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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.

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.

The combination of passive and active vehicle safety technologies can effectively improve vehicle safety. Most of them predict vehicle crashes using radar or video, but they can't be applied extensively currently due to the high cost. Another collision forecasting method is more economic which is based on the driver behavior and vehicle status, such as the acceleration, angular velocity of the brake pedal and so on. However, the acceleration and angular velocity of the brake pedal will change with the driver and the vehicle type. In order to study the effect of different drivers and vehicle types on the braking acceleration and angular velocity of the brake pedal, six volunteers were asked to drive five vehicles for simulating the working conditions of emergency braking, normal braking, inching braking and passing barricades under different velocities. All the tests were conducted on asphalt road, and comprehensive experimental design was used to arrange tests.