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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.

The research and development of data-driven highly automated driving system components such as trajectory prediction, motion planning, driving test scenario generation, and safety validation all require large amounts of naturalistic vehicle trajectory data. Therefore, a variety of data collection methods have emerged to meet the growing demand. Among these, camera-equipped drones are gaining more and more attention because of their obvious advantages. Specifically, compared to others, drones have a wider field of bird's eye view, which is less likely to be blocked, and they could collect more complete and natural vehicle trajectory data. Besides, they are not easily observed by traffic participants and ensure that the human driver behavior data collected is realistic and natural. In this paper, we present a complete vehicle trajectory data extraction framework based on aerial videos. It consists of three parts: 1) objects detection, 2) data association, and 3) data cleaning.

A dynamic controller is designed for unmanned skid-steering vehicle. The vehicle speed is controlled through driving torque of engine to achieve the desired vehicle speed and the steering is controlled through hydraulic braking on each side of the vehicle to achieve the desired yaw rate. Contrary to the common approaches by considering non-holonomic constraints, tire slip and saturation of actuators torque influencing the driving and braking are considered, based on the analysis of vehicle dynamic model and nonlinear tire model. Hence, with conditional integrators, the dynamic controller overcoming integral saturation is designed to ensure the accurate tracking for desired signals under influence of tire forces and constraint of actuators. In addition, the exponential kind filter is utilized to enhance the ability of smoothing noise of wheel speed. To perform small radius cornering maneuvers, a dynamic control strategy for steering when vehicle speed is zero is also designed.

A rapid path-planning algorithm that generates drivable paths for an autonomous vehicle operating in structural road is proposed in this paper. Cubic B-spline curve is adopted to generating smooth path for continuous curvature and, more, parametric basic points of the spline is adjusted to controlling the curvature extremum for kinematic constraints on vehicle. Other than previous approaches such as inverse kinematics, model-based prediction postprocess approach or closed-loop forward simulation, using the kinematics model in each iteration of path for smoothing and controlling curvature leading to time consumption increasing, our method characterized the vehicle curvature constraint by the minimum length of segment line, which synchronously realized constraint and smooth for generating path. And Differ from the path of robot escaping from a maze, the intelligent vehicle traveling on road in structured environments needs to meet the traffic rules.

BBW (Brake-by-wire) can increase the electric and hybrid vehicles performance and safety. This paper proposes a novel mechatronic booster system, which includes APS (active power source), PFE (pedal feel emulator), ECU (electronic control unit). The system is easily disturbed when the system parameters and the outside conditions change. The system performance is weakened. The cascade control technique can be used to solve the problem. This paper develops an adaptive cascade optimum control (ACOC) algorithm based on the novel mechatronic booster system. The system is divided into main loop and servo loop, both of them are closed-loop system. The servo-loop system can eliminate the disturbance which exists in the servo loop. So the robustness of the cascade control system is improved than which of the general closed-loop control system. Different control object is respectively chosen. The control-oriented mathematical model is designed.

Based on the four-wheel-drive electric vehicle (4WD EV), a variable structure control (VSC) strategy is designed in this paper for the anti-lock braking control. With nonpeak friction coefficient as target, sign judgment method of switch function in this VSC strategy is improved and a new control algorithm is proposed. The improved VSC strategy is made robust to the parameters of the algorithm and verified by the computer simulation as well as the hard-in-loop test. The results show that the slip rate can be controlled to a point in the stable area near the optimal slip ratio and the control strategy can effectively realize the anti-lock braking control.

BBW (Brake-by-wire) can increase the vehicle safety performance due to high control accuracy and fast response speed. As one solution of BBW, the novel Integrated-electro-hydraulic brake system (I-EHB) is proposed, which consists of electro-hydraulic booster and hydraulic pressure control unit. The electro-hydraulic booster is activated by an electric motor that driving linear motion mechanism to directly produce the master cylinder pressure. With electro-hydraulic booster as an actuator, the hydraulic pressure control problem is a key issue. Most literatures deal with the pressure control issue based on the feedback pressure signal measured by pressure sensor. As far as the authors are aware, none of the proposed techniques takes into account the pressure sensor unequipped BBW. In this paper, there is no pressure feedback signal, but there is only position feedback signal measured by position sensor for control law design.

With the electrification and intelligentialization of vehicle, requirements on more intelligent and integrated brake system are put forward. A novel integrated-electro-hydraulic brake system (I-EHB) for automotive is presented to fulfill these requirements. I-EHB is consisted of active power source (APS), pedal feel emulator (PFE), electro control unit (ECU) and hydraulic control unit (HCU). The system characteristics of I-EHB are tested through test rig. According to characteristics experiments, friction and non-linear phenomena in hydraulic pressure control are found. In order to overcome these phenomena in control of I-EHB, chatter-compensation is adopted based on experiment analysis. Algorithm are tested and optimized through test rig. As a result, through chatter-compensation the hydraulic pressure is controlled accurately and chatter-compensation is optimized for different working conditions.

Since data processing methods could not completely eliminate the uncertainty of signals, it is a key issue for stable and robust decision-making for uncertainty tolerance of intelligent vehicles. In this paper, a decision-making for an Autonomous Emergency Braking (AEB) case considering the uncertainty of road adhesion coefficient estimation (RACE) is proposed. Firstly, the 3σ criterion is employed to classify the confidence in order to establish the decision-making mechanism considering the signal uncertainty of RACE. Secondly, the model for AEB with the uncertainty of the road adhesion coefficient estimated is designed based on the Seungwuk Moon model. Thirdly, a CCRs and CCRm scenario was designed to verify the feasibility in reference to the European New Car Assessment Programme (Euro NCAP) standard. Finally, the results of 10,000 cycles test illustrate that the proposed method is stable and could significantly improve the safety confidence both in the CCRs and CCRm scenarios.

Many learning-based methods estimate ego-motion using visual sensors. However, visual sensors are prone to intense lighting variations and textureless scenarios. 4D radar, an emerging automotive sensor, complements visual sensors effectively due to its robustness in adverse weather and lighting conditions. This paper presents an end-to-end 4D radar-visual odometry (4DRVO) approach that combines sparse point cloud data from 4D radar with image information from cameras. Using the Feature Pyramid, Pose Warping, and Cost Volume (PWC) network architecture, we extract 4D radar point features and image features at multiple scales. We then employ a hierarchical iterative refinement approach to supervise the estimated pose. We propose a novel Cross-Modal Transformer (CMT) module to effectively fuse the 4D radar point modality, image modality, and 4D radar point-image connection modality at multiple scales, achieving cross-modal feature interaction and multi-modal feature fusion.

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.

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.

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.

The integrated control system which combines the differential drive assisted steering (DDAS) and the direct yaw moment control (DYC) for the distributed drive electric vehicle (DDEV) is studied. A handling improvement algorithm for the normal cornering maneuvers is proposed based on motion tracking control. Considering the ideal assistant power character curves at different velocities, an open-loop DDAS control strategy is developed to respond the driver’s demand of steering wheel torque. The DYC strategy contains the steering angle feedforward and the yaw rate feedback. The steering angle feedforward control strategy is employed to improve yaw rate steady gain of vehicle. The maximum feedforward coefficients at different velocities are obtained from the constraint of the motor external characteristic, final feedforward coefficients are calculated according to the ideal assistant power character curve of the DDAS.

The control in transient conditions when hydraulic brake and regenerative brake switch mutually is the key technical issue about electric vehicle hybrid brake system, which has a direct influence on the braking feel of driver and vehicle braking comfort. A coordination control system has been proposed, including brake force distribution correction module and motor force compensation module. Brake force distribution correction module has fixed the distribution results in hydraulic brake force intervention condition, hydraulic brake force evacuation condition and regenerative brake force low speed evacuation condition. Motor compensation module has compensated hydraulic system with motor system, which has fast and accurate response, thus the response of whole hybrid system has been improved.

In this paper, an integrated electronic hydraulic brake(I-EHB) system is introduced, which is mainly composed of a motor, a worm gear, a worm, a gear, a rack etc. The friction leads the system to the creeping phenomenon and the dead zone. These phenomenon seriously affect the response speed and the hydraulic pressure control .In order to realize the accurate hydraulic pressure control of I-EHB system, a new friction compensation control method is proposed based on LuGre dynamic friction model. And the theoretical design of adaptive control method is designed based on the feedback of the master cylinder pressure and the operating state of the system. Then the stability of the control method is proved by Lyapunov theorem. A co-simulation model is built with Matlab/Simulink and AMESim, so as to prove the validity of the control method.

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.

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.

On the dual-motor electric vehicle, which is driven by two electric motors mounted on the front and rear axles respectively, longitudinal dynamic control and electro-dynamic braking can be achieved by controlling the torque of front and rear axle motors respectively. Suspension displacement is related to the wheel torque, thus the pitch of vehicle body can be influenced by changing the torque distribution ratio. The pitch of the body has a great influence on the vehicle comfort, which occurs mainly during acceleration and braking progress. Traditionally active suspension is adopted to control the pitch of body. Instead, in this paper an ideal torque distribution strategy is developed to limit the pitch during acceleration and braking progress. This paper first explores the relationship between the torque distribution and the body pitch through the real vehicle test, which reveals the feasibility of the vehicle comfort promotion by optimizing the torque distribution coefficient.

In this paper we present a path following control design for a six-wheel skid-steering vehicle. Contrary to the common approaches that impose non-holonomic constraints, a dynamic vehicle model is established based on a pseudo-static tire model, which uses tire slip to determine tire forces. Our control system admits a modular structure, where a motion controller computes the reference vehicle yaw rate and reference vehicle speed and a dynamics controller tracks these signals. A robust nonlinear control law is designed to track the reference wheel speeds determined by the dynamics controller with proved stability properties. Saturated control techniques are employed in designing the reference yaw rate, which ensures the magnitude of the reference yaw rate does not violate the constraint from the ground-tire adhesion. The simulation results demonstrate the effectiveness of the proposed path following control design.