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SLAM (Simultaneous Localization and Mapping) plays a key role in autonomous driving. Recently, 4D Radar has attracted widespread attention because it breaks through the limitations of 3D millimeter wave radar and can simultaneously detect the distance, velocity, horizontal azimuth and elevation azimuth of the target with high resolution. However, there are few studies on 4D Radar in SLAM. In this paper, RI-FGO, a 4D Radar-Inertial SLAM method based on Factor Graph Optimization, is proposed. The RANSAC (Random Sample Consensus) method is used to eliminate the dynamic obstacle points from a single scan, and the ego-motion velocity is estimated from the static point cloud. A 4D Radar velocity factor is constructed in GTSAM to receive the estimated velocity in a single scan as a measurement and directly integrated into the factor graph. The 4D Radar point clouds of consecutive frames are matched as the odometry factor.

The current automotive industry has a growing demand for real-time transmission to support reliable communication and for key technologies. The Time-Sensitive Networking (TSN) working group introduced standards for reliable communication in time-critical systems, including shaping mechanisms for bounded transmission latency. Among these shaping mechanisms, Cyclic Queuing and Forwarding (CQF) and frame preemption provide deterministic guarantees for frame transmission. However, despite some current studies on the performance analysis of CQF and frame preemption, they also need to consider the potential effects of their combined usage on frame transmission. Furthermore, there is a need for more research that addresses the impact of parameter configuration on frame transmission under different situations and shaping mechanisms, especially in the case of mechanism combination.

Accurate and reliable localization in GNSS-denied environments is critical for autonomous driving. Nevertheless, LiDAR-based and camera-based methods are easily affected by adverse weather conditions such as rain, snow, and fog. The 4D Radar with all-weather performance and high resolution has attracted more interest. Currently, there are few localization algorithms based on 4D Radar, so there is an urgent need to develop reliable and accurate positioning solutions. This paper introduces RIO-Vehicle, a novel tightly coupled 4D Radar/IMU/vehicle dynamics within the factor graph framework. RIO-Vehicle aims to achieve reliable and accurate vehicle state estimation, encompassing position, velocity, and attitude. To enhance the accuracy of relative constraints, we introduce a new integrated IMU/Dynamics pre-integration model that combines a 2D vehicle dynamics model with a 3D kinematics model.

Positioning system is a key module of autonomous driving. As for LiDAR SLAM system, it faces great challenges in scenarios where there are repetitive and sparse features. Without loop closure or measurements from other sensors, odometry match errors or accumulated errors cannot be corrected. This paper proposes a construction method of LiDAR anchor constraints to improve the robustness of the SLAM system in the above challenging environment. We propose a robust anchor extraction method that adaptively extracts suitable cylindrical anchors in the environment, such as tree trunks, light poles, etc. Skewed tree trunks are detected by feature differences between laser lines. Boundary points on cylinders are removed to avoid misleading. After the appropriate anchors are detected, a factor graph-based anchor constraint construction method is designed. Where direct scans are made to anchor, direct constraints are constructed.

Optimization design of hard point parameters for hinge mechanism has been paid more attention in recent years, attributable to their significant improvement in dynamic performance. In this paper, the experimental analysis and dynamic optimization design of hinge mechanism is performed. The acceleration measurement experiments are carried out at different arrangement points and under different working conditions. Furthermore, the accuracy of established multi-body dynamics model is verified by three-axis accelerometer measurement experiment. In addition, sensitivity analysis for electric strut and gas strut coordinates is performed and shows that the Y coordinate of the lower end point of the electric strut is the design variable that has the greatest impact on the responses.

The displacement of the shaft head fails to be accurately measured while the three-axle heavy-duty truck is driving on the reinforced pavement. In order to obtain accurate fatigue load spectrum of the suspension bracket, the acceleration signals of the shaft heads of the suspension obtained by the reinforced pavement test measurement are virtually iterated as responses. A more accurate model of the rigid-flexible coupled multi-body dynamics (MBD) of the whole vehicle is established by introducing a flexible frame based on the comprehensive modal theory. Furthermore, the vertical displacements of the shaft heads are obtained by the reverse solution of the virtual iterative method with well-pleasing precision. The accuracy of the virtual iteration is verified by comparing the simulation results with the vertical acceleration of the shaft head under the reinforced pavement in the time domain and damage domain.

With the growing prosperity of the long-distance freight and urban logistics industry, the demand for cargo trucks is gradually increasing. The connecting bracket is the critical connecting part of the truck chassis, which bears the load transmitted by the road excitation and reduces the damage to the frame caused by the load. However, the occurrence of rough road conditions is inevitable in heavy-duty transportation. In this paper, road durability tests and fatigue life analysis are carried out on the original structure to ensure the safety of the vehicle. Based on the known boundary and load constraints, a lightweight and high-performance structure is obtained through size optimization, as the original structure cannot meet the performance requirements. Firstly, the road test was conducted on the truck where the original bracket structure is located.

In this paper, the principles, advantages and disadvantages of the main technology of variable strength design of automobile B-pillar Based on the finite element simulation technology, the local stress variable strength design effect of Automobile B-pillar structure is simulated, compared and evaluated. The simulation results show that with the same mechanical properties, the overall lightweight degree of B-pillar structure with variable strength design can be reduced by about 8.9%. With the expansion of the strengthening area of variable strength design of parts, the degree of lightweight of parts can be further improved. It can be seen that the local stress variable strength design method provides a new technical option for the lightweight design of automobile parts.

The scenario-based test method is now drawing more and more attention in the field of the test for autonomous vehicles. The predefined scenarios are used in the safety verification and performance evaluation of autonomous vehicles. However, the traditional generation method for predefined scenarios is parameterized and open-looped, which makes it challenging to generate diverse and complex scenarios. It is critical when testing high-level autonomous vehicles to verify their reliability in multiple behavior transitions. In this paper, a generation method for the continuous scenario is proposed to realize a function-driven iteration of scenarios for autonomous driving systems (ADS). The method consists of a functional model of ADS and a formal description of abstract scenario.

Durability performance prediction is a critical issue in fuel cell research. During the demonstration operation of fuel cell commercial vehicles in China, this issue has attracted more attention. In this article, the long short-term memory neural network (LSTMNN), which is an improved recurrent neural network (RNN), and the demonstration operation data are used to establish the prediction model to predict the durability performance of the fuel cell stack. Then, a model based on a back-propagation neural network (BPNN) is established to be a control group. The demonstration operation data is divided into training group and validation group. The former is used to train the prediction model, and the latter is used to verify the validity and accuracy of the prediction model. The outputs of the prediction model, as the durability performance evaluation indexes of the fuel cell, are the polarization curve (current-voltage curve) and the voltage decay curve (time-voltage curve).

As the key assembly of new energy vehicles, the noise and vibration, and harshness (NVH) performance of integrated electric drive system directly affects the driving quality of new energy vehicles. In this paper, the vibration noise characteristic test of 3in1 electric drive system is carried out in the semi-muffler chamber. In order to compare and analyze the difference between 2in1 and 3in1 electric drive system NVH performance, the power electronics unit (PEU) in the 3in1 system was removed and placed on the ground away from the platform, and vibration noise test was carried out. In order to analyze the difference of NVH performance between 2in1 status and 3in1 status, the PEU in the 3in1 system was removed and placed on the ground far away from the bench, and the NVH test was carried out. The microphone signal at 1m position and the vibration acceleration signal of the key structural surface of the system are measured experimentally.

Proton exchange membrane fuel cell (PEMFC) system is considered as one of the most popular power sources because of its high energy density, fast dynamic response and zero pollution. However, the start-up at low temperature (e.g. - 30 °C) is still a major challenge for its wide application due to water freezing in Membrane Electrode Assembly (MEA). In this paper, a cold start test process in an environment cabin with auxiliary heat was carried out for a full power automotive PEMFC system, including normal operation, shutdown purge and cold start processes analysis from -30°C. Rated power of this stack is 100kW at the current density of 1.4A/cm2 and relevant maximum output power can reach to 120kW. In order to reduce the damage of high potential to MEA, on-load purge with a current of 30A is conducted to removing extra water in stack for improving cold start ability. Based on corresponding control strategy, cold start was realized successfully within 110s.

With the explosion of in-vehicle data, Time Sensitive Network (TSN) is increasingly becoming the backbone of the in-vehicle network to ensure deterministic real-time communication and Quality of Service (QoS). However, legacy buses such as CAN FD and LIN will not disappear for a long time in the future. Many protocols are deployed in the gateway and it is an important component in the security and functional safety of the communication process. In this paper, the recommended Electrical/Electronic Architecture is first given and the use cases for the TSN/CAN FD gateway are illustrated. Then, a TSN/CAN FD routing mechanism is designed and security mechanisms are deployed. The routing mechanism includes the protocol conversion module, queue cache module, and forwarding scheduling module. The protocol conversion module unpacks or packs the TSN or CAN FD frames according to the routing table.

The transient impact load generated by door closing is used as the input of the closing condition, which is an important part of door system investigation. In this article, the basic theory of transfer path analysis (TPA) is introduced to handle the abnormal vibration of the front-left door with the glass down stall position of a certain vehicle during the closure. The transient impact loads are discretized under the closed door and obtained using the inverse matrix (IM) method in TPA. Vehicle test and bench test are conducted. The closed door is subjected to the transient impact loads of the sealing strip and the latch on the body side. In the vehicle test, acceleration sensors are pasted on the target point and the reference point on the door to obtain the acceleration vibration response upon the door closure.

To overcome some drawbacks of using UHSS (Ultra High Strength Steel) in vehicle weight reduction, like spot weld HAZ (Heat Affected Zone) softening, hard machining and brittleness, a new solution of ultra-high stress strengthening was proposed and applied to the ridgelines of thin-walled structures in this paper. Firstly, stress distribution characteristics, the laws of stress variation and the compressed plate buckling process of the rectangular thin-walled beam under compressive and bending load were analyzed in elastic plastic stage by theory and Finite Element (FE) simulation. Secondly, based on elastic plastic buckling theory of the compressed plate and stress distribution similarity of the buckling process of the thin-walled box structure, three factors influencing the ultimate resistance enhancement of thin-walled hat section beam were found, and the rationality and accuracy of cross section ultimate resistance prediction formulas were also verified by FE simulation.

The main challenge for future autonomous vehicles is to identify their location and body pose in real time during driving, that is, “where am I? and how will I go?”. We address the problems of pose estimation and scene perception from continuous visual frames in intelligent vehicle. Recent advanced technology in the domain of deep learning proposes to train some learning models for vehicle’s series detection tasks in a supervised or unsupervised manner, which has numerous advances over traditional approaches, mainly reflected in the absence of manual calibration and synchronization of the camera and IMU. In the paper, we propose a novel approach for pose estimation and scene recognition with a deep fusion of multi-modal neural features in the manner of unsupervised. Firstly, low-cost camera and IMU are used to extract original visual and inertial data, then the visual and inertial encoders are utilized to encoder the feature of the two modes.

The accuracy of parking slot detection is a challenge for the safety of the Automated Valet Parking (AVP), while traditional methods of range sensor-based parking slot detection have mostly focused on the detection rate in a scenario, where the ego-vehicle must pass by the slot. This paper uses three-dimensional Light Detection And Ranging (3D LiDAR) to efficiently search parking slots around without passing by them and highlights the accuracy of detecting and tracking. For this purpose, a universal process of 3D LiDAR-based high-accuracy slot perception is proposed in this paper. First, the method Minimum Spanning Tree (MST) is applied to sort obstacles, and Separating Axis Theorem (SAT) are applied to the bounding boxes of obstacles in the bird’s-eye view, to find a free space between two adjacent obstacles. These bounding boxes are obtained by using common point cloud processing methods.

This paper presents a theoretical study of a finite hybrid piezoelectric phononic crystal (PC) beam with shunting circuits. The vibration transmissibility method (TM) is developed for the finite system. The uniform and non-uniform configurations of the resonators, piezoelectric patches and shunting circuits are respectively considered. The properties of the vibration attenuation of the hybrid PC beam undergoing bending vibration are investigated and quantified. It is shown that the proper relaxation of the periodicity of the PC is conducive to forming a broad vibration attenuation region. The hybrid piezoelectric PC combines the purely mechanical PC with the piezoelectric PC and provides more tunable mechanisms for the target band-gap. Taking the structural and circuit parameters into account, the design of experiments (DOE) method and the multi-objective genetic optimization method are employed to improve the vibration attenuation and meet the lightweight demand of the attachments.

There are a large number of uncertainties in engineering design, and the accumulated uncertainties will enlarge the overall failure probability of the structure system. Therefore, structural design considering uncertainties has good guiding significance for improving the reliability of engineering structures. To address this issue, the dynamic-static structural topology optimization is established and reliability-based topology optimization with decoupling format is conducted in this study. The design point which satisfying the constraint of the target reliability indicator is obtained according to the reliability indicators of the first-order reliability method, and the uncertain design variables are modified into a deterministic variable according to the sensitivity information.

Highly accurate parking slot detection methods are crucial for Automated Valet Parking (AVP) systems, to meet their demanding safety and functional requirements. While previous efforts have mostly focused on the algorithms’ capabilities to detect different types of slots under varying conditions, i.e. the detection rate, their accuracy has received little attention at this time. This paper highlights the importance of trustworthy slot detection methods, which address both the detection rate and the detection accuracy. To achieve this goal, an accurate slot detection method and a reliable ground-truth slot measurement method have been proposed in this paper. First, based on a 2D laser range finder, datapoints of obstacle vehicles on both sides of a slot have been collected and preprocessed. Second, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm has been improved to efficiently cluster these unevenly-distributed datapoints.