AV Occupant ID Optical Based Occupant Identification and Classification for Autonomous Vehicles
Matt Reed, Don B. Chaffin Collegiate Research Professor – The University of Michigan Transportation Research Institute
Due to recent advances in sensing technologies, modern vehicle occupant classification systems enable personalized vehicle experiences and adaptive occupant crash protection. However, most systems are limited to occupant detection and simple classification, and thus, accurate estimation of body characteristics are needed to support more advanced occupant classification. This paper presents a model-based characterization method for vehicle occupants using a 3D depth camera. This method automatically estimates standard anthropometric data of an occupant such as stature and weight along with the body shape by fitting a statistical body shape model to depth image data. The system is even robust to a wide range of clothing and is capable of generating accurate results. A variety of other algorithms were developed to improve the fitting result, including seat geometry detection and head location estimation. The new capability has a range of potential applications for improving occupant safety and providing an optimized interior configuration for the occupant. The final report for this project will not be publicly available.
Institution(s): University of Michigan Transportation Research Institute
Award Year: 2017
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