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Found 9 papers, 1 papers with code
RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications
A new automotive radar data set with measurements and point-wise annotations from more than four hours of driving is presented.
Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data
A complete overview of the surrounding vehicle environment is important for driver assistance systems and highly autonomous driving.
Off-the-shelf sensor vs. experimental radar -- How much resolution is necessary in automotive radar classification?
Furthermore, the generalization capabilities of both data sets are evaluated and important comparison metrics for automotive radar object detection are discussed.
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar
In this work, we depart from visible-wavelength approaches and demonstrate detection, classification, and tracking of hidden objects in large-scale dynamic environments using Doppler radars that can be manufactured at low-cost in series production.
A Multi-Stage Clustering Framework for Automotive Radar Data
The merging process is often implemented in form of a clustering algorithm.
Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices
The reference system allows to much more precisely generate real world radar data distributions of VRUs than compared to conventional methods.
Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles
Radar-based road user classification is an important yet still challenging task towards autonomous driving applications.
Automated Ground Truth Estimation of Vulnerable Road Users in Automotive Radar Data Using GNSS
Annotating automotive radar data is a difficult task.
Radar-based Feature Design and Multiclass Classification for Road User Recognition
The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions.
