With the rapid development of intelligent and connected vehicles, the experimental road test for the advanced driving assistance system (ADAS) is dramatically increasing around the world. Considering its high cost and hazardous situations, simulation test based on a dummy model is becoming a promising way for ADAS road test practice to reduce the experiment expanses. This study proposed a methodology for the evaluation of the performance of human and dummies with distinct designed materials based on the data extracted from the Doppler effect of millimeter-wave radar. Echo data of 8 different angles from 0 to 360 degrees, with the an interval of 45 degrees, at the same distance between the test object and the signal source is collected. Meanwhile, the echo energy is collected for correlation modeling and analysis among groups. By evaluating the performance of humans and dummies via statistical analysis, a close correlation was found which results verified the substitutability of the dummy for the ADAS experiment test. The correlation coefficient between human and dummies ranges from 0.75 to 0.93. The support vector machine (SVM) model was developed and fitted to predict the echo energy in diverse environments. The mean average error (MAE) is 5.42–11.42 in the training and testing datasets while root mean square error (RMSE) is 0.43–0.90. The methods developed in the study can simulate the real ADAS road test environment and support future experimental research.

随着智能网联汽车的快速发展，全球范围内高级驾驶辅助系统 （ADAS） 的实验性道路测试正在急剧增加。考虑到其高成本和危险情况，基于虚拟模型的仿真测试正在成为 ADAS 道路测试实践减少实验范围的一种有前途的方式。本研究根据从毫米波雷达多普勒效应中提取的数据，提出了一种评估具有不同设计材料的人和假人性能的方法。在测试对象与信号源之间相同距离处，以 45 度的间隔收集 8 个不同角度的回波数据，间隔为 360 度。同时，收集回波能量用于组间的相关性建模和分析。通过统计分析评估人类和假人的性能，发现两者之间存在密切相关性，结果验证了假人在 ADAS 实验测试中的可替代性。人和假人之间的相关系数范围为 0.75 到 0.93。开发并拟合支持向量机 （SVM） 模型以预测不同环境中的回波能量。训练和测试数据集中的平均误差 （MAE） 为 5.42-11.42，而均方根误差 （RMSE） 为 0.43-0.90。研究中开发的方法可以模拟真实的 ADAS 道路测试环境，并支持未来的实验研究。