical regions in the environment of automated vehicles, enhancing safety without relying on prior map knowledge.

Contribution

The novel approach enables real-time identification of threat regions using only online data, improving safety and computational efficiency in automated driving.

Findings

Enables safe operation in critical scenarios

Reduces computational load by focusing on relevant regions

Operates effectively without prior map knowledge

Abstract

The advance towards higher levels of automation within the field of automated driving is accompanied by increasing requirements for the operational safety of vehicles. Induced by the limitation of computational resources, trade-offs between the computational complexity of algorithms and their potential to ensure safe operation of automated vehicles are often encountered. Situation-aware environment perception presents one promising example, where computational resources are distributed to regions within the perception area that are relevant for the task of the automated vehicle. While prior map knowledge is often leveraged to identify relevant regions, in this work, we present a lightweight identification of safety-relevant regions that relies solely on online information. We show that our approach enables safe vehicle operation in critical scenarios, while retaining the benefits of non-uniformly distributed resources within the environment perception.