IEEE Trans Cybern. 2017 Nov;47(11):3609-3620. doi: 10.1109/TCYB.2016.2573321. Epub 2016 Jun 9.
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.
本文通过惯性测量研究摩托车的碰撞和危险检测。对于这种车辆，最困难的挑战是区分道路异常和真正的危险。这通常通过在加速度计测量上设置绝对阈值来完成。这些阈值是通过昂贵的碰撞测试从经验上调整的。当要处理的信号数量增加时，这种经验方法既昂贵又不直观。我们提出了一种基于自组织神经网络的方法，可以处理来自不同类型传感器的大量输入。该方法使用加速度计和陀螺仪测量。尽管不需要进行碰撞测试来进行训练，但所提出的方法能够识别危险情况。该方法在仿真环境中进行了测试；与基准方法的比较表明了所提出方法的优势。
