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In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.

This paper proposes a new method of predicting the sound absorption performance of polymer wool using artificial neural networks (ANN) model. Some important parameters of the proposed model have been adjusted to best fit the non-linear relationship between the input data and output data. What's more, the commonly used multiple non-linear regression model is built to compare with ANN model in this study. Measurements of the sound absorption coefficient of polymer wool based on transfer function method are also performed to determine the sound absorption performance according to GB/T18696. 2-2002 and ISO10534- 2: 1998 (E) standards. It is founded that predictions of the new model are in good agreement with the experiment results.

To provide a feasible transitional solution from all-by-human driving style to fully autonomous driving style, this paper proposed concept and its control algorithm of a robust lane-keeping ‘co-pilot’ system. In this a semi-autonomous system, Learning based Model Predictive Control (LBMPC) theory is employed to improve system's performance in target state tracking accuracy and controller's robustness. Firstly, an approximate LTI model which describes driver-vehicle-road closed-loop system is set up and real system's deviations from the LTI system resulted by uncertainties in the model are regarded as bounded disturbance. The LTI model and bounded disturbances make up a nominal model. Secondly, a time-varying model which is composed of LTI model and an ‘oracle’ component is designed to observe the possible disturbances numerically and it is online updated using Extended Kalman Filter (EKF).

In the face of energy and environmental problems, how to improve the economy of fuel cell vehicles (FCV) effectively and develop intelligent algorithms with higher hydrogen-saving potential are the focus and difficulties of current research. Based on the Toyota Mirai FCV, this paper focuses on the short-term speed prediction algorithm based on the back propagation neural network (BP-NN) and carries out the research on the short-term speed prediction algorithm based on BP-NN. The definition of NN and the basic structure of the neural model are introduced briefly, and the training process of BP-NN is expounded in detail through formula derivation. On this basis, the speed prediction model based on BP-NN is proposed. After that, the parameters of the vehicle speed prediction model, the characteristic parameters of the working condition, and the input and output neurons are selected to determine the topology of the vehicle speed prediction model.

Vehicle Longitudinal Control (VLC) algorithm is the basis function of automotive Cruise Control system. The main task of VLC is to achieve a longitudinal acceleration tracking controller, performance requirements of which include fast response and high tracking accuracy. At present, many control methods are used to implement vehicle longitudinal control. However, the existing methods are need to be improved because these methods need a high accurate vehicle dynamic model or a number of experiments to calibrate the parameters of controller, which are time consuming and costly. To overcome the difficulties of controller parameters calibration and accurate vehicle dynamic modeling, a vehicle longitudinal control algorithm based on iterative learning control (ILC) is proposed in this paper. The algorithm works based on the information of input and output of the system, so the method does not require a vehicle dynamics model.

In this paper, a computer model of a multi-purpose vehicle (MPV) is built to study vehicle ride comfort by multi-body system dynamic theory. Virtual test rigs are developed to perform natural body frequency tests and random road input tests on the complete vehicle multi-body dynamic model. By comparing simulation results with field test results, the accuracy of the model is validated and the feasibility of virtual test rigs is established.

Steer-by-wire System is a new conception for steering system, which eliminates those mechanical linkages between hand steering wheel and front wheels, and communicates among the driver and wheels by signals and controllers. All these facilities improve the safety and conformability of the vehicle system and get rid of the mechanical constricts. This paper proposed three vehicle stability control strategies, including front wheel control, yaw rate feedback control and yaw rate& acceleration feedback control. We compared these three control methods by simulation and simulator tests. We also studied the integrated control algorithm of Steer-by-Wire System and 4WS, and compared with 2WS for SBW and the classical 4WS.

In China there is a strong trend in the application of vehicles equipped with automatic transmissions in considering the complexity of traffic and the convenience of automatic transmissions. As a type of automatic transmission, automated mechanical transmission (AMT) shows great potential to be developed as a main transmission because of its simple structures, easy upgrade from manual transmission (MT) and low price. Support Vector Machine (SVM) is a new statistic method which could make a good prediction with limited training instances. Compared with Artificial Neutral Network (ANN), SVM can provide better genetic ability. In order to verify the ability of the new method, the model trained by one set of AMT car data was applied on some other AMT vehicles, and the predicted results were compared with subjective rating results by expert drivers and analyzed to identify the potential of this new assessment system.

Vehicle lane keeping system is becoming a new research focus of drive assistant system except adaptive cruise control system. As we all known, vehicle lateral dynamics show strong nonlinear and time-varying with the variety of longitudinal velocity, especially tire’s mechanics characteristic will change from linear characteristic under low speed to strong nonlinear under high speed. For this reason, the traditional PID controller and even self-tuning PID controller, which need to know a precise vehicle lateral dynamics model to adjust the control parameter, are too difficult to get enough accuracy and the ideal control quality. Based on neural network’s ability of self-learning, adaptive and approximate to any nonlinear function, an adaptive PID control algorithm with BP neural network self-tuning online was proposed for vehicle lane keeping.

Coordinated EGR-VNT control based on the intake air mass observer is presented in this paper to deal with the transient AFR control of turbocharged diesel engine. The air mass model embedded in the observer is a Takagi-Sugeno fuzzy neural network trained with transient simulation results. It can predict the charged fresh air mass entering the cylinder. In a high load region, when EGR is not effective, the coordinated EGR-VNT control will also bring benefits to the transient air-fuel-ratio control. The simulation results of TDI engine model verify that the transient control strategy will allow a better control of the intake air mass, and thus improve air-fuel-ratio control and reduce NOx emission in transients.

In this work, a XD132 Road Roller from XCMG in China was employed as a research basis to study the heat exchange performance of the heat dissipation module under varied working conditions. The module in the XD132 consists of a cooling fan and three radiators. At first, the numerical investigation on the elementary units of radiators was performed to obtain Colburn j factor and Fanning friction f factor, which were used for the ε-NTU method to predict the radiator performance. The fan was numerically tested in a wind test tunnel to acquire the performance curve. The performance data from both investigations were transformed into the boundary conditions of the numerical vehicle model in a virtual tunnel. A field experiment was carried out to validate the simulation accuracy, and an entrance coefficient was proposed to discuss the performance regularity under four working conditions.

As a typical parameter of the road-vehicle interface, the road friction potential acts an important factor that governs the vehicle motion states under certain maneuvering input, which makes the prior knowledge of maximum road friction capacity crucial to the vehicle stability control systems. Since the direct measure of the road friction potential is expensive for vehicle active safety system, the evaluation of this variable by cost effective method is becoming a hot issue all these years. A ‘wheel slip based’ maximum road friction coefficient estimation method based on a modified Dugoff tire model for distributed drive electric vehicles is proposed in this paper. It aims to evaluate the road friction potential with vehicle and wheel dynamics analyzing by using standard sensors equipped on production vehicle, and fully take the advantage of distributed EV that the wheel drive torque and rolling speed can be obtained accurately.

At the collision moment, a driver’s lower extremity will be in different foot position, which leads to the different posture of the lower extremity with various muscle activations. These will affect the driver’s injury during collision, so it is necessary to investigate further. A simulated collision scene was constructed, and 20 participants (10 male and 10 female) were recruited for the test in a driving simulator. The braking posture and muscle activation of eight major muscles of driver’s lower extremity (both legs) were measured. The muscle activations in different postures were then analyzed. At the collision moment, the right leg was possible to be on the brake (male, 40%; female, 45%), in the air (male, 27.5%; female, 37.5%) or even on the accelerator (male, 25%; female, 12.5%). The left leg was on the floor all along.

Intelligent driving, aimed for collision avoidance and self-navigation, is mainly based on environmental sensing via radar, lidar and/or camera. While each of the sensors has its own unique pros and cons, camera is especially good at object detection, recognition and tracking. However, unpredictable environmental illumination can potentially cause misdetection or false detection. To investigate the influence of illumination conditions on detection algorithms, we reproduced various illumination intensities in a photo-realistic virtual world, which leverages recent progress in computer graphics, and verified vehicle detection effect there. In the virtual world, the environmental illumination is controlled precisely from low to high to simulate different illumination conditions in the driving scenarios (with relative luminous intensity from 0.01 to 400). Sedan cars with different colors are modelled in the virtual world and used for detection task.

Simulation has been considered as one of the key enablers on the development and testing for autonomous driving systems as in-vehicle and field testing can be very time-consuming, costly and often impossible due to safety concerns. Accurately modeling traffic, therefore, is critically important for autonomous driving simulation on threat assessment, trajectory planning, etc. Traditionally when modeling traffic, the motion of traffic vehicles is often considered to be deterministic and modeled based on its governing physics. However, the sensed or perceived motion of traffic vehicles can be full of errors or inaccuracy due to the inaccurate and/or incomplete sensing information. In addition, it is naturally true that any future trajectories are unknown. This paper proposes a novel modeling method on traffic considering its motion uncertainties, based on Gaussian process (GP).

This paper introduces the characteristics of the 4 wheel independent driving/4 wheel independent steering (4WID/4WIS) electric vehicle (EV). Models of Subsystems and the vehicle are constructed based on Matlab/simulink. The vehicle model allows the inputs of different drive torques and steer angles of four wheels. The dynamic characteristics of drive motors and steer motors are considered, and also it can reflect the vehicle longitudinal dynamics change due to the increase of the mass and inertia of the four wheels. Besides, drive mode selection function that is unique to this type vehicle is involved. Simulations and analyses of crab, oblique driving and zero radius turning which are the special conditions of 4WID/4WIS EV are conducted. The results show that the model can reflect the dynamic response characteristics. The model can be used to the simulation analyses of handling, stability, energy saving and control strategies verification of 4WID/4WIS EVs.

Performance testing and evaluation always plays an important role in the developmental process of a vehicle, which also applies to autonomous vehicles. The complex nature of an autonomous vehicle from architecture to functionality demands even more quality-and-quantity controlled testing and evaluation than ever before. Most of the existing testing methodologies are task-or-scenario based and can only support single or partial functional testing. These approaches may be helpful at the initial stage of autonomous vehicle development. However, as the integrated autonomous system gets mature, these approaches fall short of supporting comprehensive performance evaluation. This paper proposes a novel hierarchical and systematic testing and evaluation approach to bridge the above-mentioned gap.

As one of advanced driver assistance systems (ADAS), automatic parking system has great market prospect and application value. In this paper, based on an intelligent vehicle platform, an automatic parking system is designed by using multi-point preview theory. The vehicle kinematics model was established, based on Ackermann steering principle. By analyzing working conditions of parallel parking, complex constraint condition of parking trajectory is established and reference trajectory based on sine wave is proposed. In addition, combined with multi-point preview theory, the design of trajectory following controller for automatic parking is completed. The cost function is designed, which consider the trajectory following effect and the degree of easy handling. The optimization of trajectory following control is completed by using the cost function.

With the popularity of electrification and driver assistance systems on vehicle dynamics and controls, the steering performance of the vehicle put forward higher requirements. Thus, the steer-by-wire technology is becoming particularly important. Through specific control algorithm, the steer-by-wire system electronic control unit can receive signals from other sensors on the vehicle, realize the personalized vehicle dynamics control on the basis of understanding the driver’s intention, and grasp the vehicle movement state. At the same time, to make these driver assistance systems better cooperate with human drivers, reduce system frequent false warning, full consideration of mutual adaptation for the systems and the driver’s characteristics is critical. This paper focuses on the steering performance of steer-by-wire vehicle. Feature parameters are obtained from the virtual turning experiment designed on the driving simulator experimental platform.

Prototype of a brake-by-wire (BBW) system named Distributed Electro-hydraulic Braking System (DEHB) has been developed. As a BBW system, DEHB is suitable to be used in electric vehicles (EV) and hybrid electric vehicles (HEV). Comparing to the ‘dry’ type distributed BBW systems such as Electro-mechanical Braking System (EMB) or Electric Wedge Brake (EWB), the ‘wet’ feature of DEHB brings benefits to system cost, installation, performance and reliability. In this paper, prototype of the DEHB was described. Based on its ‘wet’ feature, a new fail-safe control for DEHB was proposed. Two types of DEHB architectures that can perform the proposed fail-safe control were described. Superiority of the proposed fail-safe control and architectures for DEHB were examined and verified through simulations and HIL experiments, which helps DEHB to reach a high level of safety and reliability with reduced cost on electro/electronic redundancy.