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Traffic accidents avoidance is one of the main advantages for automated vehicles. As one of the main causes of vehicle collision accidents, lane changing of the ego vehicle in case that the obstacle vehicles appear in the blind spot with uncertain motion intentions is one of the main goals for the automated vehicle. An intention-aware lane changing collision assistance strategy basing on traffic situation assessment in the complex traffic scenarios is proposed in this paper. Typical Regions of Interest (ROI) within the detection range of the blind spots are selected basing on the road topology structures and state space consisting of the ego vehicle and the obstacle vehicles. Then the motion intentions of the obstacle vehicles in ROI are identified basing on Gaussian Mixture Models (GMM) and the corresponding motion trajectories are predicted basing on the state equation.

Lane detection and tracking play a key role in autonomous driving, not only in the LKA System but help estimate the pose of the vehicle. While there has been significant development in recent years, traditional outdoor SLAM algorithms still struggle to provide reliable information in challenging dynamic environments such as lack of roadside landscape or surrounding vehicles at almost the same speed or on the road in the woods. On the structured road, lane markings as static semantic features may provide a stable landmark assist in robust localization. As most of the current lane detection work mainly on separated images ignoring the relationship between adjacent frames, we propose a pixel-level lane tracking method for autonomous vehicles. In this paper, we introduce a deep network to detect and track lane features. The network has two parallel branches. One branch detects the lane position, while the other extracts the point description on a pixel level.

This paper studies a multi-objective optimization design of interior noise for an automotive body. An acoustic-structure coupled model with materials and properties was established to predict the interior noise based on a passenger car. Moreover, three kinds of approximation models related damping thickness and the root mean square of the driver’s ear sound pressure level were established through Latin hypercube method and the corresponding experiments. The prediction accuracy was analyzed and compared for the approximate response surface model, Kriging model and Radial Basis Function neural network model. On this basis, multi-objective optimization of the vehicle interior noise was conducted by using NSGA-II. According to the optimization results, the damping composite structure was applied on the car body structure. Then, the comparison of sound pressure level response at driver’s ear location before and after optimization was performed at speed of 60 km/h on a smooth road.

This study presents a hybrid optimization approach of TOPSIS-based Taguchi method and entropy measurement for the determination of the optimal suspension parameters to achieve an enhanced compromise among ride comfort, road friendliness which means the extent of damage exerted on the road by the vehicles, and handling stabilities of a self-dumping truck. Firstly, the full multi-body dynamic vehicle model is developed using software ADAMS/Car and the vehicle model is then validated through ride comfort road tests. The performance criterion for ride comfort evaluation is identified as root mean square (RMS) value of frequency weighted acceleration of cab floor, while the road damage coefficient is used for the evaluation of the road-friendliness of a whole vehicle. The lateral acceleration and roll angle of cab were defined as evaluation indices for handling stability performance.

A detailed multi-body dynamic model of a passenger car was modeled using ADAMS/Car and then checked by the ride comfort and handling stability test results in this paper. The performance criterion for ride comfort evaluation was defined as the overall weighted acceleration root mean square (RMS) value of car body floor, while the roll angle and lateral acceleration of car body were considered as evaluation indicators for handling stability performance. Simultaneously, spring stiffness and shock absorber damping coefficients of the front and rear suspensions were taken as the design variables (also called factors), which were considered at three levels. On this basis, a L9 orthogonal array was employed to perform the ride and handling simulations.

With vehicle platooning becoming an important research field in recent years, it is now imperative to introduce platoons as part of the dynamic environment, considering overtaking and merging possibilities. This article studies optimal speed trajectories and longitudinal control with optimized energy efficiency for an autonomous vehicle with several preceding platoons and full terrain information. It aims at improving the energy efficiency of vehicles with Advanced Driver Assistance Systems (ADAS). A forward discrete dynamic programming (DDP) algorithm with distance as the discretization basis is used to derive speed trajectories in the trade-off between air drag reduction and energy saved by utilizing the road slope information. The problem is decomposed into decisions whether to overtake or to merge into the nearest platoon with the assumption of sufficient distance among platoons.

Air spring has a variable stiffness characteristic, its vibration frequency is much lower than that of leaf spring and will not vary with load of vehicle. More and more air springs are applied on automobile suspension. A study on the automobile ride comfort, and the controllability and stability about the bus with air suspension is performed in the paper, which is based on multi-body system dynamics.

In order to improve drivers’ acceptance to advanced driver assistance systems (ADAS) with better adaptation, drivers’ driving behavior should play key role in the design of control strategy. Adaptive cruise control systems (ACC) have many factors that can be influenced by different driving behavior. It is important to recognize drivers’ driving behavior and take human-like parameters to the adaptive cruise control systems to assist different drivers effectively via their driving characteristics. The paper proposed a method to recognize drivers’ behavior and intention based on Gaussian Mixture Model. By means of a fuzzy PID control method, a personalized ACC control strategy was designed for different kinds of drivers to improve the adaptabilities of the systems. Several typical testing scenarios of longitudinal case were created with a host vehicle and a traffic vehicle.

Minimum energy consumption with maximum comfort driving experience define the ideal human mobility. Recent technological advances in most Advanced Driver Assistance Systems (ADAS) on electric vehicles not only present a significant opportunity for automated eco-driving but also enhance the safety and comfort level. Understanding driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system comfort. This research focuses on the personalized and green adaptive cruise control for intelligent electric vehicle, which is also known to be MyEco-ACC. MyEco-ACC is based on the optimization of regenerative braking and typical driving styles. Firstly, a driving style model is abstracted as a Hammerstein model and its key parameters vary with different driving styles. Secondly, the regenerative braking system characteristics for the electric vehicle equipped with 4-wheel hub motors are analyzed and braking force distribution strategy is designed.

To reduce the interference and conflict of human-machine cooperative control, lighten the operation workload of drivers, and improve the friendliness and acceptability of intelligent vehicles, a personalized human-machine cooperative lane-change trajectory tracking control method was proposed. First, a lane-changing driving data acquisition test was carried out to collect different driving behaviors of different drivers and form the data pool for the machine learning method. Two typical driving behaviors from an aggressive driver and a moderate driver are selected to be studied. Then, a control structure combined by feedforward and feedback control based on Long Short Term Memory (LSTM) and model-based optimum control was introduced. LSTM is a machine learning method that has the ability of memory. It is used to capture the lane-changing behaviors of each driver to achieve personalization. For each driver, a specific personalized controller is trained using his driving data.

Accurate tire pressure monitoring system (TPMS) is of great practical importance and the reliability and safety of its power supply module has great concern. The piezoelectric-based surface acoustic wave (SAW) sensor is considered to have great potential in this field because of its passive, wireless and small size advantages. This paper presents the application of passive and wireless SAW sensors for real-time tire condition monitoring. The pressure sensitive structure is optimized and a three-resonator structure is also designed sensing temperature and pressure. Furthermore, a fast detection system is developed to realize high-speed signal acquisition. At last, experiments are executed and the SAW temperature and pressure sensor property is measured.

Adaptive Cruise Control (ACC) includes three modes: cruise control, car following control, and autonomous emergency braking. Among them, the car following control mode is mainly used to manage the speed and vehicle spacing approach the preceding vehicle within the range of smooth acceleration changes. In addition, although the motion information signal of the preceding vehicle can be collected by auxiliary equipment, it is still a random variable and normally regarded as a disturbance to affect the performance of vehicle controller. Therefore, this paper proposed an ACC strategy considering the disturbance of the preceding vehicle and multi-objective optimization.

Active steering technology can improve the operability of the driver by the involvement to the steering system. Driver is the major controller of the vehicle Therefore, the involvement of advanced technologies including the active steering technology shouldn’t interfere with the intention of the driver, and the driver should still have great control of the vehicle. The aim of this paper is to solve the problem of the driver’s control when the active steering system works to improve the flexibility of the low speed and the stability of the high speed, and the active steering model based on the driver’s steering intention is established. Through the CarSim simulation software, this paper adopts 9 parameters related to the vehicle steering of the DLC (Double Line Change). And PCA (Principal Component Analysis) algorithm, a tool of statistical analysis, is applied to select 4 parameters which can stand for the DLC from the 9 parameters, which makes the data processing easier.

With the aim of addressing the problem of inconsistency of the traditional proportion integration (PI) driver model with the actual driving behavior, a longitudinal driver model based on the elastic net regression (ENR) and adaptive network fuzzy inference system (ANFIS) method is proposed. First, longitudinal driving behavior data are collected through bench tests to extract the characteristic parameters that affect driving behavior. A quadratic regression model is established after considering the nonlinear characteristics of the driver behavior. The multi-collinear problem of high-dimensional variables in the regression model is solved by the ENR method, and the parameters with significant influence on driving behavior selected. A longitudinal driver model of ANFIS was established with the selected characteristic parameters as input. Finally, the validity of the model is verified by comparing it with the PI and ENR driver models.

Aiming at the problems of long lane change intention recognition, complicated lane change model, and huge amount of processing data in the current research, this paper uses principal component analysis to improve the driver’s lane change intention recognition model using traditional pattern recognition. Firstly collect 7 parameters including driver operation and vehicle running characteristics. After data standardization and PCA (principal component analysis), the top three principal components that can reflect the information content of the original data are nearly 90%. Then, a lane-change intent recognition model based on GMM-HMM was established, three lane change intents cannot be directly observed as the hidden state of the model; and three principal component quantities obtained through linear changes are used as observational measurements.

With the rapid development of science and technology, the automobile industry is developing rapidly, and intelligent networking and autonomous driving have become new research hotspots. The safety and efficiency of vehicle driving has always been an important research topic in the transportation field. Due to reducing the participation of drivers, autonomous vehicles can reduce traffic accidents caused by human factors. While the development of intelligent networking can achieve information sharing between vehicles, and improve driving efficiency to a certain extent. Based on the game theory and the minimum safe distance condition, this paper establishes a lane changing decision model of intelligent network-connected autonomous vehicles, puts forward a game payoff function and analyzes the game strategy.

With the rapid development of modern economy and society, traffic congestion has become an increasingly serious problem. Vehicle cooperative driving can alleviate traffic congestion and improve road traffic capacity. Compare with vehicle separate control, cooperative driving combines various vehicle systems, and highly integrates information on obstacle location, vehicle status and driving intention. Then the controller uniformly issues instructions to ensure the orderly driving of the platoon. In the cooperative driving platoon, the displacement difference and the speed difference between vehicles have a certain relationship, which reduces the possibility of traffic accidents and then improves the safety of driving. In the process of cooperative driving, if there are multiple vehicles whose speeds don’t meet the current lane requirements, or if there are obstacles ahead, multi-vehicle lane change measures must be taken.

With the popularity of electrification and driver assistance systems on vehicle dynamics and controls, the steering performance of the vehicle put forward higher requirements. Thus, the steer-by-wire technology is becoming particularly important. Through specific control algorithm, the steer-by-wire system electronic control unit can receive signals from other sensors on the vehicle, realize the personalized vehicle dynamics control on the basis of understanding the driver’s intention, and grasp the vehicle movement state. At the same time, to make these driver assistance systems better cooperate with human drivers, reduce system frequent false warning, full consideration of mutual adaptation for the systems and the driver’s characteristics is critical. This paper focuses on the steering performance of steer-by-wire vehicle. Feature parameters are obtained from the virtual turning experiment designed on the driving simulator experimental platform.

Based on the research and analysis of the current brake systems, this paper presents a novel electro-hydraulic brake system, which can better meet the functional requirements. The system mainly contains a master cylinder, two brake hydraulic cylinders and drive motors, two transmission mechanisms, thirteen solenoid valves, pedal force simulator, etc. Since the proposed brake system uses a dual motor along with two brake hydraulic cylinders, it has advantages in providing fast pressure response, flexible working modes, high precision and strong fault tolerance. In order to facilitate the study of pressure control algorithm for the proposed brake system, a mathematical model of the brake system is firstly established, then a multiplexed time-division pressure control algorithm is proposed to realize the simultaneous or partially simultaneous pressure control, which ensures the high precision and short response time.

Most of the Advanced Driver Assistance System (ADAS) applications are aiming at improving both driving safety and comfort. Understanding human drivers' driving styles that make the systems more human-like or personalized for ADAS is the key to improve the system performance, in particular, the acceptance and adaption of ADAS to human drivers. The research presented in this paper focuses on the classification and identification for personalized driving styles. To motivate and reflect the information of different driving styles at the most extent, two sets, which consist of six kinds of stimuli with stochastic disturbance for the leading vehicles are created on a real-time Driver-In-the-Loop Intelligent Simulation Platform (DILISP) with PanoSim-RT®, dSPACE® and DEWETRON® and field test with both RT3000 family and RT-Range respectively.