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The conventional hood with single material and stiffener structural form conceals some limitations on pedestrian protection and lightweight, not satisfying the requirements of structural strength, pedestrian protection and lightweight contradictory with each other at the same time. In this paper, a novel type hood is proposed to develop sandwich structure using architected cellular material with negative Poisson's ratio (NPR) configuration based on the decoupling thought of structural strength and energy absorption. Core-layer aluminum alloy material with NPR is used to meet the requirement of impact energy absorption, inner and outer skin using carbon fiber is selected to achieve high structural stiffness needed. This paper starts from the relations between geometric parameters of core-layer architected cellular material and mechanical properties, on this basis, the optimal geometric parameters can be expected using the multiobjective optimization method.

In order to solve the abnormal vibration of a light bus, order tracking analysis of finite element simulation and road test was made to identify the vibration source, finding that the rotation angular frequency of the wheels and the first two natural frequency of the body structure overlaps, resonance occurring which lead to increased vibration. To stagger the first two natural frequency and excitation frequency of the body, thickness of sheet metal and skeleton of the body-in-white were chosen as the design variables, rise of the first two natural frequency of the body-in-white as the optimization objective, optimal design and sensitivity analysis of the body-in-white was carried out with the modal analysis theory. Combining with the modal sensitivity and mass sensitivity of sheet metal and skeleton, the optimum design was achieved and tests analysis was conducted.

This paper studies a multi-objective optimization design of interior noise for an automotive body. An acoustic-structure coupled model with materials and properties was established to predict the interior noise based on a passenger car. Moreover, three kinds of approximation models related damping thickness and the root mean square of the driver’s ear sound pressure level were established through Latin hypercube method and the corresponding experiments. The prediction accuracy was analyzed and compared for the approximate response surface model, Kriging model and Radial Basis Function neural network model. On this basis, multi-objective optimization of the vehicle interior noise was conducted by using NSGA-II. According to the optimization results, the damping composite structure was applied on the car body structure. Then, the comparison of sound pressure level response at driver’s ear location before and after optimization was performed at speed of 60 km/h on a smooth road.

Lane detection and tracking play a key role in autonomous driving, not only in the LKA System but help estimate the pose of the vehicle. While there has been significant development in recent years, traditional outdoor SLAM algorithms still struggle to provide reliable information in challenging dynamic environments such as lack of roadside landscape or surrounding vehicles at almost the same speed or on the road in the woods. On the structured road, lane markings as static semantic features may provide a stable landmark assist in robust localization. As most of the current lane detection work mainly on separated images ignoring the relationship between adjacent frames, we propose a pixel-level lane tracking method for autonomous vehicles. In this paper, we introduce a deep network to detect and track lane features. The network has two parallel branches. One branch detects the lane position, while the other extracts the point description on a pixel level.

For the roll vibration problem of a Truck, a 4-DOF roll vibration model of its front suspension system was built. According to dynamics theory, the complex modal vibration modes of the model were all obtained. At the same time, the frequency response functions of frame roll angle acceleration, the relative dynamic load of wheel and the suspension dynamic deflection were respectively presented. Then their characteristics were respectively researched. In the process of characteristic analysis, a new system parameter was proposed, which is the space ratio of the space between suspensions of left and right sides and the wheel track of the front axle (space ratio in short). At last, the influence of system parameters on the vibration transmission property was also reserached, which included the natural frequency of the frame, the damping ratio, the stiffness ratio, the mass ratio, the rotational inertia ratio and the space ratio.

This paper addresses the issues of long-term signal loss in localization and cumulative drift in SLAM-based online mapping and localization in autonomous valet parking scenarios. A GPS, INS, and SLAM fusion localization framework is proposed, enabling centimeter-level localization with wide scene adaptability at multiple scales. The framework leverages the coupling of LiDAR and Inertial Measurement Unit (IMU) to create a point cloud map within the parking environment. The IMU pre-integration information is used to provide rough pose estimation for point cloud frames, and distortion correction, line and plane feature extraction are performed for pose estimation. The map is optimized and aligned with a global coordinate system during the mapping process, while a visual Bag-of-Words model is built to remove dynamic features.