Model-Based Knowledge-Driven Machine Learning for Automotive Radar Imaging and Perception

Abstract

Electronic Thesis or DissertationThis thesis enhances automotive radar object detection by integrating deep learningnetworks with radar signal processing expertise. Automotive radar sensors are essential inadvanced driver assistance systems and autonomous vehicles due to their low cost,robustness, and effective operation in all weather conditions. Cameras and LiDAR systems,while offering advanced environmental perception, suffer performance degradation inadverse weather and poor visibility and often have higher costs. Millimeter-waveautomotive radars, operating between 76–81 GHz with bandwidths up to 4 GHz, providehigh range resolution and strong penetration capabilities through fog, rain, snow, smoke,and dust. Despite these advantages, radar’s potential for object detection and classificationremains underutilized due to limitations in angular resolution, reliance on sparse pointclouds in commercial systems, and the scarcity of publicly available high-resolutionautomotive radar datasets.To address these challenges, this thesis focuses on three key enhancements. First, wepropose novel deep learning frameworks for Direction of Arrival (DOA) estimation, aimedat improving angular resolution and object localization accuracy while simultaneouslyreducing system complexity. Second, by integrating deep learning into the radar signalprocessing pipeline, we enhance feature extraction from raw radar data. This integrationnot only improves radar image quality but also increases the reliability of subsequentobject detection and classification tasks. Third, we develop the BAMA Radar Dataset, acomprehensive collection of radar data with corresponding LiDAR and camera data,specifically tailored for autonomous driving scenarios and diverse environmental conditions.This dataset fills a critical gap, as existing autonomous vehicle perception datasets oftenprioritize camera and LiDAR recordings, with limited radar data. Using this dataset, wedesign and implement an object detection network optimized for high-resolution radarimagery, addressing the unique characteristics of radar data to enhance detectionperformance. The network is trained and evaluated on our dataset and other public radardatasets, ensuring robust validation of its capabilities.Through these advancements, this thesis enhances the capability of automotive radarsystems for object detection and classification in autonomous vehicles. Integrating deeplearning with radar signal processing boosts radar performance and complements existingperception systems, contributing to safer and more reliable autonomous drivingtechnologies