PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain

TL;DR

PolarNet introduces a deep neural network that processes automotive radar data in the polar domain, achieving fast, accurate open space segmentation to enhance autonomous driving perception systems.

Contribution

This paper presents PolarNet, a novel deep learning model specifically designed for radar data in the polar domain, improving robustness and efficiency over traditional methods.

Findings

Achieves state-of-the-art performance in radar-based open space segmentation.

Provides faster processing speeds compared to existing approaches.

Maintains a compact model size suitable for real-time applications.

Abstract

Camera and Lidar processing have been revolutionized with the rapid development of deep learning model architectures. Automotive radar is one of the crucial elements of automated driver assistance and autonomous driving systems. Radar still relies on traditional signal processing techniques, unlike camera and Lidar based methods. We believe this is the missing link to achieve the most robust perception system. Identifying drivable space and occupied space is the first step in any autonomous decision making task. Occupancy grid map representation of the environment is often used for this purpose. In this paper, we propose PolarNet, a deep neural model to process radar information in polar domain for open space segmentation. We explore various input-output representations. Our experiments show that PolarNet is a effective way to process radar data that achieves state-of-the-art performance and processing speeds while maintaining a compact size.