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

arXiv: https://arxiv.org/abs/1812.08449 · HTML: https://mlsanity.com/paper/1812.08449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [Deep Generic Dynamic Object Detection Based on Dynamic Grid Maps](https://mlsanity.com/paper/2410.14799/) (2024)
- [Near-Shore Mapping for Detection and Tracking of Vessels](https://mlsanity.com/paper/2502.18368/) (2025)

## Abstract (arXiv)

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.