Adjusting Positions of Vehicle Parts based on Rules for Unknown Drivers

摘要

This paper describes an inference engine used in a system and semantic representation for the system which automatically adjusts the positions of vehicle parts based on rules. The inference engine has rules stored in a knowledge base, which describe the relation between the position of the vehicle part and the driver's body size. The inference engine receives the driver's body sizes in real time, and finds a rule associated with the input values by matching a pattern between them. According to the value defined in the rule, the position of the vehicle part is changed automatically. This rule is automatically modified by learning the relation between the driver's preferred position and body size. The number of selected rules and reasoning time are selected as performance indicators of the inference engine. Also, an ontology is designed to share the development results with others. Automated vehicle parts control system can be used as a method that improves the driver's satisfaction by automatically recommending the driver's preferred position in an environment where many unknown people use the same vehicle like a shared car or a rental car.