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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.

Environmental perception is a crucial subsystem in autonomous vehicles. In order to build safe and efficient traffic transportation, several researches have been proposed to build accurate, robust and real-time perception systems. Camera and LiDAR are widely equipped on autonomous self-driving cars and developed with many algorithms in recent years. The fusion system of camera and LiDAR provides state-of the-art methods for environmental perception due to the defects of single vehicular sensor. Extrinsic parameter calibration is able to align the coordinate systems of sensors and has been drawing enormous attention. However, differ from spatial alignment of two sensors’ data, joint calibration of multi-sensors (more than two sensors) should balance the degree of alignment between each two sensors.

A new range-extension hybrid powertrain concept, namely the Tongji Extended-range Hybrid Technology (TJEHT) was developed and demonstrated in this study. This hybrid system is composed of a direct-injection gasoline engine, a traction motor, an Integrated Starter-Generator (ISG) motor, and a transmission. In addition, an electronically controlled clutch between the ISG motor and engine, and an electronically controlled synchronizer between the ISG motor and transmission are also employed in the transmission case. Hence, this system can provide six basic operating modes including the single-motor driving, dual-motor driving, serial driving, parallel driving, engine-only driving and regeneration mode depending on the engagement status of the clutch and synchronizer. Importantly, the unique dual-motor operation mode can improve vehicle acceleration performance and the overall operating efficiency.

Tracking vehicle trajectories is essential for autonomous vehicles and advanced driver-assistance systems to understand traffic environment and evaluate collision risk. In order to reduce the position deviation and fluctuation of tracking on-road vehicle by millimeter-wave radar (MMWR), an interactive multi-model Kalman filter (IMM-KF) tracking algorithm including data association and track management is proposed. In general, it is difficult to model the target vehicle accurately due to lack of vehicle kinematics parameters, like wheel base, uncertainty of driving behavior and limitation of sensor’s field of view. To handle the uncertainty problem, an interacting multiple model (IMM) approach using Kalman filters is employed to estimate multitarget’s states. Then the compensation of radar ego motion is achieved, since the original measurement is under the radar polar coordinate system.

For an electric vehicle driven by four in-wheel motors, the torque of each wheel can be controlled precisely and independently. A closed-loop control method of differential drive assisted steering (DDAS) has been proposed to improve vehicle steering properties based on those advantages. With consideration of acceleration requirement, a three dimensional characteristic curve that indicates the relation between torque and angle of the steering wheel at different vehicle speeds was designed as a basis of the control system. In order to deal with the saturation of motor's output torque under certain conditions, an anti-windup PI control algorithm was designed. Simulations and vehicle tests, including pivot steering test, lemniscate test and central steering test were carried out to verify the performance of the DDAS in steering portability and road feeling.

This paper describes a methodology to estimate yaw rate of a 4-wheel-drive electric vehicle, in which wheel driven torque can be independently controlled by electric motor. Without non-driven wheels it would be difficult to estimate the vehicle yaw rate precisely, especially when some of the four wheels have large slip ratio. Therefore, a model based estimation methodology is put forward, which uses four wheel speeds, steering wheel angle and vehicle lateral acceleration as input signals. Firstly the yaw rate is estimated through three different ways considering both vehicle kinematics and vehicle dynamics. Vehicle kinematics based method has good estimation accuracy even when the vehicle has large lateral acceleration. However, it can not provide satisfying results when the wheel has large slip ratio. In contrast, vehicle dynamics based method is not so sensitive to wheel slip ratio.

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.

The vehicular fuel cell stack is unavoidably impacted by the vibration in the real-world usage due to the road unevenness. However, effects of vibration on stacks have yet to be completely understood. In this work, the mechanical integrity and gas-tightness of the stack were investigated through a strengthen road vibration test with a duration of 200 h. The excitation signals applied in the vibration test were simulated by the acceleration of the stack, which were previously measured in a vehicle vibration test. The load signals of the vehicle vibration test were iterated through a road simulator from vehicle acceleration signals which were originally sampled in the proving ground. Frequency sweep test was conducted before and after the vibration test. During the vibration test, mechanical structure inspection and pressure maintaining test of the stack were conducted at regular intervals.

In this paper, a speed tracking controller is designed for the All-terrain vehicles. The method of feedforward with state variable feedback based on conditional integrators is adopted by the proposed control algorithm. The feedforward is designed considering the influence of the road slope on the longitudinal dynamics, which makes the All-terrain vehicles satisfy the acceleration demand of the upper controller when it tracks the desired speed on the road with slope varying greatly. The road slope is estimated based on a combined kinematic and dynamic model. This method solves the problem that road slope estimation requires an accurate vehicle dynamic model and are susceptible to acceleration sensor bias. Based on the vehicle dynamic model and the nonlinear tire model, the method of conditional integration is used in the state variable feedback, which considers the saturation constraint of the actuator with the intention of preventing the divergent integral operation.

Path tracking is the rudimentary capability and primary task for autonomous ground vehicles (AGVs). In this paper, a novel four-wheel-independent-steering (4WIS) and four-wheel-independent-drive (4WID) electric vehicle (EV) is proposed which is equipped with steer-by-wire (SBW) system. For path-tracking controller design, the nonlinear vehicle model with 2 degrees of freedom (DOF) is built utilizing the nonlinear Dugoff tire model. The nonlinear dynamic model of SBW system is conducted as well considering the external disturbances. As to the path-tracking controller design, an integrated four-wheel steering (4WS) and direct yaw-moment control (DYC) system is designed based on the model predictive control (MPC) algorithm to track the target path described by desired yaw angle and lateral displacement. Then, the fast terminal sliding mode controller (FTSMC) is proposed for the SBW system to suppress disturbances.

The active engine mount (AEM) is developed in automotive industry to improve overall NVH performance. The AEM is designed to reduce major-order signals of engine vibration over a broad frequency range, therefore it is of vital importance to extract major-order signals from vibration before the actuator of the AEM works. This work focuses on a method of real-time extraction of the major-order acceleration signals at the passive side of the AEM. Firstly, the transient engine speed is tracked and calculated, from which the FFT method with a constant sampling rate is used to identify the time-related frequencies as the fundamental frequencies. Then the major-order signals in frequency domain are computed according to the certain multiple relation of the fundamental frequencies. After that, the major-order signals can be reconstructed in time domain, which are proved accurate through offline simulation, compared with the given signals.

Micro electric vehicle has gained increasingly popularity among the public due to its compact size and reasonable price in China in recent years. Since design factors that influence the power of electric vehicle drive-motor like maximum speed, acceleration time and so on are not fixed but varies in certain scopes. Therefore, to optimize the process of matching drive-motor’s power, qualitatively and quantitatively studies should be done to determine the optimal parameter combination and improve the design efficiency. In this paper, three basic operating conditions including driving at top speed, ascending and acceleration are considered in the matching process. And the Sobol’ method of global sensitivity analysis (GSA) is applied to evaluate the importance of design factors to the drive-motor’s power in each working mode.

In order to improve the robustness and stability of autonomous vehicle at high speed, a path tracking approach which combines front steering and differential braking is investigated in this paper. A bicycle model with 3-DOFs is established and a linear time-varying predictive model using front steering as its control input can be derived. Based on model predictive theory, the path tracking issue using linear time-varying model predictive control can be transformed into an online quadratic programming problem with constraints. The expected front steering angle can be obtained from online moving optimization. Then the direct yawing control is adopted to treat two types of differential braking control. The first one investigates steady-state gain of yaw rate in linear 2-DOFs vehicle model, and designs a stable differential braking controller which is based on reference yaw rate.

Faced with intricate traffic conditions, the single sensor has been unable to meet the safety requirements of Advanced Driver Assistance Systems (ADAS) and autonomous driving. In the field of multi-target tracking, the number of targets detected by vision sensor is sometimes less than the current tracks while the number of targets detected by millimeter wave radar is more than the current tracks. Hence, a multi-sensor information fusion algorithm is presented by utilizing advantage of both vision sensor and millimeter wave radar. The multi-sensor fusion algorithm is based on centralized fusion strategy that the fusion center takes a unified track management. At First, vision sensor and radar are used to detect the target and to measure the range and the azimuth angle of the target. Then, the detections data from vision sensor and radar is transferred to fusion center to join the multi-target tracking with the prediction of current tracks.

Vehicle sideslip angle estimation is of great importance to the vehicle stability control as it could not be measured directly by ordinary vehicle-mounted sensors. As a result, researchers worldwide have carried out comprehensive research in estimating the vehicle sideslip angle. First, as the attitude would affect the acceleration information measured by the IMU directly, different kinds of vehicle attitude estimation methods with multi-sensor fusion are presented. Then, the estimation algorithms of the vehicle sideslip angle are classified into the following three aspects: kinematic model based method, dynamic model based method, and fusion method. The characteristics of different estimation algorithms are also discussed. Finally, the conclusion and development trend of the sideslip angle estimation are prospected.

The coordinated control of stability and steering systems in collision avoidance steering evasion has been widely studied in vehicle active safety area, but the studies are mainly aimed at autonomous vehicle without driver or conventional combustion engine vehicle. This paper focuses on the control of hybrid vehicle integrated with rear hub in emergency steering evasion situation, and considering the driver’s characteristics. First, the mathematics model of vehicle dynamics and driver has been given. Second, based on the planned steering evasion path, the model predictive control method is presented for achieving higher evasion path tracking accuracy under driver’s steering input. The prediction model includes an adaptive preview distance driver model and a vehicle dynamics model to predict the driver input and the vehicle trajectory.

The unscented Kalman filter (UKF) is applied to estimate the real vehicle velocity. The velocity estimation algorithm uses lateral acceleration, longitudinal acceleration and yaw rate as inputs. The non-linear vehicle model and Dugoff tire model are built as the estimation model of UKF. Some parameters of Dugoff tire model and vehicle, which can't be measured directly, are identified by the particle swarm optimization (PSO). For the purpose of evaluating the algorithm, the estimation values of UKF are compared with measurements of the Inertial and GPS Navigation system. Besides, the real time property of UKF is tested by xPC Target, which is a real-time software environment from MathWorks. The result of the real vehicle experiment demonstrates the availability of the UKF and PSO in vehicle velocity estimation.

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.

Lane marking detection plays a crucial role in Autonomous Driving Systems or Advanced Driving Assistance System. Vision based lane marking detection technology has been well discussed and put into practical application. LiDAR is more stable for challenging environment compared to cameras, and with the development of LiDAR technology, price and lifetime are no longer an issue. We propose a lane marking detection algorithm based on solid-state LiDARs. First a series of data pre-processing operations were done for the solid-state LiDARs with small field of view, and the needed ground points are extracted by the RANSAC method. Then, based on the OTSU method, we propose an approach for extracting lane marking points using intensity information.

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.