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Vehicle safety systems may use occupant physiological information, e.g., occupant heights and weights to further enhance occupant safety. Determining occupant physiological information in a vehicle, however, is a challenging problem due to variations in pose, lighting conditions and background complexity. In this paper, a novel occupant height estimation approach is presented. Depth information from a depth camera, e.g., Microsoft Kinect is used. In this 3D approach, first, human body and frontal face views (restricted by the Pitch and Roll values in the pose estimation) based on RGB and depth information are detected. Next, the eye location (2D coordinates) is detected from frontal facial views by Haar-cascade detectors. The eye-location co-ordinates are then transferred into vehicle co-ordinates, and seated occupant eye height is estimated according to similar triangles and fields of view of Kinect.

Driver state sensing technologies start to be widely used in vehicular systems developed from different manufacturers. To optimize the cost and minimize the intrusiveness towards driving, majority of these systems rely on in-cabin camera(s) and other optical sensors. With their great capabilities of detecting and intervening driver distraction and inattention, these technologies might become key components in future vehicle safety and control systems. However, currently there are no common standards available to compare the performance of these technologies, thus it is necessary to develop one standardized process for the evaluation purpose.

A vehicle integrated sensing and analysis system has been designed, implemented, and demonstrated to nonintrusively monitor several biological signals of the driver. The biological driver signals measured by the system are the heart electrical signals or pseudo Lead-I electrocardiography (pLI-ECG), the galvanic skin response (GSR) or electrical conductance measured from the driver's fingers to palm, the palm skin temperature, the face skin temperature, and the respiration rate. The pLI-ECG and GSR measurements are made through direct contact of the driver hands with stainless steel electrodes integrated in the steering wheel rim. The temperature measurements are made with non-contacting infrared temperature sensors, also located on the steering wheel. The respiration rate was measured using a flexible thin film piezoelectric sensor affixed to the seatbelt.

Predictive crash sensing and deployment control of safety systems require reliable and accurate kinematic information about potential obstacles in the host vehicle environment. The projected trajectories of obstacles in the path of the vehicle assist in activation of safety systems either before, or just after collision for improved occupant protection. This paper presents an analysis of filtering and estimation techniques applied to imminent crash conditions. Optimization of design criteria to achieve required response performance, and noise minimization, are evaluated based on the safety system to be activated. The predicted target information is applied in the coordinated deployment of injury mitigation safety systems.

Integrated, microprocessor-based, predictive crash systems provide opportunities for significant improvements in automobile safety. Consequently, next generation safety systems will incorporate various kinds of engineering concepts such as radar and laser-based sensors as well as vision-based electronic imaging systems that track the distance and closing velocity of objects detected to be on a potential collision path with a vehicle. A discussion on the synergies obtained from the integration of sensor systems for enhanced performance of safety products is presented. A multi-level situation awareness approach to assess the potential for imminent collision, and activate safety actuator systems to meet the challenges of improved safety, is presented.