Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Maps

TL;DR

This paper presents a comprehensive environment perception framework for autonomous vehicles that fuses multi-object tracking, dynamic occupancy grid maps, and digital maps to improve robustness and accuracy in diverse scenarios.

Contribution

It introduces a novel fusion approach combining object tracks and occupancy grids with a confidence measure, enhancing environment perception for autonomous driving.

Findings

Robust perception in rural and urban scenarios demonstrated.

Fusion approach reduces false positives and improves detection accuracy.

Confidence validation enhances reliability of object estimates.

Abstract

Autonomously driving vehicles require a complete and robust perception of the local environment. A main challenge is to perceive any other road users, where multi-object tracking or occupancy grid maps are commonly used. The presented approach combines both methods to compensate false positives and receive a complementary environment perception. Therefore, an environment perception framework is introduced that defines a common representation, extracts objects from a dynamic occupancy grid map and fuses them with tracks of a Labeled Multi-Bernoulli filter. Finally, a confidence value is developed, that validates object estimates using different constraints regarding physical possibilities, method specific characteristics and contextual information from a digital map. Experimental results with real world data highlight the robustness and significance of the presented fusing approach, utilizing the confidence value in rural and urban scenarios.