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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.

Harmony-with-traffic refers to the ability of autonomous vehicles to maximize the driving benefits such as comfort, efficiency, and energy consumption of themselves and the surrounding traffic during interactive driving under traffic rules. In the test of harmony-with-traffic, one or more background vehicles that can respond to the driving behavior of the vehicle under test are required. For this purpose, the functional requirements of car-following model for harmony-with-traffic evaluation are analyzed from the dimensions of test conditions, constraints, steady state and dynamic response. Based on them, an interactive car-following model (ICFM) is developed. In this model, the concept of equivalent distance is proposed to transfer lateral influence to longitudinal. The calculation methods of expected speed are designed according to the different car-following modes divided by interaction object, reaction distance and equivalent distance.

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.

In recent years, intelligent driving technology is being extensively studied. This paper proposes a path planning method for perpendicular parking based on vehicle kinematics model using MPC optimization, which aims to solve the perpendicular parking task. Firstly, in the case of any initial position and orientation of the vehicle, judging whether the vehicle can be parked at one step according to the location of the parking place and the width of the lane, and then calculating the starting position for parking, and use the Bezier curve to connect the initial position and the starting position. Secondly, reference parking path is calculated according to the collision constraints of the parking space. Finally, because the parking path based on the vehicle kinematics model is composed of circle arcs and straight lines, the curvature of the path is discontinuous. The reference parking path is optimized using Model Predictive Control (MPC).

A tractor-trailer vehicle (TTV) consists of an actuated tractor attached with several full trailers. Because of its nonlinear and noncompleted constraints, it is a challenging task to avoid collisions for path planner. In this paper, we propose an efficient method to plan an optimal trajectory for TTV to reach the destination without any collision. To deal with the complicated constraints, the trajectory planning problem is formulated as an optimal control problem uniformly, which can be solved by the interior point method. A novel incremental optimization solving algorithm (IOSA) is proposed to accelerate the optimization process, which makes the number of trailers and the size of obstacles increase asynchronously. Simulation experiments are carried out in two scenarios with static obstacles. Compared with other methods, the results show that the planning method with IOSA outperforms in the efficiency.

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.

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.

Recently, electro-hydraulic brake systems (EHB) has been developed to take place of the vacuum booster, having the advantage of faster pressure build-up and continuous pressure regulation. In contrast to the vacuum booster, the pressure estimation for EHB is worth to be studied due to its abundant resource (i.e. electric motor) and cost-effective benefit. This work improves an interconnected pressure estimation algorithm (IPEA) based on actuator characteristics by introducing the vehicle dynamics and validates it via vehicle tests. Considering the previous IPEA as the prior pressure estimation, the wheel speed feedback is used for modification via a proportional-integral (PI) observer. Superior to the IPEA based on actuator characteristics, the proposed PEA improves the accuracy by more than 20% under the mismatch of pressure-position relation.

A real-time obstacle-avoidance trajectory planner for on-road autonomous vehicle is proposed in this paper. A cubic B spline core is parametric to generate path with continuous curvature as well as taking the extreme curvature limited by steer system into account. By sampling the target sets via offsetting along the reference path, lots path sets are produced with same heading. As embedded with collision checker and path evaluator, a path selector could pick out the best one to planning speed profile for coupling trajectory to track. Finally, according to the change of path curvature, the speed profile scheduler addresses the conflict of curvature and deceleration. Referencing to the ISO3888-2:2011, a collision avoidance scenario was design to validate the planner. The test results of ten cycles test illustrate that the planner has high enough real-time performance as mean plan time less than 100ms with success rate about 100%.

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.

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.

Velocity is one of the most important inputs of active safety systems such as ABS, TCS, ESC, ACC, AEB et al. In a distributed drive electric vehicle equipped with four in-wheel motors, velocity is hard to obtain due to all-wheel drive, especially in wheel slipping conditions. This paper focus on longitudinal velocity estimation of the distributed drive electric vehicle. Firstly, a basic longitudinal velocity estimation method is built based on a typical Kalman filter, where four wheel speeds obtained by wheel speed sensors constitute an observation variable and the longitudinal acceleration measured by an inertia moment unit is chosen as input variable. In simulations, the typical Kalman filter show good results when no wheel slips; when one or more wheels slip, the typical Kalman filter with constant covariance matrices does not work well. Therefore, a gain matrix adjusting Kalman filter which can detect the wheel slip and cope with that is proposed.

This paper presents several pressure estimation algorithms (PEAs) for a decoupled electro-hydraulic brake system (EHB), which is driven by an electric motor + reduction gear. Most of the pressure control solutions are based on standard pressure-based feedback control, requiring a pressure signal. Although the pressure sensor can produce the pressure feedback signal, it will increase cost and enlarge installation space. The rotation angle of electric motor is available by the built-in sensor, so the pressure can be estimated by using the rotation angle. Considering the typical nonlinearities (i.e. friction, pressure-position relationship) and uncertainties (i.e. disturbance caused by friction model), the estimation-oriented model is established. The LuGre model is selected to describe the friction, and the pressure-position relationship is fitted by a quadratic polynomial.

In the hybrid brake system of electric vehicle, due to the limitation of the motor braking force when the motor is at high speed and the failure of the regenerative braking force when the motor is at low speed, there are three transitional conditions in hybrid braking: the hydraulic brake system intervenes the braking, the hydraulic brake system withdraws the braking and the regenerative braking force withdraws the braking. Due to the response speed of the hydraulic system is slower than that of the motor, there is a large braking impact (the derivative of braking deceleration) in the transitional conditions of hybrid braking, which deteriorates the smoothness and comfort in braking. Aiming at the impact caused by the poor cooperation between the hydraulic braking force and the motor braking force, a coordinated strategy of double closed-loop feedback and motor force correction is proposed in this paper.

Vehicle sideslip angle is significant for electronic stability control devices and hard to estimate due to the nonlinear and uncertain vehicle and tire dynamics. In this paper, based on the two track vehicle dynamic model considering the tire pneumatic trail variation, the vehicle sideslip angle estimation method was proposed. First, the extra steering angle of each wheel caused by kinematics and compliance characteristics of the steering system and suspension system was analyzed. The steering angle estimation method was designed. Since the pneumatic trail would vary with different tire slip angle, distances between the center of gravity (COG) and front&rear axle also change with the tire slip angle. Then, based on the dynamic pneumatic trail and estimated steering angle, we modified the traditional two track vehicle dynamic model using a brush tire model. This model matches the vehicle dynamics more accurately.

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 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.

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.