Turn-Intent Analysis Using Body Pose for Intelligent Driver Assistance

Human-centric, pervasive computing environments, with integrated sensing, processing, networking, and displays, provide an appropriate framework for developing effective driver-assistance systems. Also essential when developing such systems are systematic efforts to understand and characterize driver behavior. In an attempt to make such a predictive turn-assistance safety system a reality, we equipped an experimental vehicle with cameras and sensors to capture the vehicle dynamics, view of the road ahead, and driver´s body pose. We investigated how and to what extent we could use body-pose information to detect and predict driver activities. We analyzed the detection performance of a two-class pattern classifier using receiver-operator-characteristic curves, which describe the classifier´s ability to suppress missed detections and false alarms. The curves provide a ratio indicating the system´s attainable proactivity (ability to foresee a user´s needs) versus its transparency (ability to avoid user annoyance). Our goal is to eventually develop vision-based body-pose-recovery and behavior-recognition algorithms for driver-assistance systems