Aiming at the problems of excessive cycles and latitude disasters in the traditional obstacle hazard division, the system uses a symmetric FIR filter. In this way, using symmetry to optimize the filter coefficients only requires N/ 2 multiplications and N additions. It can generate adaptive obstacle data and shorten the response time of the filter; in the problem of dimensional disaster, the system uses principal component analysis to achieve dimensionality reduction, considering the danger degree, distance, movement state of the obstacle, etc. In the case of a feature parameter, the two features are unified into one data set for processing, so that while improving the flexibility of the classification hyperplane, it also shortens the time required for the classification of obstacles. The result proves that the calculation time of the algorithm of the system is shortened to 0. 25 s, and the accuracy of the recognition of high-risk obstacles reaches 96. 80%.