High-Precision Digital Traffic Recording with Multi-LiDAR Infrastructure Sensor Setups

TL;DR

This paper investigates the use of multiple infrastructure-mounted LiDAR sensors to record traffic data, demonstrating that data fusion from several sensors significantly improves multi-object detection and tracking accuracy in urban scenarios.

Contribution

It provides the first comprehensive analysis of fused multi-LiDAR infrastructure sensor setups for high-precision traffic recording, both in simulation and real-world environments.

Findings

Fused LiDAR data improves object detection accuracy.

Tracking precision reaches within a few centimeters.

Multi-sensor setups outperform single LiDAR systems.

Abstract

Large driving datasets are a key component in the current development and safeguarding of automated driving functions. Various methods can be used to collect such driving data records. In addition to the use of sensor equipped research vehicles or unmanned aerial vehicles (UAVs), the use of infrastructure sensor technology offers another alternative. To minimize object occlusion during data collection, it is crucial to record the traffic situation from several perspectives in parallel. A fusion of all raw sensor data might create better conditions for multi-object detection and tracking (MODT) compared to the use of individual raw sensor data. So far, no sufficient studies have been conducted to sufficiently confirm this approach. In our work we investigate the impact of fused LiDAR point clouds compared to single LiDAR point clouds. We model different urban traffic scenarios with up to eight 64-layer LiDARs in simulation and in reality. We then analyze the properties of the resulting point clouds and perform MODT for all emerging traffic participants. The evaluation of the extracted trajectories shows that a fused infrastructure approach significantly increases the tracking results and reaches accuracies within a few centimeters.