Perception Through 2D-MIMO FMCW Automotive Radar Under Adverse Weather

TL;DR

This paper develops and tests 2D-MIMO FMCW automotive radar techniques for accurate object detection and velocity estimation in adverse weather conditions like snow and fog, where cameras fail.

Contribution

It introduces a new radar test-bed with a 2D MIMO array, develops signal models, and validates DoA estimation algorithms under challenging weather scenarios.

Findings

Radar imaging algorithms successfully detect objects in snow and fog.

The 2D-MIMO FMCW radar provides robust object localization in adverse weather.

Experimental validation shows potential for radar-based ADAS in poor visibility conditions.

Abstract

Millimeter-wave (mmWave) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) features that require accurate location and Doppler velocity estimates of objects, independent of environmental conditions. To explore radar-based ADAS applications, we have updated our test-bed with Texas Instrument's 4-chip cascaded FMCW radar (TIDEP-01012) that forms a non-uniform 2D MIMO virtual array. In this paper, we develop the necessary received signal models for applying different direction of arrival (DoA) estimation algorithms and experimentally validating their performance on formed virtual array under controlled scenarios. To test the robustness of mmWave radars under adverse weather conditions, we collected raw radar dataset (I-Q samples post demodulated) for various objects by a driven vehicle-mounted platform, specifically for snowy and foggy situations where cameras are largely ineffective. Initial results from radar imaging algorithms to this dataset are presented.