Robust Radar Mounting Angle Estimation in Operational Driving Conditions

TL;DR

This paper introduces a neural network-based method combining radar and IMU data for accurate, real-time estimation of automotive radar mounting angles in complex driving environments, outperforming previous techniques.

Contribution

It presents a novel signal processing pipeline utilizing neural networks and vehicle kinematics for robust radar mounting angle estimation in real-world conditions.

Findings

Achieves state-of-the-art accuracy and robustness in radar mounting angle estimation.

Reliable estimates obtained within approximately 25 seconds of driving.

First to demonstrate accurate estimation in complex traffic without controlled setups.

Abstract

The robust estimation of the mounting angle for millimeter-wave automotive radars installed on moving vehicles is investigated. We propose a novel signal processing pipeline that combines radar and inertial measurement unit (IMU) data to achieve accurate and reliable performance in realistic driving scenarios. Unlike previous studies, the method employs neural networks to process sparse and noisy radar measurements, reject detections from moving objects, and estimate radar motion. In addition, a measurement model is introduced to correct IMU bias and scale factor errors. Using vehicle kinematics, the radar mounting angle is then computed from the estimated radar motion and the vehicle's yaw rate. To benchmark performance, the proposed approach is comprehensively compared with two problem formulations and four estimation techniques reported in the literature. Validation is carried out on the challenging RadarScenes dataset, covering over 79 km of real-world driving. Results show that the proposed method achieves state-of-the-art accuracy and robustness, with reliable estimates obtained within approximately 25 seconds of driving. To the best of our knowledge, this is the first study to demonstrate that automotive radar mounting angles can be accurately estimated in complex, real traffic conditions, without requiring controlled environments, dedicated targets, or specially designed driving routes.