Data on distracted driving conditions and passenger retention conditions in the car were collected, and combined with a neural network model, the lightweight network MobileNet V2 in the end-to-end distracted driving network was used to achieve distracted driving recognition. By connecting YOLO V5 target detector, DBFace face detector, and MobileNet V2 face classifier in series, an accurate health and safety monitoring plan was designed. Real vehicle test verification schemes were designed respectively, to verify the feasibility of two vehicle occupant health and safety tasks. The results show that the proposed scheme has high detection accuracy while ensuring real-time performance.

In order to solve the problem that the detection of children left in the car is easily disturbed by external factors, this paper proposed a detection algorithm for children left in the car based on limb size. First, the OpenPose algorithm was used to find the pixel coordinates of the human skeleton points on the infrared image, and then the camera internal parameters and picture depth information were used to calculate the limb size, and finally the body size was fitted to the volume of the occupants in the car for adult or child judgment. The experimental results show that this method has higher accuracy compared with other methods, such as infrared thermal sensor, capacitive sensor, is not easily disturbed by other environmental factors.

In order to realize wide application of traditional image processing methods in intelligent transportation systems, the paper studied the identification and counting methods of vehicles in highway surveillance video. Using Python programming language and based on the OpenCV library, the design of the identification and counting function of vehicles was completed through a series of traditional image processing methods such as grayscale, denoising, background subtraction and morphological operations. This solution was compared with the solution based on the combination of the YOLOv3 model and the Simple Online and Realtime Tracking (SORT) algorithm. The results show that traditional image processing methods can detect moving target, but there are problems of low accuracy and low versatility compared with the solution based on deep learning. The paper therefore proposed the research suggestion of combining traditional image processing and deep learning.

In view of the diversified development of occupants’ sitting postures of intelligent vehicles in the future, vehicle restraint systems MADYMO multi-rigid-body simulation model was set up for the 50 percentile male dummies, and its reliability was validated according to the real vehicle crash test data, and then the occupants’ sitting postures were divided into five categories through the model. And the influence of the combination of Autonomous Emergency Braking (AEB) system and seat belt on the dummy’ injury risk of four kinds of dummy sitting postures except for the standard sitting postures in frontal crashing was studied. The synthetic acceleration of the head and hip of the dummy and the force of each part of the dummy were analyzed. The results show that the occupant restraint system with standard sitting posture can improve the head acceleration of occupant with different sitting posture, and different sitting posture will cause different injuries.

This paper analyzed the main changes of new China-New Car Assessment Program (C-NCAP) side crash barrier and challenges of vehicle structure, simultaneously analyzed the difference of vehicle structure deformation after impact based on side impact FEA result of a vehicle under new side impact test condition. The paper also proposed to improve material strength and thickness of doors and B pillar, enlarge the area joining door cover with auto body, this proposal achieved a weight reduction of 15% compared with the traditional solution.

To improve the structural crashworthiness of the car, a finite element model of the front rail of the vehicle was established with the front rail of a Plug-in Hybrid Electric Vehicle (PHEV) as research object, China New Car Assessment Program (C-NCAP) were referred to and based on the frontal 100% overlapping rigid barrier crash test. With the cross section of car front rail, interior & exterior panel material and thickness as the research object, and the specific energy absorption, mean crush force and collapse efficiency were taken as the research objective, the front rail of the vehicle was simulated and analyzed. The sample points were selected based on the Hamersley sampling method, and the response surface of the front rail was optimized by the improved least squares method. The results showed that the optimized specific energy absorption of the front rail of the vehicle increased by 1 129.14 J/kg, and the mean crush force increased by 8.2 kN, the crushing efficiency increased by 2.59 percent point.

In order to study the injury of occupants in different sitting postures, a simulation model with Thor-AV dummy and vehicle was constructed based on a vehicle. A C-NCAP 100% overlap rigid barrier crash simulation was carried out for three groups of occupant dummies with different sitting postures (seat cushion angle 25°, backrest 42°, 52° and 62°). The results show that, the larger the backrest angle, the higher the head injury risk. Neck shear force and axial tension gradually increase with the backrest angle. There is of great risk with clavicle fracture during crash. Under the influence of the increase of backrest angle, the axial compression force of thoracic T12 poses a great threat to occupant safety, with the maximum compression force reaching 11.6 kN when the backrest angle is 62°. The restraint system could not effectively protect the occupants in the large-angle backrest seat of the vehicle.

Finite element model of a BIW was built by applying HyperMesh software and based on lightweight requirements, this paper proposed to replace advanced high-strength steel with hot stamping steel PHS1500 for BIW A-pillar reinforcement plates and replace B410LA with DP780 for the front longitudinal beam, and replace QSTE500TM with QP980 for the front guard cross member, the paper also proposed to reduce thickness. According to C-NCAP management rules, LS-DYNA was applied to perform a 100% overlap rigid wall collision analysis for the BIW model at 50 km/h. The results show that based on the results of the frontal collision analysis of traditional high-strength steel materials, after the material upgrade the intrusive amount at A-pillar reinforcement site and fireproof board leg inspection point decreases by 8.9% and 15.9%, respectively, the frontal collision performance of the BIW has been greatly improved, and the weight is reduced by 6.224 kg.