Toward next generation of driver assistance systems: A multimodal sensor-based platform

Achieving secure vehicles equipped with accident prevention systems is one of the greatest passions of researchers in automotive laboratories. This article provides a detail description on the state of the art techniques for road situation monitoring and driver's behavior analysis (human factors). The focus would be to gain a practical multifaceted approach in order to simultaneous analysis of driver's distraction, and potential road hazards to make an emergency intervention in case of dangerous driving situations. In this regard, a diversity of information via Iris/Pupil status monitoring and EEG spectrum has been gathered and then fused with out-vehicle sensors such as RADAR, LIDAR, Ultrasonic and Vision. In order to cope with computational complexities due to multiple sensors, a heuristic method of multi sensory data fusion and fuzzy solutions has been developed. All discussions are based on real sensors. The method could be applied on various driver assistance systems such as cruise control, overtaking and lane keeping systems.