Automated Automotive Radar Calibration With Intelligent Vehicles

TL;DR

This paper introduces an automated, geo-referenced calibration method for automotive radar sensors using intelligent vehicle location data, enhancing environment perception and enabling cooperative driving.

Contribution

It presents a novel hypothesis filtering scheme for automatic radar calibration that does not require external vehicle modifications and leverages vehicle location data.

Findings

Successfully calibrates infrastructure sensors automatically

Improves environment perception accuracy

Enables cooperative driving scenarios

Abstract

While automotive radar sensors are widely adopted and have been used for automatic cruise control and collision avoidance tasks, their application outside of vehicles is still limited. As they have the ability to resolve multiple targets in 3D space, radars can also be used for improving environment perception. This application, however, requires a precise calibration, which is usually a time-consuming and labor-intensive task. We, therefore, present an approach for automated and geo-referenced extrinsic calibration of automotive radar sensors that is based on a novel hypothesis filtering scheme. Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles. This location data is then combined with filtered sensor data to create calibration hypotheses. Subsequent filtering and optimization recovers the correct calibration. Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner, thus enabling cooperative driving scenarios.