Abstract
Automotive radar perception is an integral part of automated driving systems. Radar sensors benefit from their excellent robustness against adverse weather conditions such as snow, fog, or heavy rain. Despite the fact that machine-learning-based object detection is traditionally a camera-based domain, vast progress has been made for lidar sensors, and radar is also catching up. Recently, several new techniques for using machine learning algorithms towards the correct detection and classification of moving road users in automotive radar data have been introduced. However, most of them have not been compared to other methods or require next generation radar sensors which are far more advanced than current conventional automotive sensors. This article makes a thorough comparison of existing and novel radar object detection algorithms with some of the most successful candidates from the image and lidar domain. All experiments are conducted using a conventional automotive radar system. In addition to introducing all architectures, special attention is paid to the necessary point cloud preprocessing for all methods. By assessing all methods on a large and open real world data set, this evaluation provides the first representative algorithm comparison in this domain and outlines future research directions.
Highlights
Automated vehicles are a major trend in the current automotive industry
At intersection over union (IOU)=0.5 it leads by roughly 1% with 53.96% mean Average Precision (mAP), at IOU=0.3 the margin increases to 2%
The increased lead at IOU=0.3 is mostly caused by the high Average Precision (AP) for the truck class (75.54%)
Summary
Automated vehicles are a major trend in the current automotive industry. Applications rank from driver assistance functions to fully autonomous driving. The most prominent representatives are camera, lidar, and radar sensors. While camera and lidar have high angular resolution and dense sensor scans, automotive radar sensors have a good range discrimination. Due to their large wavelength, radar sensors are highly robust against adverse weather situations such as snow, fog, heavy rain, or direct light incidence. They are able to estimate a relative (Doppler) velocity with a single sensor scan, or, more recently, measure polarimetric information [1, 2]. The most common approach to utilize Doppler (2021) 3:6
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
