A Human-Vehicle Collaborative Driving Framework for Driver Assistance

With a goal to improve transportation safety, this paper proposes a collaborative driving framework based on assessments of both internal and external risks involved in vehicle driving. The internal risk analysis includes driver drowsiness detection and driver intention recognition that helps to understand the human driver's behavior. Steering wheel data and facial expression are used to detect the driver's drowsiness. Hidden Markov models are adapted to recognize the driver's intention using the vehicle's lane position, control, and state data. For the external risk analysis, a co-pilot utilizes a collision avoidance system to estimate the collision probability between the ego vehicle and other nearby vehicles. Based on the risk analyses, we design a novel collaborative driving scheme by fusing the control inputs from the human driver and the co-pilot to obtain the final control input for the ego vehicle under different circumstances. The proposed collaborative driving framework is validated in an assisted-driving testbed, which enables both autonomous and manual driving capabilities.