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This paper addresses the issues of long-term signal loss in localization and cumulative drift in SLAM-based online mapping and localization in autonomous valet parking scenarios. A GPS, INS, and SLAM fusion localization framework is proposed, enabling centimeter-level localization with wide scene adaptability at multiple scales. The framework leverages the coupling of LiDAR and Inertial Measurement Unit (IMU) to create a point cloud map within the parking environment. The IMU pre-integration information is used to provide rough pose estimation for point cloud frames, and distortion correction, line and plane feature extraction are performed for pose estimation. The map is optimized and aligned with a global coordinate system during the mapping process, while a visual Bag-of-Words model is built to remove dynamic features.

Driver steering feature clustering aims to understand driver behavior and the decision-making process through the analysis of driver steering data. It seeks to comprehend various steering characteristics exhibited by drivers, providing valuable insights into road safety, driver assistance systems, and traffic management. The primary objective of this study is to thoroughly explore the practical applications of various clustering algorithms in processing driver steering data and to compare their performance and applicability. In this paper, principal component analysis was employed to reduce the dimension of the selected steering feature parameters. Subsequently, K-means, fuzzy C-means, the density-based spatial clustering algorithm, and other algorithms were used for clustering analysis, and finally, the Calinski-Harabasz index was employed to evaluate the clustering results. Furthermore, the driver steering features were categorized into lateral and longitudinal categories.

The rapid development of city traffic makes the driving conditions faced by vehicles increasingly complex. The drive-by-wire chassis vehicle has the characteristics of four-wheel independent steering, four-wheel independent drive and four-wheel independent braking, which has become a current research hotspot because that can meet various complex working conditions. However, it is precisely because of the high degree of controllability of the drive-by-wire chassis that the research on the control strategy has become difficult. In this paper, an integrated control strategy based on the hierarchical algorithm framework is designed for the drive-by-wire chassis vehicle, which includes a centralized control layer, a tire force distribution layer and an actuator control layer.

To address the current situation of the limited driver-vehicle cooperative steering actuation structure, this paper proposes a feasible driver-vehicle shared steering control actuation architecture based on the differential steering. Firstly, a shared steering execution architecture is established, which contains traditional steering system controlled by human driver and differential steering system acting as the automatic execution system. In this paper, a specific driver-vehicle shared control architecture is established with the front-wheel hub motor-based differential steering system and a single-view angle based human driver model. Then, an upper-level sliding mode controller for path tracking is developed and implemented as the automatic steering system, and the driver-vehicle shared control is achieved by the proposed non-cooperative game model.

It’s hard to achieve complete self-driving using hand-crafting generalized decision-making rules, while the end-to-end self-driving system is low in complexity, does not require hand-crafting rules, and can deal with complex situations. Modular-based self-driving systems require multi-task fusion and high-precision maps, resulting in high system complexity and increased costs. In end-to-end self-driving, we usually only use camera to obtain scene status information, so image processing is very important. Numerous deep learning applications benefit from multi-task learning, as the multi-task learning can accelerate model training and improve accuracy with combine all tasks into one model, which reduces the amount of calculation and allows these systems to run in real-time. Therefore, the approach of obtaining rich scene state information based on multi-task learning is very attractive. In this paper, we propose an approach to multi-task learning for semantics, geometry and motion.

With the rise of intelligent transportation systems around the world, research on automobile active safety technology has gained widespread attention. Autonomous Emergency Braking (AEB) which can avoid or mitigate collision by active braking has become a hot research topic in the field of automobile. However, there are some limitations in the present AEB collision avoidance strategy, including lack of effective identification of road adhesion conditions, mismatch of active braking system parameters and imperfection of target vehicle motion information, which leads to poor collision avoidance performance on low adhesion coefficient road surface and intervention with the normal driving operation of the driver. A new collision avoidance strategy for AEB is proposed in this paper.

This paper presents a HiL test bench specifically designed for closed-loop testing of the monocular-vision based ADAS sensors, whereby the animated pictures of the virtual scene is calibrated and projected onto a 120-degree circular screen, such that the camera sensor installed has the same vision as the observation of the real-world scene. A high-fidelity AEBs model is established and deployed in the real-time target of the HiL system, making intervention decisions based on the instance-level detection information transmitted from the physical sensor. By referring to the 2018 edition of the C-NCAP testing protocol, the HiL tests of the rear-end collision scenarios is performed to investigate the performance and characteristics of the longitudinal-motion sensing of the sensor sample under test.

This essay aims to develop an electrically controlled steering test bench and lay a solid foundation for the development of steering system. The first part mainly introduces the function, structure and working principle of the test bench. For the hardware system, it includes the steering system, fixture, sensors as well as a frameless disk motor for carrying out automatic motor input, and a dual linear motor system selected as the road resistance simulation actuator. As for the software, MATLAB/Simulink, CarSim, RTI and ControlDesk are used. Therefore, with the help of real-time simulation platform, researchers can not only build control strategy and dynamic model but also control the experiment and tune parameters in real-time. The second part of the essay aims to identify both electric and mechanical parameters of R-EPS system by carrying out tests on the proposed test bench. As parameters are successfully identified, the feasibility of the test bench is also verified.

With rapid development of the automobile industry and the growing maturity of the automotive electronic technologies, the distributed-traction electric vehicle with four-wheel-distributed steering/braking/traction systems is regarded as an important development direction. With its unique chassis structure, it is the ideal benchmark platform used to evaluate active safety systems. The distributed-traction electric vehicle with four-wheel-distributed steering system is essentially full drive-by-wire vehicle. With its flexible chassis layout and high control degrees-of-freedom, the full drive-by-wire electric vehicle acted as a kind of redundant system is an ideal platform for the research of integrated control. In this treatise, the longitudinal dynamics of the electric vehicle as well as its lateral and yaw motions are controlled simultaneously.

Distributed steering vehicle uses four steering motors to achieve four wheel independent steering. The steering angle of each wheel can be distributed respectively. The tire cornering characteristics are added to traditional steering model to study the angle allocation control algorithm. Using the constraint relation between tire slip angle, vehicle speed, yaw rate and front steering angle, and connecting with the ideal ackermann steering relationship, steering angle allocation of front wheel independent steering and four wheel independent steering is derived. Then simulated analysis is carried out to demonstrate the efficiency of the algorithm. Improvements in tire wear condition are determined by evaluating the optimization in tire lateral force, and the vehicle stability is determined by vehicle slip angle. The simulation results show that the angle allocation control algorithm has a good effect on improving tire wear condition and enhancing the stability of vehicle.

The distributed driving electric vehicle, uses four in-wheel motors as distributed power sources is a typical over-actuated system. Thus, this kind of vehicle has better stability potential and fault tolerance than the conventional one. In this paper, the general structure of fault-tolerance control (FTC) system based on control allocation is analyzed. And a reconfigurable driving force allocation strategy is proposed to ensure the trajectory tracking and stability when some motors’ faults occur. Both the constraints of tire force and actuators are taken into consideration. With motors’ faults treated as the constraints of actuators, FTC is integrated. For validation, the proposed allocation strategy is simulated in co-simulation environment based on Carsim and Matlab/Simulink.

According to the vehicle’s driving conditions, electronically controlled air suspension (ECAS) systems can actively adjust the height of vehicle body, so that better ride comfort and handling stability will be achieved, which can’t be realized by traditional passive suspension. This paper presents a design and implementation of ECAS controller for vehicle. The controller is aimed at adjusting the static and dynamic height of the vehicle. To exactly track the height of the vehicle and satisfy the control demand of air suspension, a height sensor decoding circuit based on the inductance sensor is designed. Based on it, a new height control algorithm is adopted to achieve rapid and precise control of vehicle height. To verify the function of the designed controller and the proposed height control algorithm, an air spring loading test bench and an ECU-in-loop simulation test bench are respectively established.

The driving range of the electric vehicle (EV) greatly restricts the development of EVs. The vehicles waste plenty of energy on account of automobiles frequently braking under the city cycle. The regenerative braking system can convert the braking kinetic energy into the electrical energy and then returns to the battery, so the energy regeneration could prolong theregenerative braking system. According to the characteristics of robustness in regenerative braking, both regenerative braking and friction braking based on fuzzy logic are assigned after the front-rear axle’s braking force is distributed to meet the requirement of braking security and high-efficient braking energy regeneration. Among the model, the vehicle model and the mechanical braking system is built by the CRUISE software. The paper applies the MATLAB/SIMULINK to establish a regenerative braking model, and then selects the UEDC city cycle for model co-simulation analysis.

A new electric power steering system (EPS) dynamic friction model based on normalized Bouc-Wen model is given, as well as its structure form and model features. In addition, experimental method is used to identify corresponding parameters. In order to improve road feel feedback, this paper analyzes the shortcoming of traditional constant friction compensation control method and proposes a variable friction compensation control method which the friction compensation current changes according to the assist characteristic gain. Through simulation and real vehicle test verification, variable friction compensation control method eliminates the effect of basic assist characteristic, and improves the driver’s road feel under high speed.

This paper presents a fault-tolerant control (FTC) algorithm for four-wheel independently driven and steered (4WID/4WIS) electric vehicle. The Extended Kalman Filter (EKF) algorithm is utilized in the fault detection (FD) module so as to estimate the in-wheel motor parameters, which could detect parameter variations caused by in-wheel motor fault. A motion controller based on sliding mode control (SMC) is able to compute the generalized forces/moments to follow the desired vehicle motion. By considering the tire adhesive limits, a reconfigurable control allocator optimally distributes the generalized forces/moments among healthy actuators so as to minimize the tire workloads once the actuator fault is detected. An actuator controller calculates the driving torques of the in-wheel motors and steering angles of the wheels in order to finally achieve the distributed tire forces. If one or more in-wheel motors lose efficacy, the FD module diagnoses the actuator failures first.

For the vehicles equipped with Electric Power Steering (EPS) system, the friction and damping effect brought by assisted motor and worm gear mechanism influence the return ability and handing stability. In order to eliminate the impacts, it is necessary to add return-to-center control in EPS control strategy. This paper proposes a practical active return-to-center control strategy with steering wheel angle signals based on return state identification. In the strategy, the return state of the steering system is identified quickly according to the two signals steering wheel angle velocity and steering wheel torque. Only under return state, a double closed-loop PID control strategy is carried out to calculate a compensation current to improve the return ability. For validating the proposed strategy, a fine EPS model including BLDC assisted motor is built based on carsim and simulink co-simulation platform.

Electro-hydraulic power steering system (EHPS) maintains the advantages of Hydraulic power steering system (HPS) and Electric power steering system (EPS).It is even more superior than this two. In the foreseeable future, this system will have a certain development space. Assistant characters analysis was carried out in this paper. Control strategy based on steering states and feedback control strategy were designed too. Besides, aiming at the emergency steering conditions, steering angular velocity additional controlling strategy was brought out. Under emergency steering conditions, steering angular velocity additional controlling strategy will be applied. Additional steering moment will be calculated to ensure the assistant follow steering rapidly.

Electronically controlled air suspension (ECAS) systems have been widely used in commercial vehicles to improve the ride comfort and handling stability of vehicles, as it can adjust vehicle height according to the driving conditions and the driver's intent. In this paper, the vehicle height adjustment process of ECAS system is studied. A mathematical model of vehicle height adjustment is derived by combining vehicle dynamics theory and thermodynamics theory of variable mass system. Reasons lead to the problems of “over-charging”, “over-discharging” and oscillation during the process of height adjustment are analyzed. In order to solve these problems, a single neuron proportional-integral-derivative (PID) controller is proposed to realize the accurate control of vehicle height. By simulation and semi-physical rig test, the effectiveness and performance of the proposed control algorithm are verified.

This paper presents an integrated chassis controller with multiple hierarchical layers for 4WID/4WIS electric vehicle. The proposed systematic design consists of the following four parts: 1) a reference model is in the driver control layer, which maps the relationship between the driver's inputs and the desired vehicle motion. 2) a sliding mode controller is in the vehicle motion control layer, whose objective is to keep the vehicle following the desired motion commands generated in the driver control layer. 3) By considering the tire adhesive limits, a tire force allocator is in the control allocation layer, which optimally distributes the generalized forces/moments to the four wheels so as to minimize the tire workloads during normal driving. 4) an actuator controller is in the executive layer, which calculates the driving torques of the in-wheel motors and steering angles of the four wheels in order to finally achieve the distributed tire forces.

Electric Power Steering System (EPS) can directly provide auxiliary steering torque via a motor. The motor and the reducer in mechanical system will make the friction torque in steering system larger, as a result, the ability of steering returning will be reduced. Therefore, during the design of EPS system control strategy, an extra active return-to-middle control strategy is needed. For the fact that most of the low-end vehicles equipped with EPS system do not have a steering wheel angle sensor, a control strategy has to work without the datum of steering wheel angle. This paper proposes an active return-to-middle control method without steering wheel angle sensor, based on the estimated aligning torque which is converted to the pinion, and expounds how to determine the steering system current motion state in detail. This control method will work just during the turning condition, so it has no effect on the EPS basic assist characteristics.