Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges

TL;DR

This paper reviews deep learning methods for radar in autonomous driving, highlighting challenges like low resolution and data scarcity, and discusses opportunities for advancing radar perception in autonomous vehicles.

Contribution

It provides a comprehensive overview of current radar deep learning research, identifying key themes, challenges, and opportunities to enhance autonomous vehicle perception.

Findings

Radar offers high-resolution velocity imaging and robustness in adverse weather.

Current radar models are under-utilized due to data challenges and reliance on optical models.

The paper highlights opportunities for future research in radar data fusion and uncertainty modeling.

Abstract

Radar is a key component of the suite of perception sensors used for safe and reliable navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity imaging, detection of agents in occlusion and over long ranges, and robust performance in adverse weather conditions. However, the usage of radar data presents some challenges: it is characterized by low resolution, sparsity, clutter, high uncertainty, and lack of good datasets. These challenges have limited radar deep learning research. As a result, current radar models are often influenced by lidar and vision models, which are focused on optical features that are relatively weak in radar data, thus resulting in under-utilization of radar's capabilities and diminishing its contribution to autonomous perception. This review seeks to encourage further deep learning research on autonomous radar data by 1) identifying key research themes, and 2) offering a comprehensive overview of current opportunities and challenges in the field. Topics covered include early and late fusion, occupancy flow estimation, uncertainty modeling, and multipath detection. The paper also discusses radar fundamentals and data representation, presents a curated list of recent radar datasets, and reviews state-of-the-art lidar and vision models relevant for radar research. For a summary of the paper and more results, visit the website: autonomous-radars.github.io.