Agent-Based Driver Abnormality Estimation

For enhancing current driver assistance and information systems with regard to the capability to recognize an individual driver's needs, we conceive a system based on fuzzy logic and a multi-agent-framework. We investigate how it is possible to gain useful information about the driver from typical vehicle data and apply the knowledge on our system. In a pre-stage, the system learns the driver's regular steering manner with the help of fuzzy inference models. By comparing his regular and current manner, the system recognizes whether the driver is possibly impaired and betakes in a risky situation. Furthermore, the steering behavior and traffical situation are continuously observed for similar pattern. According to the obtained information, the system tries to conform its assistance functionalities to the driver's needs.