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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.

Accurate and reliable localization in GNSS-denied environments is critical for autonomous driving. Nevertheless, LiDAR-based and camera-based methods are easily affected by adverse weather conditions such as rain, snow, and fog. The 4D Radar with all-weather performance and high resolution has attracted more interest. Currently, there are few localization algorithms based on 4D Radar, so there is an urgent need to develop reliable and accurate positioning solutions. This paper introduces RIO-Vehicle, a novel tightly coupled 4D Radar/IMU/vehicle dynamics within the factor graph framework. RIO-Vehicle aims to achieve reliable and accurate vehicle state estimation, encompassing position, velocity, and attitude. To enhance the accuracy of relative constraints, we introduce a new integrated IMU/Dynamics pre-integration model that combines a 2D vehicle dynamics model with a 3D kinematics model.

With the development of brake-by-wire technology, electro-hydraulic composite braking technology came into being. This technology distributes the total braking force demand into motor regenerative braking force and hydraulic braking force, and can achieve a high energy recovery rate. The existing composite braking control belongs to single-channel control, i.e., the four wheel braking pressures are always the same, so the hydraulic braking force distribution relationship of the front and rear wheels does not change. For single-axle-driven electric vehicles, the additional regenerative braking force on the driven wheels will destroy the original braking force distribution relationship, resulting in reduced braking efficiency of the driven wheels, which are much easier to lock under poor road adhesion conditions.

In the process of ABS control, the Anti-lock braking system (ABS) of the vehicle adjusts the wheel cylinder brake pressure through the hydraulic actuator so as to control the movement of the wheel. The high-speed on-off valve (HSV) is the key components of the Anti-lock braking system. HSV affects the performance of the hydraulic actuator and the valve response characteristics affects the Anti-lock braking system pressure response as well as braking effect. In this paper, the electromagnetic field theory and flow field theory of HSV are analyzed, and simulation analysis of electromagnetic field characteristics of HSV is done by ANSYS. Combined with the ANSYS analysis results, a precise physical model of HSV is constructed in AMESim. Meanwhile, the valve response characteristics are analyzed. Moreover, the influence of different wheel cylinder diameter and PWM carrier frequency on hydraulic braking force characteristics are analyzed.

In order to solve the problem that in the traditional trilateral positioning algorithm, the final positioning error is large when there is a certain error in the measured three-sided distance, a UWB positioning algorithm based on Back Propagation (BP) neural network is proposed. The algorithm utilizes the fast learning characteristic and the ability of approximating any non-linear mapping of neural network, and realizes the location of the mobile label through the TOA measurement value provided by the base station and the BP neural network. By comparing the traditional trilateral positioning algorithm, the BP neural network algorithm based on four distance inputs and the BP neural network algorithm based on four distance inputs with trilateral positioning coordinates, it can be seen that the positioning error of traditional trilateral positioning algorithm is 30 cm, and the positioning error of the positioning algorithm based on the BP neural network proposed in this paper is 10 cm.

Electromechanical Braking System (EMB) stops the wheel by motor and related enforce mechanism to drive braking pads to clamp the friction plate. It is compact in sized as well as faster in response, which solves the issue of potential leakage and slows response of traditional hydraulic brake system. The institutions at home and abroad have put forward all kinds of new structural schemes of EMB. At present, there are various EMB structural schemes, but the analysis and evaluation of these schemes are relatively few. In this paper, on the basis of a large number of research, the EMB actuator is modular decomposed according to function ,then the parametric 3D model library of each function module is established. According to brake requirements of the target vehicle, a development platform is set up to match EMB actuator structure scheme quickly.

Vehicle active collision avoidance includes collision avoidance by braking and by steering. However, both of these two methods have their limitations. Therefore, it is significant to establish the feasible region of active collision avoidance to choose the optimal way to avoid traffic accidents. This paper focuses on the steering control of an autonomous vehicle to track the planned trajectory and to perform an emergency collision avoidance maneuver. Meanwhile, the collision avoidance effect of steering control is compared with that of braking control. The path tracking controller is designed by hierarchical control structure. The upper controller includes model predictive control allocation and speed controller, and the lower is designed by weighted least-squares control allocation for torque allocation. Besides, seven order polynomial is used for path planning.

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.

As is known to all, the structure of the chassis has been greatly simplified as the application of in-wheel motor in electric vehicle (EV) and distributed control is allowed. The micro EV can alleviate traffic jams, reduce the demand for motor and battery capacity due to its small size and light weight and accordingly solve the problem that in-wheel motor is limited by inner space of the wheel hub. As a result, this type of micro EV is easier to be recognized by the market. In the micro EV above, two seats are side by side and the battery is placed in the middle of the chassis. Besides, in-wheel motors are mounted on the rear axle and only front axle retains traditional hydraulic braking system. Based on this driving/braking system, distribution of braking torque, system reliability and braking intensity is analyzed in this paper.

Study on the braking torque allocation of the ABS (Anti-lock Brake System) of the electro-hydraulic brake system in the distributed drive electric vehicles, using a hierarchical control structure, of which the lower controller takes a braking torque allocation strategy based on frequency, so as to achieve a good braking effect. The lower controller uses the strategies which are based on the filter principle or the weighted least squares algorithm. To the former, Butterworth filter is selected to execute the braking torque allocation. Then the ABS braking torque allocation strategy based on Butterworth filter and the weighted least squares are designed and analyzed respectively, finally their braking effects are simulated and contrasted in Simulink and AMESim.

In this paper, a novel Electromechanical Brake actuator (EMB) is redesigned aimed at an electric vehicle driven by wheel hub motor. The two way ball screw is adopted in this mechanism. Clearance automatic adjustment and parking braking function is added in this mechanism. As a consequence, fast braking response is achieved and the wear difference of the inner and outer pads can be minimized and the initial braking force can also be improved. The electric vehicle is based on a traditional chassis. In this electric vehicle which driven by wheel hub motor, the brake disc and brake actuator will be correspondingly moved inside because wheel hub motor will take up inner space of wheel hub. As a result, the actuator might interfere with the suspension and steering systems and influence hard spot of chassis design. To solve this problem, conversely installed caliper concept is used in this paper.

Accurate parameters of vehicle motion state are very important to the active safety of a vehicle. Currently the extended Kalman filter and unscented Kalman filter are widely used in estimation of the key state parameters, such as speed. In this situation, tire model must be used. The Magic Formula Tire Model is widely used in vehicle dynamics simulation because of its high versatility and accuracy. However, it requires a large number of parameters, which make the key state parameters of a real vehicle difficult to accurately obtain. Therefore, it is limited in real-time control of a vehicle. Firstly, the original Magic Formula Tire Model is simplified in this paper; then Jin Chi's Tire Model is introduced; thirdly, parameters of both the simplified Magic Formula and Jin Chi's Tire Model are identified using PSO (Particle Swarm Optimization) algorithm. Finally, Jin Chi's Tire Model is also used in parameters identification of experimental data.

With the objective to regulate hydraulic pressure accurately by controlling high speed on-off valve (HSV), finite element models are parameterized based on measured parameters of an ABS hydraulic actuator unit (HCU). The data that reflects transient electromagnetic characteristics of HSV is selected with finite element numerical simulation. Taking full advantage of those data, accurate physical models of HSV are built with other parts of hydraulic braking system. Then a new system structure is proposed to control hydraulic pressure. Not only do simulation results show ideal control effect, but also hydraulic braking system can be controlled under arbitrary input signal. Accordingly, hydraulic braking force can achieve fine regulation. Finally, the hydraulic braking system is utilized to design antilock brake control system for four-wheel-drive electric vehicle with electro-hydraulic braking. That kind of system is established on the basis of hierarchical control structure.

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.