Multi-sensor Fusion for In-cabin Vehicular Sensing Applications

Cyber-physical-human systems (CPHS) in AI-based driver assistance applications require the integration of data from diverse modalities. These in-cabin CPHSs offer rich sensing capabilities, encompassing the vehicle, its surroundings, and the driver. One of the primary challenges in CPHSs is the incorporation of human behavior modeling to steer interactions that bolster human performance. This study introduces an in-cabin vehicular sensing framework that merges a driving simulator with a CPHS, thereby enabling researchers to devise and test multi-sensor fusion methodologies in human-in-the-loop driving situations. Initial experiments reveal that a driver interruptibility model can be effectively trained using the data gathered from our system.