Hazard Detection for Motorcycles via Accelerometers: A Self-Organizing Map Approach
提出一种基于自组织神经网络的摩托车碰撞与危险检测方法，利用加速度计和陀螺仪数据识别危险状况，无需昂贵的碰撞测试训练，仿真实验显示优于传统阈值方法。
This paper deals with collision and hazard detection for motorcycles via inertial measurements. For this kind of vehicles, the most difficult challenge is to distinguish road's anomalies from real hazards. This is usually done by setting absolute thresholds on the accelerometer measurements. These thresholds are heuristically tuned from expensive crash tests. This empirical method is expensive and not intuitive when the number of signals to deal with grows. We propose a method based on self-organized neural networks that can deal with a large number of inputs from different types of sensors. The method uses accelerometer and gyro measurements. The proposed approach is capable of recognizing dangerous conditions although no crash test is needed for training. The method is tested in a simulation environment; the comparison with a benchmark method shows the advantages of the proposed approach.