Driving-style-oriented human-like automated driving and its verification

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Analyses of road accident data have shown that there are more than 1.2 million road fatalities every year worldwide. In most of the traffic accidents, the driver behaviour or driver error is the most critical causal factor. These are calling for an emerging development of vehicle automation which provides not only the improved driving safety, but also the increased efficiency and mobility for children, elderly, disabled who are not able to drive.*******The currently existing automated vehicles developed by several car manufacturers provide a very encouraging future for automated driving, however, fully automated driving is still infeasible at a large scale before overcoming some important technological challenges. Studies have also shown that many users experience motion sickness while riding in an automated vehicle, which would indicate a considerable negative impact on the public acceptance and quick penetration of automated vehicles. Furthermore, higher-level automated vehicles would be expected to provide users sufficient flexibilities for doing work-related tasks in the vehicle on the road. In order to enhance the user-friendliness as well as in-vehicle task efficiency for automated vehicles, this proposed research aims to develop a novel framework of human-like automated driving that integrates driving style recognition and autonomous driving algorithms. The research outcomes will help to improve the user friendliness, comfort and also achieve personalization of automated vehicles.*******The proposed research program will initiate a new paradigm for human-like automated driving with new scientific advances and technological innovations. The framework effectively bridges the fundamental understanding and characterization of human driver driving styles and autonomous driving trajectory planning and tracking control, for achieving the ‘human-like' attribute in automated driving. The new paradigm will help to enable the car companies to develop new-generation of automated driving vehicles to be personalized and more user-friendly. Furthermore, the research outcomes of this program will assist to boost the public acceptance, offer recommendations related to user experience in automated vehicles for policy makers, and accelerate the penetration of automated vehicles. The research program will also offer an excellent HQP training opportunity for the four graduate students directly involved as well as other student members in the laboratory. ******