The invention provides an automatic driving target detection uncertainty method for improving expected function safety, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining a training set, a verification set and a test set through collecting driving environment data; secondly, an existing YOLO target detector is improved, cognitive uncertainty estimation and random uncertainty estimation are carried out on the existing YOLO target detector, and random uncertainty is used for calibrating the confidence coefficient of the YOLO target detector; and finally, the data in the test set is detected through the calibrated YOLO target detector, and the calibrated YOLO target detector directly outputs the bounding box, the category label and the calibrated confidence of each target. According to the method, random uncertainty estimation is incorporated into a loss function, the model confidence coefficient is calibrated through iterative learning, the robustness of the model is enhanced, and thus the reliability and safety of the automatic driving system in a complex driving environment are improved.
本发明提供一种提升预期功能安全的自动驾驶目标检测不确定性的方法,涉及自动驾驶技术领域,本方法通过采集行驶环境数据获得训练集、验证集和测试集;然后对现有YOLO目标检测器进行改进,对其分别进行认知不确定性估计和随机不确定性估计,并使用随机不确定性来校准YOLO目标检测器置信度;最后将测试集中的数据通过校准后的YOLO目标检测器进行检测,校准后的YOLO目标检测器直接输出每个目标的边界框、类别标签以及校准后的置信度。本申请将随机不确定性估计纳入损失函数,通过迭代学习校准模型置信度,增强模型的鲁棒性,从而提升自动驾驶系统在复杂驾驶环境下的可靠性和安全性。
Automatic driving target detection uncertainty method for improving expected function safety
提升预期功能安全的自动驾驶目标检测不确定性的方法
2025-02-11
Patent
Electronic Resource
Chinese
IPC: | B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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