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In recent years, with the increase of traffic accidents and traffic jams, lane change, as one of the most important and commonly automatic driving operations for autonomous vehicles, is receiving attention in academia. It is considered to be one of the important solutions that play an important role in improving road traffic safety and efficiency. However, most existing lane-changing models are rule-based lane-changing models. These models only assume a one-direction impact of surrounding vehicles on the lane-changing vehicle. In fact, lane change is a process of mutual interaction between vehicles due to the complexity and uncertainty of the traffic environment. Moreover, the safety and efficiency of existing lane-changing decision algorithms need to be improved. In this paper, we proposed a multivehicle cooperative control approach with a distributed control structure to control the model.

Conventionally, the engines are calibrated under the assumption that engines will be made exactly to the prints, and all the engines from the same batch will be identical. However, engine-to-engine variations do exist which will affect the engine performances, and part-to-part variations, i.e., the tolerance, is an important factor leading to engine-to-engine variations. There are researches conducted on the influence of dimensional tolerances on engine performance, however, the impact of straightness, which is an important geometric tolerance, on lubrication is an unsolved issue. This study presents a systematic method to model the straightness and to analyze its effects on the friction loss. The bearing model is built based on elastohydrodynamic (EHD) theory. Meanwhile a novel modeling method to represent any form of straightness in three-dimensional space is proposed.

Aiming at the problem of weak communication, strong interference, cross-domain, and large-scale environment, it is difficult to achieve efficient decision-making and planning in the collaborative operation of intelligent groups. Based on the SOM algorithm, this paper proposes a dual-selection allocation and distributed vectorized trajectory planning. Form a collaborative planning algorithm that can be updated with high frequency and a rational decision-making mechanism. Provide technical support for collaborative search and detection of intelligent groups. At the same time, based on the principle of minimum consistency, this paper proposes a clock synchronization model under spatial coordination and conducts simulation experiments to verify it. The result proves the efficiency and practicability of the collaborative intelligent decision-making plan proposed in this paper.

For the purposes of on-line control, e.g., in an automatic driving system, or of closed-loop directional control simulation, an optimal preview artificial neural network (ANN) driver model based on error elimination algorithm(EEA) is built. Then the optimal preview times are discussed in high frequency range in this system. The simulation results of optimal preview ANN driver model and Error Elimination Algorithm driver model are compared under the condition of different vehicle speeds and paths, which shows that the proposed approach is efficient and reliable enough, particularly for driver-vehicle closed-loop system.

Owing to the stochastic nature of engine knock, determination of the knock limited spark angle (KLSA) is difficult in engine cycle simulation. Therefore, the state-of-the-art knock modeling is mostly limited to either merely predicting knock onset (i.e. auto-ignition of end gas) or combining a simple unburned mass fraction (UMF) model representative of knock intensity (KI). In this study, a newly developed KLSA model, which takes both predictions of knock onset and intensity into account, is firstly introduced. Multiple variables including the excess air ratio, EGR ratio, cylinder pressure and the end gas temperature are included in the knock onset model. Based on the auto-ignition theory of hot spots in end gas, both the energy density and heat release rate in hot spots are taken into consideration in the KI model.

In partially automated and conditionally automated vehicles, a part of the work of human drivers is replaced by the system, and the main source of safety risks is no longer system failures, but non-failure risks caused by insufficient system function design. The absence of unreasonable risk due to hazards resulting from functional insufficiencies of the intended functionality or by reasonably foreseeable misuse by persons, is referred to as the Safety Of The Intended Functionality. Drivers have the responsibility to supervise the automated driving system. When they don't agree with the operation behavior of the system, they will interfere with the instructions. However, this may lead to potential risks.

A novel magnetorheological fluid dual clutch (MRFDC) for electric vehicle transmission is proposed in this article. The structure was based on the MR fluid clutch and traditional dual clutch equipped on internal combustion engine vehicle. Therefore the MRFDC combines the advantages of MR fluid clutch and dual clutch transmission (DCT) to achieve high control accuracy and fast response. The structure of MRFDC was designed by Unigraphics (UG) three-dimensional (3D) modeling software. Then, finite element analysis (FEA) for magnetic field was conducted by ANSYS under different applied currents from 0.1A to 1A with 0.1A space to obtain the relation between the applied current and magnetic field. In this article, Herschel-Bulkley model is used to predict the MR fluid behavior because of the high shear rate of MR fluid.

An EV prototype, with all the wheels respectively driven by 4 inwheel motors, is developed, and undergoes a series of practical measurements and road tests. Based on the obtained vehicle parameters, a multi-body dynamics model is built by using SolidWorks and Adams/Car, and then validated by track test data. The virtual prototype is served as the control plant in simulation. An adaptive fractional order PID (A-FO-PID) controller is designed to enhance the handling and stability performance of the EV. Considering the model uncertainties, e.g. the variation in body mass distribution and the consequent change in yaw moment of inertial, a Parameter Self-Adjusting Differential Evolution (PSA-DE) algorithm is adopted for tuning the controller parameters, i.e. KP, KI, KD, λ and μ. As a modification of traditional DE algorithm, the so-called Variance of Population’s Fitness is utilized to evaluate the diversity of the population.

Vehicle lateral states such as lateral distance at a preview point and heading angle are indispensable for lane keeping control systems, and such states are normally estimated by fusing signals from an onboard vision system and inertial sensors. However, the sampling rates and measurement delays are different between the two kinds of sensing devices. Most of the conventional methods simply neglect measurement delay and reduce sampling rate of the estimator to adapt to the slow sensors/devices. However, the estimation accuracy is deteriorated, especially considering the delay of visual signals may not be constant. In case of electric vehicles, the actuators for steering and traction are motors that have high control frequency. Therefore, the frequency of vehicle state feedback may not match the control frequency if the estimator is infrequently updated. In this paper, a multi-rate estimation algorithm based on Kalman filter is proposed to provide lateral states with high frequency.

The present work aims at the reduction of transmission delay at the level of AFDX ES (Avionics Full Duplex Switched Ethernet End-Systems). To this end, two approaches, namely Network Calculus and response time analysis (RTA), are employed in the computation of upper bound delay. To evaluate the delay regarding different scheduling policies, the arrival curve of the flow on output of ES is established for given traffic shaping algorithm and service mode. Computational analysis shows that Bandwidth Allocation Gap (BAG) based scheduling is the optimal policy at the level of AFDX ES, which leads to the tightest output arrival curve among all possible scheduling policies. BAG-based scheduling consists in assigning higher priority to virtual links with smaller BAG thus corresponding to the well known Rate-Monotonic Algorithm. Furthermore, schedulability criterion are established based on RTA.

Electric vehicles (EVs) have become a hot research topic due to the petroleum crisis and air pollution issues, and Hybrid EVs (HEVs) equipped with engines and motors are popular nowadays due to their advantage over Pure EVs. The energy distribution between the engine and the motor is the major task of the control strategy or energy management for HEVs. Rule-based and optimization-based approaches are developed in this area, but not much work has been done for large-size super-capacitor (SC) equipped HEVs, like Hybrid buses. In this paper, a new optimization-based control strategy for a hybrid bus equipped with SCs as the energy regeneration system is presented. Considering the driving patterns of a bus that is of frequent accelerations and decelerations, it is proposed to characterize each time instant by its speed and acceleration, and the energy distribution is optimized based on these two state variables.

An energy-regenerative vehicle suspension is proposed. Permanent-magnet direct-current motors are utilized as the active actuators in automotive suspension. The significant characteristic of the suspension is that vibration energy from the road excitation can be regenerated and transformed into electric energy while good suspension performance can be maintained. The modeling of electrical suspension system has been completed and simulated in Matlab/Simulink. The motor actuator working as a generator is proved to maintain the performance of vibration control and energy-regeneration. The prototype of motor actuator is designed and made. The vibration absorption and regeneration performances are verified by full-vehicle experiments.

A new kind of integrated semi-track air-cushion pitch controller is proposed in this paper. The controller first compute the target working point based on a weighed function, which is the combination of optimal power consumption and pitch angle control demand. Then the sequential quadratic programming algorithm distributes the general target values to specific control values. The performance of the controller is verified through co-simulation between Matlab/Simulink and ADAMS/View. The simulation results show the effectiveness of the control algorithm and the correctness of the choice in physical configuration with two air cushions for vehicle body pitch control.

The vehicle chassis integrated control system can improve the stability of vehicles under extreme conditions using tire force allocation algorithm, in which, the nonlinearity and uncertainty of tire-road contact condition need to be taken into consideration. Thus, An MPC (Model Predictive Control) controller is designed to obtain the additional steering angle and the additional yaw moment. By using a robust optimal allocation algorithm, the additional yaw moment is allocated to the slip ratios of four wheels. An SMC (Sliding-Mode Control) controller is designed to maintain the desired slip ratio of each wheel. Finally, the control performance is verified in MATLAB-CarSim co-simulation environment with open-loop manoeuvers.

Accuracy of heat flux models is critical to clutch design in case of excessive temperatures due to large amounts of friction heat generated in the narrow space. Pressure distribution on the clutch friction interface is an important factor affecting heat flux distribution, thus affecting temperature distribution. In this paper, an experiment is conducted to obtain the pressure distribution for one typical dry clutch equipped with a set of diaphragm spring. Considering that the frictional interface is in contact, this study makes use of pressure sensitive film and acquires data based on image processing techniques. Then a polynomial mathematical model with dimensionless parameters is developed to fit the pressure distribution on the friction disc. After that, the proposed pressure model is applied to a thermal model based on finite element method. In addition, two conventional thermal models (i.e., uniform heat flux model and uniform pressure model), are implemented for comparison.

With the development of economic globalization, the speed of development of container terminals is also very rapid. Under the pressure brought by the surge in throughput, the unmanned and intelligent terminals will become the future development direction of terminals. As the cornerstone of the unmanned terminal, the positioning technology provides the most basic position information for system scheduling, path planning, real-time correction, and loading and unloading. Therefore, this paper is aimed to design a low-cost, high-precision, and easy-to-maintain unmanned dock positioning system in order to better solve the problem of unmanned dock positioning. The main research content of this paper is to design a positioning algorithm for unmanned terminal Automated Guided Vehicle (AGV) based on single-line lidar, including point cloud data acquisition, background filtering, point cloud clustering, vehicle position extraction, and result optimization.

Visual Place Recognition (VPR) excels at providing a good location prior for autonomous vehicles to initialize the map-based visual SLAM system, especially when the environment changes after a long term. Condition change and viewpoint change, which influences features extracted from images, are two of the major challenges in recognizing a visited place. Existing VPR methods focus on developing the robustness of global feature to address them but ignore the benefits that local feature can auxiliarily offer. Therefore, we introduce a novel hierarchical place recognition method with both global and local features deriving from homologous VLAD to improve the VPR performance. Our model is weak supervised by GPS label and we design a fine-tuning strategy with a coupled triplet loss to make the model more suitable for extracting local features.