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The driver's face detection and tracking method important for Advanced Driver Assistance Systems (ADAS) and autonomous driving in various situations. The deep SORT algorithm has integrated appearance information, the motion model and the intersection-over-union (IOU) distance methods, and has been applied to face tracking, but it depends on detection information in every frame. Once the detection information lacks, the deep SORT algorithm will wait until the target detects bounding boxes appear again, even if the target didn’t disappear or shield. Hence, we propose to use a new tracker that not completely depend on the detection algorithm to cascade with the deep SORT algorithm to realize stable driver's face tracking. At first, the driver's face detection and tracking will be accomplished by the MTCNN-deep-SORT algorithm.

Statistic analysis on commercial vehicle crash accidents in China were done by using the annual traffic accident reports from Ministry of Public Security. The Chinese crash safety rules on commercial vehicle were introduced. The main reasons which cause severe injury to the passenger in the cab in frontal crash accidents were studied. HYPERMESH software was used to do the finite element modelling of the frontal structure and cab of a production truck. The swing hammer impact simulation was conducted by using LS-DYNA software and the results were compared with the test results to validate the model. A new supporting structure for the cab to improve the safety of the passenger in cab was proposed. Meanwhile, an extendable and retractable longitudinal beam energy absorbing structure was also studied by using the finite element model. The simulation results show that these structures can obviously improve the frontal crash safety of the commercial vehicle.

As one of the most challenging driving tasks, parking is a common but particularly troublesome problem in large cities. Recently, an excellent solution-automated valet parking (AVP) has become a hot research topic, which allows the driver to leave the vehicle in a drop-off area, while the vehicle driving into the parking slot by itself. For AVP, the precise localization is an indispensable module. However, the global positioning system (GPS) cannot be used in the underground parking lot and the localization method based on lidar is too expensive. In response to solve this problem, we propose a simultaneous localization and mapping system with the semantic information of parking slots (PS-SLAM), which is based on visual-inertial and around view images. First, the calibration of multi-sensors is conducted to obtain their intrinsic and extrinsic parameters. In this way, the around view image and transformation matrices between sensors can be acquired.