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The trajectory variation of preceding objects with changing road curvature and uncertain driving behaviors of both host and preceding cars make it difficult for conventional radar-based Adaptive Cruise Control (ACC) system to effectively select its valid target object, which is mainly caused by the deficient judgment about the preceding curves and the behaviors of preceding cars. Through analysis of the trajectories that host and preceding objects generate, the new proposed method can differentiate the operating conditions of each car, either in straight lane, on curve or in lane-change, thus front path prediction and host vehicle's future lane estimation can be precisely fulfilled. From radar and host car's information a coordinate that changes under several criteria can be established, and based on this coordinate the trajectories of preceding and host car can be recorded and analyzed, some mathematics methods are adopted to reach the qualitative conclusion.

This paper proposed a novel fault-tolerant control method based on control allocation via dynamic constrained optimization for electric vehicles with XBW systems. The total vehicle control command is first derived based on interpretation on driver's intention as a set of desired vehicle body forces, which is further dynamically distributed to the control command of each actuator among vehicle four corners. A dynamic constrained optimization method is proposed with the cost function set to be a linear combination of multiple control objectives, such that the control allocation problem is transformed into a linear programming formulation. An analytical yet explicit solution is then derived, which not only provides a systematic approach in handling the actuation faults, but also is efficient and real-time feasible for in-vehicle implementation. The simulation results show that the proposed method is valid and effective in maintaining vehicle operation as expected even with faults.

A fault detection and diagnosis (FDD) algorithm of 4WID/4WIS Electric Vehicles has been proposed in this study aiming to find the actuator faults. The 4WID/4WIS EV is one of the promising architectures for electric vehicle designs which is driven independently by four in-wheel motors and steered independently by four steering motors. The 4WID/4WIS EVs have many potential abilities in advanced vehicle control technologies, but diagnosis and accommodation of the actuator faults becomes a significant issue. The proposed FDD approach is an important part of the active fault tolerant control (AFTC) algorithm. The main objective of the FDD approach is to monitor vehicle states, find the faulty driving motor and then feedback fault information to the controller which would adopt appropriate control laws to accommodate the post-fault vehicle control system.

Power management of a hybrid energy storage system (HESS) with battery and supercapacitor(SC) is of critical importance for electric vehicles to achieve good driving performance, long traveling range and high energy efficiency. Due to the great differences in dynamic characteristics between battery and supercapacitor, and the complexity of a HESS, proper power management strategy between battery and supercapacitor remains to be challenging. The proposed research in this paper is to develop a power-balance and wavelet-transform based strategy for power distribution in a way such that each device can be utilized optimally. The transient dynamics is first decoupled via wavelet-transform algorithm while the power-balance algorithm is employed to improve system robustness based on the desired velocity-SOC relationship and a fuzzy logical controller. Finally some simulations have been conducted with results shown that the proposed strategy is valid and effective.

This paper presents a fault-tolerant control (FTC) algorithm for four-wheel independently driven and steered (4WID/4WIS) electric vehicle. The Extended Kalman Filter (EKF) algorithm is utilized in the fault detection (FD) module so as to estimate the in-wheel motor parameters, which could detect parameter variations caused by in-wheel motor fault. A motion controller based on sliding mode control (SMC) is able to compute the generalized forces/moments to follow the desired vehicle motion. By considering the tire adhesive limits, a reconfigurable control allocator optimally distributes the generalized forces/moments among healthy actuators so as to minimize the tire workloads once the actuator fault is detected. An actuator controller calculates the driving torques of the in-wheel motors and steering angles of the wheels in order to finally achieve the distributed tire forces. If one or more in-wheel motors lose efficacy, the FD module diagnoses the actuator failures first.

PEPS (Passive Entry and Passive Start) system is gradually becoming a main stream option in automotive keyless entry application, which improves the convenience and vehicle anti-theft performance. Based on the complex functions and safety technical requirements of the PEPS controller, and due to the development method of the model-based system design widely used in the automotive electronics industry, this paper presents a model-based on the development of PEPS controller method, which introduces the process of modeling and automatic code generation for the PEPS controller. Through Simulink/Stateflow of PEPS controller using logic system modeling, the PEPS controller complex system functions are divided into different function layers with each functional layer modeling respectively, and implement logic function design by the graphical language.

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.

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.

In order to solve such problems that the millimeter-wave radar is of large computation, poor robustness and low precision of the target tracking algorithm, this paper presents an algorithmic framework for millimeter-wave radar tracking of target-vehicles. The target measurement information outside the millimeter- wave radar detection range is eliminated by the data plausibility judgment method based on the millimeter-wave radar detection parameters. Target clustering is made using Manhattan distance, to eliminate clutter interference and cluster multiple target measurements into one. The data association is made by use of nearest neighbor to determine the correspondence between information received measured by the radar and the real target. The vehicle is the key detection target of the vehicle millimeter-wave radar during road driving.

The high popularity of automobiles has led to frequent collisions. According to the latest statistics of the United Nations, about 1.25 million people worldwide die from road traffic accidents each year. In order to improve the safety of vehicles in driving, the active safety system has become a research hotspot of various car companies and research institutions around the world. Among them, the more mature and popular active security system are Forward Collision Warning(FCW) and Autonomous Emergency Braking(AEB). However, the current active safety system is based on traditional sensors such as radar and camera. Therefore, the system itself has many limitations due to the shortage of traditional sensors. Compared to traditional sensors, Vehicle to Everything (V2X) technology has the advantages of richer vehicle parameter information, no perceived blind spots, dynamic prediction of dangerous vehicle status, and no occlusion restriction.

High-precision and real-time ego-motion estimation is vital for autonomous vehicle. There is a lot GPS-denied maneuver such as underground parking lot in urban areas. Therefore, the localization system relying solely on GPS cannot meets the requirements. Recently, lidar odometry and visual odometry have been introduced into localization systems to overcome the problem of missing GPS signals. Compared with visual odometry, lidar odometry is not susceptible to light, which is widely applied in weak-light environments. Besides, the autonomous parking is highly dependent on the geometric information around the vehicle, which makes building map of surroundings essential for autonomous vehicle. We propose a lidar inertial odometry and mapping. By sensor fusion, we compensate for the drawback of applying a single sensor, allowing the system to provide a more accurate estimate.

With the increasing number of urban cars, parking has become the primary problem that people face in daily life. Therefore, many scholars have studied the automatic parking system. In the existing research, most of the path planning methods use the combined path of arc and straight line. In this method, the path curvature is not continuous, which indirectly leads to the low accuracy of path tracking. The parking path designed using the fifth-order polynomial is continuous, but its curvature is too large to meet the steering constraints in some cases. In this paper, a continuous-curvature parking path is proposed. The parking path tracker based on Model Predictive Control (MPC) algorithm is designed under the constraints of the control accuracy and vehicle steering. Firstly, in order to make the curvature of the parking path continuous, this paper superimposes the fifth-order polynomial with the sigmoid function, and the curve obtained has the continuous and relatively small curvature.

Traffic accidents avoidance is one of the main advantages for automated vehicles. As one of the main causes of vehicle collision accidents, lane changing of the ego vehicle in case that the obstacle vehicles appear in the blind spot with uncertain motion intentions is one of the main goals for the automated vehicle. An intention-aware lane changing collision assistance strategy basing on traffic situation assessment in the complex traffic scenarios is proposed in this paper. Typical Regions of Interest (ROI) within the detection range of the blind spots are selected basing on the road topology structures and state space consisting of the ego vehicle and the obstacle vehicles. Then the motion intentions of the obstacle vehicles in ROI are identified basing on Gaussian Mixture Models (GMM) and the corresponding motion trajectories are predicted basing on the state equation.

Steer-by-Wire system is capable of improving the performance of vehicle handling and stability, and assisting driving. It becomes a key technique to control front wheel angle and simulate the steering resistance delivered to the driver because of removing mechanical linkages between the steering wheel and the front wheels. This paper proposes a bilateral control method of steering wheel torque drive/pinion angle feedback, which is disaccustomed of controlling steering wheel block and steering actuator as master-slave plants. The pinion angle, steering wheel angle and its torque signals are used in the control logic without estimating or measuring the tire/road force. Simulations and vehicle experiments proceeded with this proposed method and the results confirmed that it achieves the bilateral control of the position and torque between the two plants.

Electro-Hydraulic Brake (EHB) system is a kind of active control brake systems of automobile, the pedal from the calipers actuation separated and no longer limited by conventional hardware. The system may come together with ABS, ESP, and ASR function, also the communication with other systems is done via the CAN network. EHB system may be classified a “stepping stone” technology to full brake-by-wire and brings huge transform for the performance of braking system. In this paper, vehicle dynamic models were established and accomplished the control strategy for vehicle stability control with EHB system which can adjust wheel and vehicle motion, improve the lateral and longitudinal vehicle stability. This result was verified by simulation which shows that the controller is effective on improving the vehicle stability.

This paper presents a study on steering effort preference of Chinese drivers based on ADSL Driving Simulator. The results of the simulation test demonstrates that Chinese drivers' steering effort preference increases with vehicle speed, which is similar to European and Japanese drivers', but the mean preference effort level itself is lower than that of European and Japanese drivers' and this same steering effort preference increases obviously with lateral acceleration in linear region (lateral acceleration level lower than 0.3g) while not as evidently in nonlinear region (lateral acceleration level higher than 0.3g).

It is difficult to obtain state variables accurately or economically while vehicle is moving, however these state variables are significant for chassis control. Although many researches have been done, a complex model always leads to a control system with poor real-time performance, while simple model cannot show the real characteristics. So, in order to estimate the value of yaw rate and side slip angle accurately and sententiously, an Extended Kalman Filter (EKF) observer is proposed, which is based on an ameliorated 2-DOF “bicycle model”. The EKF algorithm is achieved numerically and verified by the results from the real field test.

The ability to recognize traffic vehicles’ lane change maneuver lays the foundation for predicting their long-term trajectories in real-time, which is a key component for Advanced Driver Assistance Systems (ADAS) and autonomous automobiles. Learning-based approach is powerful and efficient, such approach has been used to solve maneuver recognition problems of the ego vehicles on conventional researches. However, since the parameters and driving states of the traffic vehicles are hardly observed by exteroceptive sensors, the performance of traditional methods cannot be guaranteed. In this paper, a novel approach using multi-class probability estimates and Bayesian inference model is proposed for traffic vehicle lane change maneuver recognition. The multi-class recognition problem is first decomposed into three binary problems under error correcting output codes (ECOC) framework.

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.