A Novel Method of Radar Modeling for Vehicle Intelligence

Event
SAE-TONGJI 2016 Driving Technology of Intelligent Vehicle Symposium
Authors Abstract
Content
The conventional radar modeling methods for automotive applications were either function-based or physics-based. The former approach was mainly abstracted as a solution of the intersection between geometric representations of radar beam and targets, while the latter one took radar detection mechanism into consideration by means of “ray tracing”. Although they each has its unique advantages, they were often unrealistic or time-consuming to meet actual simulation requirements. This paper presents a combined geometric and physical modeling method on millimeter-wave radar systems for Frequency Modulated Continuous Wave (FMCW) modulation format under a 3D simulation environment. With the geometric approach, a link between the virtual radar and 3D environment is established. With the physical approach, on the other hand, the ideal target detection and measurement are contaminated with noise and clutters aimed to produce the signals as close to the real ones as possible. The proposed modeling method not only makes it feasible, safe and convenient to develop and test radar-based Advanced Driver Assistance Systems (ADAS) under various simulated scenarios and environment efficiently at lower cost, it also achieves good balance between model fidelity and computational efficiency. The proposed method further enables real-time simulation under a Hardware-In-the-Loop (HIL) platform. Extensive simulation such as Adaptive Cruise Control (ACC) has been conducted under Matlab/Simulink platform with a Simulink model automatically generated by PanoSim, and the results demonstrate that the proposed radar modeling method is valid and effective.
Meta TagsDetails
DOI
https://doi.org/10.4271/2016-01-1892
Pages
6
Citation
Guo, J., Deng, W., Zhang, S., Qi, S. et al., "A Novel Method of Radar Modeling for Vehicle Intelligence," SAE Int. J. Passeng. Cars – Electron. Electr. Syst. 10(1):50-56, 2017, https://doi.org/10.4271/2016-01-1892.
Additional Details
Publisher
Published
Sep 14, 2016
Product Code
2016-01-1892
Content Type
Journal Article
Language
English