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The problem of path planning for the autonomous vehicle in environment with moving and stationary obstacles is considered. An algorithm based on modified Kohonen rule and behavioural cloning (MKBC) is developed. The MKBC algorithm, as improvement of RBF neural network, uses the training values as weighting values, rather then values from the previous time instance. This enables an intelligent system to learn from examples (operator's demonstrations) to control a robot vehicle, in this case, to avoid stationary or moving obstacle. Important characteristic of the MKBC algorithm is polynomial complexity, while most other path planning algorithms are exponential. Experiments determined that it is robust to parameter change and suitable for real time application.