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A fuzzy system to determine the vehicle yaw angle

机译:一种确定车辆偏航角的模糊系统

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The reproduction of the vehicle motion is a crucial element of accident reconstruction. Apart from the position of the center of gravity in an inertial coordinate system, the vehicle heading plays an important role. The heading is the sum of the yaw angle and the vehicle body side slip angle. In standard vehicles, the yaw angle can be determined using the yaw rate sensor and the wheel speeds. However, the yaw rate sensor is often subject to temperature drift. The wheel speed signals are forged at low speeds or due to slip. These errors result in significant deviations of reconstructed and real vehicle heading. Therefore, an intelligent combination of these signals is required. This paper describes a fuzzy system which is capable to increase the accuracy of yaw angle calculation by means of fuzzy logic. Before the data is applied to the fuzzy system, it is preprocessed to ensure the accuracy of the fuzzy system inputs. Afterwards, the reliability of the described sensors is determined according to the driving situation. The heuristic knowledge about the reliability is implemented in the fuzzy system's knowledge base. The right choice of signal inputs is also important to conclude sensor reliability. Depending on the determined reliability, the significance of each individual sensor for yaw rate calculation is adapted. Measurements with a test vehicle proof the validity of the presented system. Tests with artificial errors analyze the fuzzy system's sensitivity to sensor failures.
机译:车辆运动的再现是事故重建的关键因素。除了在惯性坐标系中重心的位置,车辆标题起着重要作用。标题是偏航角和车身侧滑角的总和。在标准车辆中,可以使用横摆率传感器和车轮速度来确定横摆角。然而,横摆率传感器通常受温度漂移。车轮速度信号以低速或由于滑动锻造。这些错误导致重建和实际车辆标题的显着偏差。因此,需要这些信号的智能组合。本文介绍了一种模糊系统,该系统能够通过模糊逻辑提高横摆角计算的准确性。在将数据应用于模糊系统之前,预处理以确保模糊系统输入的准确性。之后,根据驾驶情况确定所描述的传感器的可靠性。关于可靠性的启发式知识在模糊系统的知识库中实现。正确选择信号输入也很重要,可以得出传感器可靠性。根据确定的可靠性,调整了每个单独传感器进行横摆率计算的重要性。用测试车辆证明所提出的系统的有效性测量。具有人工错误的测试分析了模糊系统对传感器故障的敏感性。

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