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Radar is a key component of the suite of perception sensors used for safe andreliable navigation of autonomous vehicles. Its unique capabilities includehigh-resolution velocity imaging, detection of agents in occlusion and overlong ranges, and robust performance in adverse weather conditions. However, theusage of radar data presents some challenges: it is characterized by lowresolution, 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 arefocused on optical features that are relatively weak in radar data, thusresulting in under-utilization of radar's capabilities and diminishing itscontribution to autonomous perception. This review seeks to encourage furtherdeep learning research on autonomous radar data by 1) identifying key researchthemes, and 2) offering a comprehensive overview of current opportunities andchallenges in the field. Topics covered include early and late fusion,occupancy flow estimation, uncertainty modeling, and multipath detection. Thepaper also discusses radar fundamentals and data representation, presents acurated list of recent radar datasets, and reviews state-of-the-art lidar andvision models relevant for radar research. For a summary of the paper and moreresults, visit the website: autonomous-radars.github.io.

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