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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.

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.

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.

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.

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.

Vehicle pose estimation is a key technology for autonomous vehicles and a prerequisite for path planning and vehicle control. Visual localization has gradually attracted extensive attention from academia and industry due to its low cost and rich semantic information. However, the incremental calculation principle of the odometry inevitably leads to the accumulation of localization error with the travel distance. To solve this problem, we propose a position correction algorithm based on lightweight landmark map, and further compensate the localization error by analyzing the error characteristics. The proposed algorithm takes the stop lines on the road as landmarks, and pairs bag-of-word vectors with the positions of the corresponding landmarks. Once landmarks in the map are encountered and successfully associated, the position of the landmarks can be exploited to effectively reduce the drift of the odometry. We also present a reliable landmark map construction method.

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.