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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.

Wheel Aerodynamics is an important part of vehicle aerodynamics. The wheels can notably influence the total aerodynamic drag, lift and ventilation drag of vehicles. In order to simulate the real on-road condition of driving cars, the moving ground and wheel rotation is of major importance in CFD. However, the wheel rotation condition is difficult to be represented exactly, so this is still a critical topic which needs to be worked on. In this paper, a study, which focuses on two types of cars: a fastback sedan and a notchback DrivAer, is conducted. Comparing three different wheel rotating simulation methods: steady Moving wall, MRF and unsteady Sliding Mesh, the effects of different methods for the numerical simulation of vehicle aerodynamics are revealed. Discrepancies of aerodynamic forces between the methods are discussed as well as the flow field, and the simulation results are also compared with published experimental data for validation.

In this work, the influences of various real-timely available energy management strategies on vehicle fuel consumption (VFC) and energy flow of a range-extender electric vehicle were studied The strategies include single-point, multi-point, speed-following, and equivalent consumption minimization strategy. In addition, the dynamic programming method which cannot be used in real time, but can provide the optimal solution for a known drive situation was used for comparison. VFCs and energy flow characteristics with different strategies under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) were obtained through computer modeling, and the results were verified experimentally on a range-extender test bench. The experimental results are consistent with the modeled ones in general with a maximum deviation of 4.11%, which verifies the accuracy of the simulation models.

“LIN-BOX” is designed as a development tool for simulation, implementation and test of the automotive LIN (Local Interconnect Network) control devices or entire network. The tool can be used to simulate master and/or slaves around LIN system. The configurable signal processing makes it possible to simulate and test the communication behavior. LIN-BOX monitors the bus traffic in the vehicle. The data on LIN bus can not only be shown on various windows but also written into log files. LIN-BOX has been used by several cases for debugging and validation, the result shows that it is a powerful tool for LIN cluster design, simulation and test.

The research and development of data-driven highly automated driving system components such as trajectory prediction, motion planning, driving test scenario generation, and safety validation all require large amounts of naturalistic vehicle trajectory data. Therefore, a variety of data collection methods have emerged to meet the growing demand. Among these, camera-equipped drones are gaining more and more attention because of their obvious advantages. Specifically, compared to others, drones have a wider field of bird's eye view, which is less likely to be blocked, and they could collect more complete and natural vehicle trajectory data. Besides, they are not easily observed by traffic participants and ensure that the human driver behavior data collected is realistic and natural. In this paper, we present a complete vehicle trajectory data extraction framework based on aerial videos. It consists of three parts: 1) objects detection, 2) data association, and 3) data cleaning.

Recently, several datasets have become available for occupant monitoring algorithm development, including real and synthetic datasets. However, real data acquisition is expensive and labeling is complex, while virtual data may not accurately reflect actual human physiology. To address these issues and obtain high-fidelity data for training intelligent driving monitoring systems, we have constructed a hybrid dataset that combines real driving image data with corresponding virtual data generated from 3D driving scenarios. We have also taken into account individual anthropometric measures and driving postures. Our approach not only greatly enriches the dataset by using virtual data to augment the sample size, but it also saves the need for extensive annotation efforts. Besides, we can enhance the authenticity of the virtual data by applying ergonomics techniques based on RAMSIS, which is crucial in dataset construction.

During current design process of vehicle engine exhaust system, the frequently-used approach mainly concerns an individual component, which usually results in not meeting the overall design requirements or unreasonable design parameters. Here a concept of comprehensive evaluation metrics for vehicle engine exhaust system was established, of which a new weight factor assignment method was proposed, named change rate method, as the core of evaluation system to be especially studied. Taking muffler as an instance, six weight factor assignment schemes were adopted to compare with each other. And the rationality and practicability of the change rate assignment method was verified by the muffler noise experiments. The results show that, the change rate method makes the weight assignment more scientific and rigorous. And the new method can reflect the wishes of designers and completely displays the performance comparison and evaluation between schemes.

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.

With the popularity of automated vehicles, the future mixed traffic flow contains automated vehicles with different degrees of intelligence developed by other manufacturers. Therefore, simulating the interaction behavior of automated vehicles with varying levels of intelligence is crucial for testing and evaluating autonomous driving systems. Since the algorithm of traffic vehicles with various intelligence levels is difficult to obtain, it leads to hardships in quantitatively characterizing their interaction behaviors. Therefore, this paper designs a new automated vehicle test platform to solve the problem. The intelligent vehicle testbed with multiple personalized in-vehicle control units in the loop consists of three parts: 1. Multiple controllers in the loop to simulate the behavior of traffic vehicles；2. The central console applies digital twin technology to share the same traffic scenario between the tested vehicle and the traffic vehicle, creating a mixed traffic flow. 3.

As one of the key technologies in autonomous driving, the lateral planning module guides the lateral movement during the driving process. An integrated lateral planning module should consider the non-holonomic constraints of a vehicle, the optimization of the generated trajectory and the applicability to various scenarios. However, the current lateral planning methods can only meet parts of these requirements. In order to satisfy all the performance requirements above, a novel Potential Field (PF) based lateral planning method is proposed in this paper. Firstly, a PF model is built to describe the potential risk of the traffic entities, including the obstacles, road boundaries and lines. The potential fields of these traffic entities are determined by their properties and the traffic regulations. Secondly, the planning algorithm is presented, which comprises three modules: state prediction, state search and trajectory generation.

Parallel-connected modules have been widely used in battery packs for electric vehicles nowadays. Unlike series-connected modules, the direct state inconsistency caused by parameter inconsistency in parallel modules is current and temperature non-uniformity, thus resulting in the inconsistency in the speed of aging among cells. Consequently, the evolution pattern of parameter inconsistency is different from that of series-connected modules. Since it’s practically impossible to monitor each cell’s current and temperature information in battery packs, considering cost and energy efficiency, it’s necessary to study how the parameter inconsistency evolves in parallel modules considering the initial parameter distribution, topology design and working condition. In this study, we assigned cells of 18650 format into several groups regarding the degree of capacity and resistance inconsistency. Then all groups are cycled under different environmental temperature and current profile.

To solve the problem of serious roller wear and improve the smoothness of the sliding door motion process, the rigid-flexible coupling multi-body model of the vehicle sliding door was built in ADAMS. Force boundary conditions of the model were determined to meet the speed requirement of monitoring point and time requirement of door opening-closing process according to the bench test specification. The results of dynamic simulation agreed well with that of test so the practicability and credibility of the model was verified. In the optimization of the ride comfort of the sliding door, two different schemes were proposed. The one was to optimize the position of hinge pivots and the other was to optimize the structural parameters of the middle guide. The impact load of lead roller on middle guide, the curvature of the motion trajectory and angular acceleration of the sliding door centroid were taken as optimization objectives.

To promote the development of automated vehicles (AVs), large scale of field operational tests (FOTs) were carried out around the world. Applications of naturalistic driving data should base on correlative scenarios. However, most of the existing scenario typologies, aiming at advanced driving assistance system (ADAS) and extracting discontinuous fragments from driving process, are not suitable for AVs, which need to complete continuous driving tasks. In this paper, a systematic scenario-typology consisting of four layers (from top to bottom: trip, cluster, segment and process) was first proposed. A trip refers to the whole duration from starting at initial parking space to parking at final one. The basic units ‘Process’, during which the vehicle fulfils only one driving task, are classified into parking process, long-, middle- and short-time-driving-processes. A segment consists of two neighboring long-time-driving processes and a middle or/and short one between them.

The optimization of speed holds critical significance for pure electric vehicles. In multi-intersection scenarios, the determination of terminal velocity plays a crucial role in addressing the complexities of the speed optimization problem. However, prevailing methodologies documented in the literature predominantly adhere to a fixed speed constraint derived from traffic light regulations, serving as the primary basis for the terminal velocity constraint. Nevertheless, this strategy can result in unnecessary acceleration and deceleration maneuvers, consequently leading to an undesirable escalation in energy consumption. To mitigate these issues and attain an optimal terminal velocity, this paper proposes an innovative speed optimization method that incorporates a terminal-velocity heuristic. Firstly, a traffic light state model is established to determine the speed range required to avoid coming to a stop at signalized intersections.

Gesture control has been increasingly applied to automotive industry to reduce the distraction caused by in-vehicle interactions to the primary task of driving. The aim of this study is to find out if gestures can reasonably be used to control in-car devices. Since there exists a big cultural difference of gesture between different countries because of its particularity, a set of gestures which support intuitive human-machine interaction in an automotive environment is searched. The results show a gesture dictionary for a variety of on-board functions, which conforms to Chinese drivers’ driving habits. Furthermore, this paper also describes a driving simulator test to evaluate the usability of gesture from different aspects including the effectiveness, efficiency, satisfaction, memorability and security. Static driving simulator is considered as an excellent environment for the in-car secondary task as its high safety level, repeatability and reliability.

An accurate finite element (FE) model is the basis for the numerical prediction of vibration and noise of permanent magnet synchronous motors (PMSMs). This paper provides an equivalent modeling method of PMSMs considering the orthotropic material parameters of the stator system. First, a theoretical analysis of the influence of orthotropic material parameters on modal characteristics is implemented. Subsequently, the influence of orthotropic material parameters on the modal frequency of the stator is analyzed through the FE method. Then, the modal parameters of the stator core and the stator assembly are obtained by modal tests. According to the equivalent FE model and modal parameters, the orthotropic material parameters of the stator system are acquired. Moreover, to save the calculation time and simplify the modal identification process, the influence of windings is taken into account through additional mass and additional stiffness during the modeling process.

Tire cavity noise of pure electric vehicles is particularly prominent due to the absence of engine noise, which are usually eliminated by adding Helmholtz resonators with arbitrary transversal section to the wheel rims. This paper provides theoretical basis for accurately predicting and effectively improving acoustic performance of wheel resonators. A hybrid finite element method is developed to extract the transversal wavenumbers and eigenvectors, and the mode-matching scheme is employed to determine the transmission loss of the Helmholtz resonator. Based on the accuracy validation of this method, the matching design of the wheel resonators and the optimization method of tire cavity noise are studied. The identification method of the tire cavity resonance frequency is developed through the acoustic modal test. A scientific transmission loss target curve and fitness function are defined according to the noise characteristics.

The backlash between engaging components in a driveline is unavoidable, especially when the gear runs freely and collides with the backlash, the impact torque generated increases the vibration amplitude. The power-split hybrid electric vehicle generates output torque only from the traction motor during the launching process. The nonlinear backlash can greatly influence the driveability of the driveline due to the rapid response of the traction motor and the lack of the traditional clutches and torsional shock absorbers in the powertrain. This paper focuses on the launch vibration of the power-split hybrid electric vehicle, establishes a nonlinear driveline model considering gear backlash, including an engine, two motors, a Ravigneaux planetary gear set, a reducer, a differential, a backlash assembly, half shafts, and wheels.

BBW (Brake-by-wire) can increase the electric and hybrid vehicles performance and safety. This paper proposes a novel mechatronic booster system, which includes APS (active power source), PFE (pedal feel emulator), ECU (electronic control unit). The system is easily disturbed when the system parameters and the outside conditions change. The system performance is weakened. The cascade control technique can be used to solve the problem. This paper develops an adaptive cascade optimum control (ACOC) algorithm based on the novel mechatronic booster system. The system is divided into main loop and servo loop, both of them are closed-loop system. The servo-loop system can eliminate the disturbance which exists in the servo loop. So the robustness of the cascade control system is improved than which of the general closed-loop control system. Different control object is respectively chosen. The control-oriented mathematical model is designed.

The adhesion control is the basic technology of active safety for the four-wheel driven EV. In this paper, a novel adhesion control method based on fuzzy logic control is proposed. The control system can maximize the adhesion force without road condition information and vehicle speed signal. Also, the regulation torque to prevent wheel slip is smooth and the vehicle driving comfort is greatly improved. For implementation, only the rotating speed of the driving wheel and the motor driving torque signals are needed, while the derived information of the wheel acceleration and the skid status are used. The simulation and road test results have shown that the adhesion control method is effective for preventing slip and lock on the slippery road condition.