# 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.

## Key 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.