NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving

Popov, A.; Gebhardt, Patrik; Chen, Ke; Oldja, Ryan

doi:10.1109/icra48891.2023.10160592

Cited by 10 publications

(1 citation statement)

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“…Certain learned radar occupancy estimation models, such as [100], [101], utilize UNet's [102] encoder-decoder architecture on radar point clouds for 2D BEV semantic segmentation of the scene. However, these models only predict static occupancy despite having radar data containing velocity features.…”

Section: A Occupancy Estimationmentioning

confidence: 99%

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

Srivastav,

Mandal

2023

IEEE Access

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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.

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Section: A Occupancy Estimationmentioning

confidence: 99%