Multi-View Radar Semantic Segmentation

TL;DR

This paper introduces novel multi-view radar segmentation architectures that effectively analyze radar tensor data for scene understanding, outperforming existing models while using fewer parameters, thus advancing autonomous driving safety in adverse weather.

Contribution

The paper presents new multi-view radar segmentation models and loss functions that improve scene understanding from radar data with fewer parameters than existing methods.

Findings

Our best model outperforms alternatives on the CARRADA dataset.

The proposed architectures require fewer parameters.

Results demonstrate effective radar scene segmentation in adverse weather.

Abstract

Understanding the scene around the ego-vehicle is key to assisted and autonomous driving. Nowadays, this is mostly conducted using cameras and laser scanners, despite their reduced performances in adverse weather conditions. Automotive radars are low-cost active sensors that measure properties of surrounding objects, including their relative speed, and have the key advantage of not being impacted by rain, snow or fog. However, they are seldom used for scene understanding due to the size and complexity of radar raw data and the lack of annotated datasets. Fortunately, recent open-sourced datasets have opened up research on classification, object detection and semantic segmentation with raw radar signals using end-to-end trainable models. In this work, we propose several novel architectures, and their associated losses, which analyse multiple "views" of the range-angle-Doppler radar tensor to segment it semantically. Experiments conducted on the recent CARRADA dataset demonstrate that our best model outperforms alternative models, derived either from the semantic segmentation of natural images or from radar scene understanding, while requiring significantly fewer parameters. Both our code and trained models are available at https://github.com/valeoai/MVRSS.