Moreover, numerous successes have already been achieved regarding the classification of such objects. Radar sensors have great advantages over other sensors in estimating the motion states of moving objects, because they detect velocity components within one measurement cycle.
Net radar segment code#
Both our code and trained models will be released. 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.
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. 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. However, they are seldom used for scene understanding due to the size and complexity of radar raw data and the lack of annotated datasets. 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.
Nowadays, this is mostly conducted using cameras and laser scanners, despite their reduced performances in adverse weather conditions. Understanding the scene around the ego-vehicle is key to assisted and autonomous driving.