Radar Based Object Detection and Tracking for Autonomous Driving

被引：0
|
作者：
Manjunath, Ankith [1 ]
Liu, Ying [1 ]
Henriques, Bernardo [1 ]
Engstle, Armin [1 ]
机构：
[1] AVL Softwares & Funct GmbH, Dept Autonomous Driving, Regensburg, Germany
来源：
2018 IEEE MTT-S INTERNATIONAL CONFERENCE ON MICROWAVES FOR INTELLIGENT MOBILITY (ICMIM) | 2018年
关键词：
ADAS; radar; Kalman filtering; clustering; data association; EKF; UKF; DBSCAN; JPDAF; CTRV;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Radar sensor has been an integral part of safety critical applications in automotive industry owing to its weather and lighting independence. The advances in radar hardware technology have made it possible to reliably detect objects using radar. Highly accurate radar sensors are able to give multiple radar detections per object. This work presents a postprocessing architecture, which is used to cluster and track multiple detections from one object in practical multiple object scenarios. Furthermore, the framework is tested and validated with various driving maneuvers and results are evaluated.
引用
收藏
页码：126 / 129
页数：4
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