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This paper presents an automatic vehicle classification system based upon laser intensity images obtained from range sensors (called AVCSLII). Current systems that utilize loop detectors, video cameras, and range sensors have deficiencies. The loop detectors have high failure rates due to pavement failures and poor maintenance. Video based systems and range sensors do not perform well in deteriorated atmospheric conditions (such as rain and fog). The developed generations of image based range sensors offer the promise of sensors that are less sensitive to deteriorated environmental conditions. AVCSLII system extracts features of laser intensity images, produced by laser sensory units. These features are used to train a neural network (NN). The AVCSLII system recalls its trained NN for classification of vehicles. This technique outperforms loop detectors, video cameras, and range data techniques in deteriorated environmental conditions.