Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data

TL;DR

This paper demonstrates that pedestrian body height and motion type can be estimated and classified using sparse 2D radar target data, enhancing vehicle environment perception for autonomous driving.

Contribution

It introduces a novel method to estimate pedestrian height and classify motion types solely from sparse radar target data, without needing radar spectrograms.

Findings

Pedestrian height can be accurately estimated from sparse radar targets.

Different pedestrian motion types can be reliably classified using radar data.

Radar-based classification improves robustness of environment perception.

Abstract

A complete overview of the surrounding vehicle environment is important for driver assistance systems and highly autonomous driving. Fusing results of multiple sensor types like camera, radar and lidar is crucial for increasing the robustness. The detection and classification of objects like cars, bicycles or pedestrians has been analyzed in the past for many sensor types. Beyond that, it is also helpful to refine these classes and distinguish for example between different pedestrian types or activities. This task is usually performed on camera data, though recent developments are based on radar spectrograms. However, for most automotive radar systems, it is only possible to obtain radar targets instead of the original spectrograms. This work demonstrates that it is possible to estimate the body height of walking pedestrians using 2D radar targets. Furthermore, different pedestrian motion types are classified.