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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.

In order to explore approaches for improving object detection accuracy in intelligent vehicle system, we exploit super-resolution techniques. A novel method is proposed to confirm the conjecture whether some popular super-resolution networks used for environmental perception of intelligent vehicles and robots can indeed improve the detection accuracy. COCO dataset which contains images from complex ordinary environment is utilized for the verification experiment, due to it can adequately verify the generalization of each algorithm and the consistency of experimental results. Using two representative object detection networks to produce the detection results, namely Faster R-CNN and YOLOv3, we devise to reduce the impact of resizing operation. The two networks allow us to compare the performance of object detection between using original and super-resolved images. We quantify the effect of each super-resolution techniques as well.

The action of load on the component is crucial to evaluate the performance of durability. Another factor that affects fatigue life is the boundary conditions of the test specimen being tested by introducing unrealistic loads on the component of interest. The physical test is widely conducted in the laboratory. The fixture provides additional constraints on the test specimen as well as reaction forces to balance the test system [1]. The characteristics of the fixture involved in the test is important to analyze and assess the test results [2]. The impact of the reaction force of the fixture on the spindle-coupled axle road simulation test is presented in this article. A simplified 7-DoF (degrees of freedom) model is introduced to demonstrate the dynamic behavior of the vehicle. The influence on the internal load by the fixture has been analyzed. Followed by a more detailed MBS (multibodysystem) model to give a thorough understanding of the phenomenon.

This paper constructs the Accident Crash Scenarios(ACSs) classification system based on the traffic accident data collected by the traffic management department in a Chinses city from 2013 to 2015. The classification system selects four influenced variables on the basis of Critical Driving Scenarios(CDSs) in Naturalistic Driving Data. The proportions of each variable are analyzed, and all ACSs are divided into 48 scenarios. The highest proportion of nine ACSs are extracted from all 10596 ACSs, and the result shows the ACSs involved Car-Pedalcyclist occupy the top four scenarios, and the scenarios involved intersection situations are worth attention. Pedalcyclists include bicyclists, motorcyclists, tri-cyclists and electric bicyclists. Multivariate Logistic Regression(MLR) analysis is then used to study the ACSs involved the type of Car-Pedalcyclist.

With the development of autonomous driving, the penetration rate of autonomous vehicles on the road will continue to grow. As a result, the social cooperation ability of autonomous vehicles will have a great effect on the social acceptance of autonomous driving, which can be described as harmony with traffic. In order to research the evaluation method of the harmony with traffic, this paper proposes a subjective and objective mapping evaluation method based on the Mean Impact Value and Backpropagation (MIV-BP) Neural Network, with the merging vehicle on the expressway ramp as the research object. Firstly, by taking 16 original objective indexes obtained by theoretical analysis and the subjective evaluation results as input and output, respectively, the BP Neural Network model is constructed as a baseline model. Secondly, nine selected objective indexes are selected by the MIV method based on the baseline model.

The accelerated global progress in the research and development of automobile products, and the use of new technologies, such as the Internet, cloud computing and big data, to coordinate development platforms in different regions and fields, can reduce the duration and cost of development and testing. Specifically, in the context of the current coronavirus disease (COVID-19) pandemic, which has caused great obstacles to normal logistics and transportation, personnel exchanges and information communication, platforms that can support global operation are significant for product testing and validation, because they eliminate the need for the transportation of personnel and equipment. Therefore, the establishment of a distributed test and validation platform for automotive powertrain systems, which can integrate software and hardware testing, is important in terms of both scientific research and industrialization.

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).

Hydrous ethanol not only has the advantages of high-octane number and valuable oxygen content, but also reduce the energy consumption in the production process. However, little literature investigated the internal flow and cavitation of hydrous ethanol-gasoline fuels in the multi-hole direct injector. In this simulation, a two-phase fuel flow model in injector is established based on the multi-fluid model of Euler-Euler method, and the accuracy of model is verified. On the basis of this model, the flow of different hydrous ethanol-gasoline blends is calculated under different injection conditions, and the cavitation, flow rate, and velocity at the outlet of the nozzle are predicted. Meanwhile, the influence of temperature and back pressure on the flow is also analyzed. The results show that the use of hydrous ethanol reduces the flow rate, compared with the velocity of E0, that of E10w, E20w, E50w, E85w, and E100w decreases by 10%, 12.9%, 17.6%, 20%, and 23.5%, respectively.

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 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.

To overcome the shortcoming that vehicles with multiple steering modes need to switch steering modes at parking or very low speeds, a dynamic switch method of steering modes based on MOEA/D (Multi-objective Evolutionary Algorithm Based on Decomposition) was proposed for 4WID-4WIS (Four Wheel Independent Drive-Four Wheel Independent Steering) electric vehicle, considering the smoothness of dynamic switch, the lateral stability of the vehicle and the energy economy of tires. First of all, the vehicle model of 4WID-4WIS was established, and steering modes were introduced and analyzed. Secondly, the conditions for the dynamic switch of steering modes were designed with the goal of stability and safety. According to different constraints, the control strategy was formulated to obtain the target angle of the active wheels. Then aiming at the smoothness of the dynamic switch, the active wheel angle trajectory was constructed based on the B-spline theory.

The foundation of both advanced driving assistance system(ADAS) and automated driving (AD) is an accurate environment perception system(EPS). However, evaluation and test method of EPS are seldom studied. In this paper, naturalistic driving environment was studied and test scenarios for EPS under rear-end collision risk were proposed accordingly. To describe driving environment, a new concept named environment perception element(EPE) was first proposed in this paper, which refers to all the objects that the EPS must perceive during driving. Typical environment perception elements include weather and light conditions, road features, road markings, traffic signs, traffic lights, other vehicles, pedal cyclists and pedestrians and others. Driving behaviors collected in Shanghai, China were classified and rear-end collision risk scenarios were obtained and described using EPEs. Probability distribution of EPEs was therefore obtained.

On account of environmental friendliness, high energy conversion efficiency and high power density, the proton exchange membrane fuel cell (PEMFC) has been used for automotive application for years. However, its durability in powertrain system is one of technical challenges, which restricts the large-scale commercialization of fuel cell electric vehicles (FCEV). In addition to the complex aging mechanism of PEMFC, the durability and energy relationship of key components in powertrain system, including battery and DC/DC converter, have a crucial impact on the vehicle performance, which have not been thoroughly analyzed. Nowadays, most researchers have explored the causes of components degradation from models or experiments and tried to carry out the life expectancy. Nevertheless, it is in need of system-level researches on durability against the actual automotive application.

In order to satisfy design requirements of Lane Keeping Assistance System (LKAS), a Driver Steering Override (DSO) strategy is necessary for driver’s interaction with the assistance system. The assistance system can be overridden by the strategy in case of lane change, obstacle avoidance and other emergency situations. However, evaluation and optimization of the DSO strategy for LKAS cannot easily be completed quantitatively considering driver’s acceptability. In this research, firstly subjective and objective evaluation experiment is designed. Secondly, correlations between the subjective and the objective evaluation results are established by using regression analysis. Finally, based on the correlations established previously, the optimal performance of DSO strategy is obtained by setting the desired comprehensive evaluation ratings as the optimized goal.

Lane-keeping assist system (LKA) alerts the driver or intervenes in the driving when the vehicle deviates from the lane. But its effect is highly dependent on the driver’s acceptance. Distance to Lane Crossing (DTLC) and Time to Lane Crossing (TTLC) are two important factors to consider the danger level of the scenario, which are also two references for drivers to make decisions. At present, most of the functional design standards are based on these values, while they often differ for different vehicle movements. This study uses a driving robot to precisely control the test conditions and performs field tests on two advanced autonomous vehicles in National Intelligent Connected Vehicle (Shanghai) Pilot Zone. The test conditions are extended based on various test standards and the LKA performance of vehicles in the pre-experiment.

Estimating the motion state of peripheral targets is a very important part in the environment perception of intelligent vehicles. The accurate estimation of the motion state of the peripheral targets can provide more information for the intelligent vehicle planning module which means the intelligent vehicle is able to anticipate hazards ahead of time. To get the motion state of the target accurately, the target’s range, velocity, orientation angle and yaw rate need to be estimated. Three-dimensional high-resolution automotive millimeter wave radar can measure radial range, radial velocity, azimuth angle and elevation angle about multiple reflections of an extended target. Thus, the three-dimensional range information and three-dimensional velocity information can be obtained. With multiple three-dimensional high-resolution automotive millimeter-wave radar, it is possible to measure information in various directions of a target.

A rapid path-planning algorithm that generates drivable paths for an autonomous vehicle operating in structural road is proposed in this paper. Cubic B-spline curve is adopted to generating smooth path for continuous curvature and, more, parametric basic points of the spline is adjusted to controlling the curvature extremum for kinematic constraints on vehicle. Other than previous approaches such as inverse kinematics, model-based prediction postprocess approach or closed-loop forward simulation, using the kinematics model in each iteration of path for smoothing and controlling curvature leading to time consumption increasing, our method characterized the vehicle curvature constraint by the minimum length of segment line, which synchronously realized constraint and smooth for generating path. And Differ from the path of robot escaping from a maze, the intelligent vehicle traveling on road in structured environments needs to meet the traffic rules.

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.

Based on the durability cycle test of fuel cell stack and the characteristics of cyclic working conditions, this paper defines the characteristic current point and studies the attenuation rule of the fuel cell stack voltage over time under the characteristic current point. The results show that the voltage of the fuel cell stack appears to be linear downward under the characteristic current point. and the voltage attenuation rate of the fuel cell stack increases quadratically with the increase of the current density in addition to the open-circuit voltage point. Then the coefficient of variation is introduced in statistics as the index to characterize the voltage consistency attenuation of the fuel cell stack, and its variation rule is explored. The results show that the voltage consistency of vehicle fuel cell stack decreases seriously with the increase of running time under the condition of durable cycling.

A series connection of the KVBC (Kelvin-Voigt and Bouc-wen) theoretical model of rubber bush in automobile suspension is established. The numerical calculation model is also developed through Matlab/simulation and 9 parameters are identified. Experiments are conducted on the rubber bush on a bench for dynamic and static characteristics and to supply appropriate and reliable data for parameter identification. Based on this, preload and temperature are taken into consideration in an ordinary KVBC model as two important additional factors. As a result, it leads to developing a novel model with new parameter identification, which is validated under different conditions. This new modeling method of rubber bush has three advantages. First, it shows improved accuracy for solving non-linear problems in a multi-body calculation, which is useful for researchers and vehicle engineers.