Vehicle Classification Algorithm using Fuzzy Expert Decision Set

Individual vehicle information, especially, vehicle classification data play a key role in Advanced Traffic Management and Information Systems (ATMIS). In inductive loop and piezo-sensor fusion systems, traffic data such as the vehicle length and the distance between axles are used for vehicle classification. However, classification errors often occur in distinguishing passenger cars from small trucks and in distinguishing medium-sized trucks from small trucks. It is mainly attributed to the fact that they are similar in lengths and have similar inter-axle distances. To improve the performance in vehicle classification, we develop a new algorithm using a fuzzy expert decision set. Vehicle weight and speed are used as the inputs to the fuzzy expert decision set block. The output of the fuzzy expert decision set block is a weighting factor to modify the calculated vehicle length. Experimental results show that the developed algorithm significantly improves the classification performance.