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Traffic congestions are increasingly severe in urban areas, especially at the merging areas of the ramps and the arterial roads. Because of the complex conflict relationship of the vehicles in ramps and arterial roads in terms of time-spatial constraints, it is challenging to coordinate the motion of these vehicles, which may easily cause congestions at the merging areas. The connected and automated vehicles (CAVs) provides potential opportunities to solve this problem. A centralized merging control method for CAVs is proposed in this paper, which can organize the traffic movements in merging areas efficiently and safely. In this method, the merging control model is built to formulate the vehicle coordination problem in merging areas, which is then transformed to the discrete nonlinear optimization form. A simulation model is built to verify the proposed method.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

A novel fault tolerant brake strategy for In-wheel motor driven electric vehicles based on integral sliding mode control and optimal online allocation is proposed in this paper. The braking force distribution and redistribution, which is achieved in online control allocation segment, aim at maximizing energy efficiency of the vehicle and isolating faulty actuators simultaneously. The In-wheel motor can generate both driving torque and braking torque according to different vehicle dynamic demands. In braking procedure, In-wheel motors generate electric braking torque to achieve energy regeneration. The strategy is designed to make sure that the stability of vehicle can be guaranteed which means vehicle can follow desired trajectory even if one of the driven motor has functional failure.

This paper developed a control system for the auxiliary power unit (APU) in off-road series hybrid electric special vehicle. A control system configuration was designed according to the requirements of the high voltage system in series hybrid electric special vehicle. Then optimal engine operating areas were defined. A gain scheduling engine speed PI controller was designed based on these areas. A closed loop voltage regulator was designed for the synchronous generator. The proposed control system was first validated on an APU control test bench. The test results showed the control system guaranteed the diesel APU good dynamic response characteristics while remaining stable output voltage. Finally, the APU control system was implemented on a diesel APU in an off-road series hybrid electric vehicle and a road test was conducted. The road test results showed the APU control system promised good performance in both vehicle dynamics and vehicle high voltage system.

A HIL simulator for developing vehicle adaptive cruise control systems is presented in this paper. The xPC target is used to establish real-time simulation environment. The simulator is composed of a virtual vehicle model, real components of an ACC system like ECU, electronic throttle and braking modulator, a user interface to facilitate simulation, and brake and accelerator pedals to make interactive driver inputs easier. The vehicle model is validated against data from field test. Tests of an ACC controller in the real-time are conducted on the simulator.

As vehicle systems constantly grow in complexity and are subject to higher demands on performance, distributed control has become mainstream application in automotive industry. In a distributed control system, communication network connecting local controllers plays an important role. In this article, a fuel cell bus control system under development is introduced first. And then, traditional CAN and TTCAN network are analyzed for real-time performance respectively and TTCAN is chosen for its superiority. Subsequently, a TTCAN network is designed and implemented. Finally, a test platform for TTCAN network is devised and relevant platform experiments and on-board validation on the network are discussed.

The electronic control system of the variable nozzle turbocharger (VNT) was designed. The actuator is the electro-hydraulic servo proportional solenoid. The signals of the engine pedal position sensor, the engine speed sensor, the boost pressure sensor, the intake air temperature sensor, and the ambient pressure sensor are sampled and filtered. The engine working condition is estimated. The control algorithm was designed as the closed-loop feedback digital PI control together with the open-loop feed forward control. The gain-scheduled PI control method is applied to improve the robustness. The control system was calibrated at the turbocharger test bench and the engine test bench. The results indicate the designed control system has good performance for the boost pressure control under the steady and transient conditions.

This paper presents a preliminary design and analysis of an optimal energy management and control system for a power-split hybrid electric vehicle (HEV) using hybrid dynamical control system theory and design tools. The hybrid dynamical system theory is applied to formulate HEV powertrain dynamical system in which the interactions of discrete and continuous dynamics are involved. The Sequential Quadratic Programming (SQP) method is applied to optimize power distribution. An improved dynamic programming method is employed to determine the optimal power distribution and the vehicle operating mode transitions.

It's important to monitor the longitudinal friction at the tire/road interface for automotive dynamic control systems like ABS and ASR. Of all the tire friction models the empirical model provides a good illustration on longitudinal wheel forces. An improved exponential friction model based on vehicle driving states was proposed in this paper, the model can monitor the friction characteristics between the tire and road surface for longitudinal braking. Its validity was proven using experiments and comparison with the Pacejka Magic Formula (MF) model and others.

The new concept of “human-machine shared control” provides an amazing thinking to enhance driving safety, which has been attracted a great deal of research effort in recent years. However, little attention has been paid to the nonlinearity of the shared control system brought by the tire, which significantly influences the control performance under extreme driving conditions. This paper presents a novel shared steering torque control scheme to model the human-machine steering torque interaction near the vehicle’s handling limit, where both driver and driver assistance system (DAS) are exerting steering torque to maneuver the vehicle. A six-order driver-vehicle dynamic system is presented to elaborate the relationship between steering torque input and vehicle lateral motion response. Particularly, we use a piecewise affine (PWA) method to approximate the tire nonlinearity.

The coordinated control of stability and steering systems in collision avoidance steering evasion has been widely studied in vehicle active safety area, but the studies are mainly aimed at autonomous vehicle without driver or conventional combustion engine vehicle. This paper focuses on the control of hybrid vehicle integrated with rear hub in emergency steering evasion situation, and considering the driver’s characteristics. First, the mathematics model of vehicle dynamics and driver has been given. Second, based on the planned steering evasion path, the model predictive control method is presented for achieving higher evasion path tracking accuracy under driver’s steering input. The prediction model includes an adaptive preview distance driver model and a vehicle dynamics model to predict the driver input and the vehicle trajectory.

A feasible method was developed to reconstruct the three-dimensional flame surface of SI combustion based on 2D images. A double-window constant volume vessel was designed to simultaneously obtain the side and bottom images of the flame. The flame front was reconstructed based on 2D images with a slicing model, in which the flame characteristics were derived by slicing flame contour modeling and flame-piston collision area analysis. The flame irregularity and anisotropy were also analyzed. Two different principles were used to build the slicing model, the ellipse hypothesis modeling and deep learning modeling, in which the ellipse hypothesis modeling was applied to reconstruct the flame in the optical SI engine. And the reconstruction results were analyzed and discussed. The reconstruction results show that part of the wrinkled and folded structure of the flame front in SI engines can be revealed based on the bottom view image.

Accurate identification of driver’s braking intention is essential in advanced driver assistance system and can make the driving process more comfortable and trustworthy. In this paper, a novel method for driver braking intention identification in cut-in scenarios was proposed by using driver’s gaze information and motion information of cut-in vehicles. Firstly, a "looking in and looking out" experimental platform including three eye-tracking cameras and one front-view camera was built to collect driver's gaze information and the vehicle motion information. Secondly, driver’s gaze features and motion features of cut-in vehicles were selected and the braking intention identification performance of several decision tree-based ensemble learning algorithms was compared. Thirdly, the feature importance was analyzed by using SHAP (SHapley Additive exPlanations) values. This novel method of braking intention identification makes full use of in-vehicle camera sensors.

A vehicle dynamics stability control system based on integrated-electro-hydraulic brake (I-EHB) system with hierarchical control architecture and nonlinear control method is designed to improve the vehicle dynamics stability under extreme conditions in this paper. The I-EHB system is a novel brake-by-wire system, and is suitable to the development demands of intelligent vehicle technology and new energy vehicle technology. Four inlet valves and four outlet valves are added to the layout of a conventional four-channel hydraulic control unit. A permanent-magnet synchronous motor (PMSM) provides a stabilized high-pressure source in the master cylinder, and the four-channel hydraulic control unit ensures that the pressures in each wheel cylinder can be modulated separately at a high precision. Besides, the functions of Anti-lock Braking System, Traction Control System and Regenerative Braking System, Autonomous Emergency Braking can be integrated in this brake-by-wire system.

Safety of buses is crucial because of the large proportion of the public transportation sector they constitute. To improve bus safety levels, especially to avoid driver error, which is a key factor in traffic accidents, we designed and implemented an intelligent bus called iBus. A robust system architecture is crucial to iBus. Thus, in this paper, a novel self-driving system architecture with improved robustness, such as to failure of hardware (including sensors and controllers), is proposed. Unlike other self-driving vehicles that operate either in manual driving mode or in self-driving mode, iBus offers a dual-control mode. More specifically, an online hot standby mechanism is incorporated to enhance the reliability of the control system, and a software monitor is implemented to ensure that all software modules function appropriately. The results of real-world road tests conducted to validate the feasibility of the overall system confirm that iBus is reliable and robust.

The ability of actively adjusting attitude provides a great advantage for those vehicles used in special environments such as off-road environment with extreme terrains and obstacles. It can improve vehicles’ stability and performance. This paper proposes an attitude control system for realizing the active attitude adjustment and vehicle motion control in the same time. The study is based on a vehicle with six wheel independent drive and six independent suspensions (6WIDIS), which is a kind of unmanned vehicle with six in-wheel drives and six independent hydro pneumatic suspensions. With the hydro- pneumatic suspensions, the vehicle’s attitude can be actively adjusted. This paper develops a centralized- distributed control strategy with attitude information obtained by multi-sensor fusion, which can coordinate the complex relationship among the six wheels and suspensions. The attitude control system consists of three parts.

According to the statistics of National Highway Traffic Safety Administration, driver’s cognitive distraction, which is usually caused by drivers using mobile phones, has become one of the main causes of traffic accidents. To solve this problem and guarantee the safety of man-vehicle-road system, the most critical work is to improve the accuracy of driver’s cognitive state detection. In this paper, a novel driver’s cognitive state detecting method based on LightGBM (Light Gradient Boosting Machine) is proposed. Firstly, cognitive distraction experiments of making calls are carried out on a driving simulator to collect vehicle states, eye tracking and EEG (electron encephalogram) data simultaneously and feature extraction is conducted. Then a classifier considering road and individual characteristics used for detecting cognitive states is trained based on LightGBM algorithm, with 3 predefined cognitive states including concentration, ordinary distraction and extreme distraction.

This work presents a multi-objective adaptive cruise control (ACC) system via deep reinforcement learning (DRL). During the control period, it quantitatively considers three indexes: tracking accuracy, riding comfort, and fuel economy. The system balances contradictions between different indexes to achieve the best overall control results. First, a hierarchical control architecture is utilized, where the upper level controller is synthesized under DRL framework to give out the vehicle desired acceleration. The lower level controller executes the command and compensates vehicle dynamics. Then, four state variables that can comprehensively determine the car-following states are selected for better convergence. Multi-objective reward function is quantitatively designed referring to the evaluation indexes, in which safety constraints are considered by adding violation penalty. Thereafter, the training environment which excludes the disturbance of preceding car acceleration is built.

Due to the extended range hybrid vehicle powertrain system having multivariable and non-linear characters, this paper proposed a real-time simulation development platform scheme based on model design. First, a segmented energy management strategy (thermostat + power following) was proposed, which aims to improve the engine operation efficiency and reduce the losses during both charging and discharging. Second, the offline simulation model of the extended range hybrid vehicle powertrain system is established, which can realize the control function and meet the requirements of the real vehicle. Third, the hardware in the loop simulation platform of the vehicle controller is established, and the vehicle control program can run correctly in the real-time controller. The test of the offline simulation by Matlab/Simulink and the controller’s hardware in the loop (HIL) test are completed.

An intelligent connected vehicle (ICV) swarm system that includes N vehicles is considered. Based on the special properties of potential functions, a kinematic model describing the swarm performances is proposed, which allows all vehicles to enclose the tracking target and show both tracking and formation characteristics. Treating the performances as the desired constraints, the analytical form of constraint forces can be obtained inspired by the Udwadia-Kalaba approaches. A special approach of uncertainty decomposition to deal with uncertain interferences is proposed, and a switching-type robust control method is addressed for each vehicle agent in the swarm system. The features and validity of the addressed control are demonstrated in the numerical simulations.