# Environment Modeling Based on Generic Infrastructure Sensor Interfaces Using a Centralized Labeled-Multi-Bernoulli Filter

## TL;DR

This paper introduces a generic sensor interface and a Labeled Multi-Bernoulli filter for environment modeling at urban intersections, improving object detection and tracking despite occlusions and sensor limitations.

## Contribution

It proposes a versatile sensor interface and a novel LMB filter capable of handling sparse measurements and inferring object extents, enhancing infrastructure-based environment modeling.

## Key findings

- Effective in simulation environments
- Successfully demonstrated on real-world infrastructure setup
- Improves object detection under occlusion conditions

## Abstract

Urban intersections put high demands on fully automated vehicles, in particular, if occlusion occurs. In order to resolve such and support vehicles in unclear situations, a popular approach is the utilization of additional information from infrastructure-based sensing systems. However, a widespread use of such systems is circumvented by their complexity and thus, high costs. Within this paper, a generic interface is proposed, which enables a huge variety of sensors to be connected. The sensors are only required to measure very few features of the objects, if multiple distributed sensors with different viewing directions are available. Furthermore, a Labeled Multi-Bernoulli (LMB) filter is presented, which can not only handle such measurements, but also infers missing object information about the objects' extents. The approach is evaluated on simulations and demonstrated on a real-world infrastructure setup.