🤖 AI Summary
This study addresses the challenges of missed detections and inaccurate motion parameter estimation for near-field targets in automotive pre-crash scenarios by proposing an anchor-based deep learning model tailored for high-resolution automotive radar data. The method innovatively incorporates radar image dilation into the input feature channels to enhance local micro-Doppler signatures, effectively mitigating issues caused by sparse point clouds, signal fluctuations, and multipath interference. Experimental results demonstrate that the proposed model significantly outperforms conventional radar tracking approaches on a real-world pre-crash dataset, exhibiting superior generalization capability and higher detection reliability in dynamic environments. These improvements provide robust perceptual support for intelligent safety systems, such as pretensioner airbags, enabling more accurate and timely responses.
📝 Abstract
Advanced automated driving presents significant potential to improve modern automotive safety systems, but it depends highly on the reliable activation of restraint systems. Forward-looking sensors are crucial for immediate and precise object detection. Recent developments in automotive radar technology enable detailed environment detection and the recognition of high-resolution features, such as micro-Doppler signatures. Combined with advanced AI techniques, these features significantly enhance object detection and improve the accuracy of kinematic parameter estimation. This is essential for the early and reliable activation of irreversible safety systems, such as smart airbags and adaptive seat belts. Therefore, an anchor-based AI model is presented, designed to process high-resolution radar data with an explicit focus on micro-Doppler signatures to improve pre-crash object detection. Furthermore, these signatures can improve the accuracy of kinematic object parameter estimation and reduce false negatives, especially in the critical near-field. To address the challenges of sparse and fluctuating radar point clouds, an innovative radar-image dilation technique on the feature input channels was developed to amplify local radar patterns, like micro-Doppler features. Therefore, this approach increases the system's reliability and increases its ability to detect objects in pre-crash scenarios despite radar multipath reflections and ghost objects. In order to investigate the applicability and compare the model's performance with advanced automotive radar tracking methods, a radar data set using series sensors and pre-crash relevant scenarios was recorded. The results demonstrate the advantages of the anchor-based AI model over established tracking approaches. It excels at estimating object parameters in dynamic scenarios and underscores its ability to process different data sets effectively.