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In recent years, intelligent vehicles have become one of the major research topics in vehicle engineering and have created a new opportunity for the automotive industry. Simulation and real experiment are both essential to the development of intelligent vehicle technologies. Vehicle positioning systems, such as global positioning system (GPS), play an important role in intelligent vehicle development. The GPS model plays a major part in the development of intelligent vehicle simulation systems. Primarily focusing on application requirements of intelligent vehicle simulation platforms for GPS sensor modeling, considering the major factors affecting positioning accuracy in vehicle driving environments, this article establishes a new GPS model and algorithm based on the physical and functional characteristics of GPS. As the basis of this model system, a precise ephemeris model is established to obtain the coordinates of GPS satellites at any given time.

Nowadays high thermal efficiency engine is the mainstream of the gasoline engine development, and the control of the design period is strict, which lead to the cylinder head design become more and more difficult. How to get the optimal design of cylinder head quickly is an important research topic by design professionals in the current, which not only meets the performance requirements, but also guarantees the requirement of reliability. The sub-model method is high efficient and high precision in solving the complex problem of stress and strain, which widely used in truss, road, bridge and large container. Using this technology in the local structure optimization of the cylinder head, will shorten the cycle of structure optimization significantly, and get the optimal design of cylinder head quickly. In this paper, Firstly, the cylinder head global FE model was set up.

Adaptive cruise control (ACC), as one of the advanced driver assistance systems (ADAS), has become increasingly popular in improving both driving safety and comfort. Since the objectives of ACC can be multi-dimensional, and often conflict with each other, it is a challenging task in its control design. The research presented in this paper takes ACC control design as a constrained optimization problem with multiple objectives. A hierarchical framework for ACC control is introduced, aimed to achieve optimal performance on driving safety and comfort, speed and/or distance tracking, and fuel economy whenever possible. Under the hierarchical framework, the operational mode is determined in the upper layer, in which a model predictive control (MPC) based spacing controller is employed to deal with the multiple control objectives. On the other hand, the lower layer is for actuator control, such as braking and driving control for vehicle longitudinal dynamics.

Research and development of autonomous functions for a road vehicle become increasingly active in recent years. However, the vehicle driving dynamics performance and safety are the big challenge for the development of autonomous vehicles especially in severe environments. The optimum driving dynamics can only be achieved when the traction torque on all wheels can be influenced and controlled precisely. In this study, we present a novel approach to this problem by designing an advanced torque vectoring controller for an autonomous vehicle with four direct-drive in-wheel motors to generate and control the traction torque and speed quickly and precisely, thus to improve the stability and safety of the autonomous vehicle. A four in-wheel motored autonomous vehicle equipped with Radar and camera is modelled in PanoSim software environment. Vehicle-to-Vehicle (V2V) communication is used in this software platform to avoid collision.

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).

Driver individualities is crucial for the development of the Advanced Driver Assistant System (ADAS). Due to the mechanism that specific driving operation action of individual driver under typical conditions is convergent and differentiated, a novel driver individualities recognition method is constructed in this paper using random forest model. A driver behavior data acquisition system was built using dSPACE real-time simulation platform. Based on that, the driving data of the tested drivers were collected in real time. Then, we extracted main driving data by principal component analysis method. The fuzzy clustering analysis was carried out on the main driving data, and the fuzzy matrix was constructed according to the intrinsic attribute of the driving data. The drivers’ driving data were divided into multiple clusters.

Electric vehicle (EV) has been regarded as not only an effective solution for environmental issues but also a more controllable and responsible device to driving forces with electric motors and precise torque measurement. For electric vehicle equipped with four in-wheel motors, its tire longitudinal forces can be generated independently and individually with fully utilized tire adhesion at each corner. This type of the electric vehicles has a distributed drive system, and often regarded as an over-actuated system since the number of actuators in general exceeds the control variables. Control allocation (CA) is often considered as an effective means for the control of over-actuated systems. The in-vehicle network technology has been one of the major enablers for the distributed drive systems. The vehicle studied in this research has an electrohydraulic brake system (EHB) on front axle, while an electromechanical brake system (EMB) on rear axle.

This paper proposes a Real-Time Estimation of Radar Cross Section for ADAS Simulation, aimed to enable math-based virtual development and test of ADAS. The electromagnetic scattering mechanism is firstly analyzed with targets to be typical objects in traffic. Then a geometric model is developed, in which the object surfaces are divided into multiple scattering zones corresponding to different scattering mechanism. According to different surface curvature radius and scattering mechanism, the scattering zones are approximately equivalent to plane, cylinder, sphere and so on. Using the ARD model based on an improved physical optics and diffraction theory, RCS value of a zone is estimated. Then the RCS of the object surface is obtained by vector superposition of all zones. Some typical simulation comparisons are carried out, which proves the practicability of our method.

A vehicle dynamics stability control system based on integrated-electro-hydraulic brake (I-EHB) system with hierarchical control architecture and nonlinear control method is designed to improve the vehicle dynamics stability under extreme conditions in this paper. The I-EHB system is a novel brake-by-wire system, and is suitable to the development demands of intelligent vehicle technology and new energy vehicle technology. Four inlet valves and four outlet valves are added to the layout of a conventional four-channel hydraulic control unit. A permanent-magnet synchronous motor (PMSM) provides a stabilized high-pressure source in the master cylinder, and the four-channel hydraulic control unit ensures that the pressures in each wheel cylinder can be modulated separately at a high precision. Besides, the functions of Anti-lock Braking System, Traction Control System and Regenerative Braking System, Autonomous Emergency Braking can be integrated in this brake-by-wire system.

The conventional radar modeling methods for automotive applications were either function-based or physics-based. The former approach was mainly abstracted as a solution of the intersection between geometric representations of radar beam and targets, while the latter one took radar detection mechanism into consideration by means of “ray tracing”. Although they each has its unique advantages, they were often unrealistic or time-consuming to meet actual simulation requirements. This paper presents a combined geometric and physical modeling method on millimeter-wave radar systems for Frequency Modulated Continuous Wave (FMCW) modulation format under a 3D simulation environment. With the geometric approach, a link between the virtual radar and 3D environment is established. With the physical approach, on the other hand, the ideal target detection and measurement are contaminated with noise and clutters aimed to produce the signals as close to the real ones as possible.

LiDAR sensors have played more and more important role on Intelligent and Connected Vehicles (ICV) and Advanced Driver Assistance Systems (ADAS) .However, the development and testing of LiDAR sensors under real driving environment for ADAS applications are greatly limited by various factors, and often are impossible due to safety concerns. This paper proposed a novel functional LiDAR model under virtual driving environment to support development of LiDAR-based ADAS applications under early stage. Unlike traditional approaches on LiDAR sensor modeling, the proposed method includes both geometrical modeling approach and physical modeling approach. While geometric model mainly produces ideal scanning results based on computer graphics, the physical model further brings physical influences on top of the geometric model. The range detection is derived and optimized based on its physical detection and measurement mechanism.

Environmental sensing and perception is one of the key technologies on intelligent driving or autonomous vehicles. As a complementary part to current radar and lidar sensors, ultrasonic sensor has become more and more popular due to its high value to the cost. Different from other sensors mainly based on propagation of electromagnetic wave, ultrasonic sensor possesses some unique features and physical characteristics that bring many merits to autonomous vehicle research, like transparent obstacles and highly reflective surfaces detection. Its low-cost property can further bring down hardware cost to foster widespread use of intelligent driving or autonomous vehicles. To accelerate the development of autonomous vehicle, this paper proposes a high fidelity ultrasonic sensor model based on its physical characteristics, including obstacle detection, distance measurement and signal attenuation.

The evaluation of electric vehicle electric/electronic-architectures (e/e-architectures) is the main topic of this paper. The electric vehicle is chosen as an example system, as it reflects the typical challenges of modern vehicle e/e-architecture development. The development of modern automotive technology also presents another important trend - vehicle electrification. New electric and electronic devices are developed and required in the automotive industry and control commands are exchanged by electric and electronic ones. The energy storage systems (ESS) properly reflect the above two aspects. The energy storage device also takes care of the peak loads, the high load dynamics, and it utilizes the braking energy in order to increase the efficiency. In this work a Li-ion battery and an ultracapacitor both are considered as energy storage devices.

This paper presents a unified novel function-based brake control architecture, which is designed based on a top-down approach with functional abstraction and modularity. The proposed control architecture includes a commands interpreter module, including a driver commands interpreter to interpret driver intention, and a command integration to integrate the driver intention with senor-guided active driving command, state observers for estimation of vehicle sideslip, vehicle speed, tire lateral and longitudinal slips, tire-road friction coefficient, etc., a commands integrated control allocation module which aims to generate braking force and yaw moment commands and provide optimal distribution among four wheels without body instability and wheel lock or slip, a low-level control module includes four wheel pressure control modules, each of which regulates wheel pressure by fast and accurate tracking commanded wheel pressure.

Since road electric vehicles typically require a significantly variable and random load power demand in response to traffic conditions, such as frequent sequences of acceleration and deceleration and uphill followed by downhill runs. In this context, the energy management system of electric vehicle must ensure an effective power distribution between battery and supercapacitor to satisfy load demand. In this paper, the power management control strategy of hybrid energy storage system is developed by introducing terrain information to optimize system efficiency and battery lifetime. In this presented research, we aim at developing a power management control strategy considering the influence of the terrain information on system efficiency and battery lifetime.

This paper describes a novel power management control strategy of battery and supercapacitor hybrid energy storage system to improve system efficiency and battery lifetime. In the presented research, the high and low frequency power demand in the load is separated by a Haar wavelet transform algorithm to overcome the problem of battery overload work and associated degeneration in battery lifetime resulting from an ineffective distribution between battery and supercapacitor. The purpose of frequency distribution is that the supercapacitor is used to share high frequency power components of load power demand to smooth the power demand applied to battery. However, the sole frequency control often fails to realize the optimal utilization of supercapacitor because of the uncertain variation in the driving cycle.

Nowadays, the vehicle market puts forward urgent requirement for new kinds of braking booster because the traditional vacuum booster cannot meet the demands of new energy vehicles anymore. However, one problem that all the new plans should face is how to guarantee an ideal pedal feeling. In this paper, a novel mechatronics braking booster is proposed, and servo motor introduced into the booster makes the assist rate can be adjusted under a great degrees of freedom, so the structural parameters and control parameters of the booster should be determined elaborately to get an optimal pedal feeling. The pedal feeling is always represented by the pedal stoke-force curve which is influenced by different parameters.

Brake-by-wire (BBW) system has drawn a great attention in recent years as driven by rapidly increasing demands on both active brake controls for intelligent vehicles and regenerative braking controls for electric vehicles. However, unlike conversional brake systems, the reliability of the brake-by-wire systems remains to be challenging due to its lack of physical connection in case of system failure. There are various causes for the failure of a BBW system, such as failure of brake controller, loss of sensor signals, failure of communication or even power supply, to name a few. This paper presents a fault-tolerant control under novel control architecture. The proposed control architecture includes a driver command interpreter module, a command integration module, a control allocation module, a fault diagnosis module and state observers. The fault-tolerant control is designed based on a quadratic optimal control method with consideration of actuator constraints.

This paper reports an effort to improve plan of vehicle trajectory using an approach with rapidly-exploring random trees (RRT), which has been widely adopted in the prior art for complex and dynamic traffic environment. Design and implement of an integrated threat assessment is presented that evaluates threats of the trajectory. A node and trajectory evaluation index was introduced into the proposed RRT algorithm to connect an appropriate node and select the best trajectory. The contribution of this paper is on the threat assessment that takes into account not only obstacle avoidance but also stability. The simulation is conducted and the results show that the proposed method works as expected and is valid and effective.