Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.

In this paper, a multi-cell design method with built-in stiffeners is proposed to improve the crashworthiness of metal circular tubes. In the study, multi-cell circular tubes with three different stiffeners (Line, Waved, Polyline) were subjected to compression tests under quasi-static load. The finite element model of the metal circular tube was validated by ABAQUS/EXPLICIT, and the geometry of the stiffener was designed on this basis. The results showed that the energy absorption of the multi-cell tube was significantly better than that of the initial tube. Further, ‘Waved’ and ‘Polyline’ are more conducive to improving the crashworthiness of the multi-cell tube from the geometric design of the stiffeners. Through the analysis of load-displacement responses and crashworthiness indexs, it was found that the more the number of ‘Waved’ or ‘Polyline’ (from 2 to 8), the better the energy absorption of the multi-cell tubes and the smaller the load fluctuation. In addition the effect of the number of stiffeners on energy absorption was also revealed.

It is essential to obtain accurate location of vehicles for new applications of Intelligent Transportation Systems. To remedy the defects of present Global Positioning System and vehicle-to-infrastructure (V-I) positioning technology, a new positioning approach based on vision and V-I communication is proposed. This approach aims at lane-level positioning with lower cost than conventional ones. In this approach, the position of the vehicle is represented by its lateral position (the lane number) and longitudinal position (the distance from entrance of the road) in a course coordinate system along the road; the specific lane the vehicle is occupying (the lane number) can be judged using the information of lane lines detected by vision systems; then the distance to the vehicle is obtained by a Road Side Unit (RSU) during the V-I communication; and the longitudinal position is calculated. The error of the approach on typical operating conditions is analyzed, indicating that the new approach can achieve the accuracy of less than 0.31 m for straight road and 0.58 m for typical arc road with ultra-wideband communication and ranging technologies and rational arrangement of RSUs. The feasibility of this approach is presented.