Self-supervised learning algorithm of environment recognition in driving vehicle

We consider the problem of recognizing the driving environment of a vehicle by using information obtained from some sensors of the vehicle. Previously, we presented a recognition algorithm based on an usual method of pattern matching by use of fuzzy reasoning. Furthermore, this algorithm was extended to meet the demands of nonstandard drivers and changes of vehicle properties. In this algorithm we supposed that an extra source of knowledge (supervisor) for correcting the decision taken by the classifier could be acquired, and inevitably we expected additional sensors. To cover such weakness we present a self-supervised learning algorithm. Here the supervisor is constructed by using a-cut of the membership function of the representative class. Computer simulation on practical uses of the vehicle shows efficiency of the self-supervised learning.