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In the field of active safety, the active four-wheel-steering (4WS) system seems to be an attractive alternative and an effective tool to improve the vehicles' handling stability in lane-keeping control performance. Under normal using condition, the vehicle's lateral acceleration is comparatively small, and the mathematic relationship between the small side force excitation and the small slip angle of the tire is in the linear region. Furthermore, the effects of roll, heave, and pitch motions are neglected as well as the dynamic characteristics of the tires and suspension system in this work. Therefore, the linear quadratic control (LQC) theory is used to ensure that the output of the 4WS control system can keep track of the desired yaw rate and zero-sideslip-angle response can also be realized at the same time.

Threat assessment (TA) method is vital in the decision-making process of intelligent vehicles (IVs), especially for ADAS systems. In the research of TA, the probabilistic threat assessment (PTA) method is acting an increasing role, which can reduce the uncertainties of driver’s maneuvers. However, the driver behavior model (DBM) used in present PTA methods was mainly constructed by limited data or simple functions, which is not entirely reasonable and may affect the performance of the TA process. This work aims to utilize crash data extracted from Event Data Recorder (EDR) to establish more accurate DBM and improve the current PTA method in rear-end situations. EDR data with responsive maneuvers were firstly collected, which were then employed to construct the initial DBM (I-DBM) model by using the multivariate Gaussian distribution (MGD) framework. Besides, the model was further subdivided into six parts by two important risk indicators, Time-to-collision (TTC) and velocity.

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).

At present, many electric vehicles are often equipped with only a single-stage final drive. Although the single-stage speed ratio can meet the general driving requirements of electric vehicles, if the requirements of the maximum speed and the requirements for starting acceleration or climbing are met at the same time, the power demand of the drive motor is relatively large, and the efficient area of the drive motor may be far away from the operating area corresponding to daily driving. If the two-speed automatic transmission is adopted, the vehicle can meet the requirements of maximum speed, starting acceleration and climbing at the same time, reduce the power demand of the driving motor, and improve the economy under certain power performance. This is especially important for medium and large vehicles.

How to improve the measurement accuracy of road gradient is the key content of the research on the speed warning of commercial vehicles in mountainous roads. The large error of the measurement causes a significant effect of the vehicle speed threshold, which causes a risk to the vehicle's safety. Conventional measuring instruments such as accelerometers and gyroscopes generally have noise fluctuation interference or time accumulation error, resulting in large measurement errors. To solve this problem, the Kalman filter method is used to reduce the interference of unwanted signals, thereby improving the accuracy of the slope measurement. However, the Kalman filtering method is limited by the estimation error of various parameters, and the filtering effect is difficult to meet the project research requirements.

The drivability plays an important role for marketability and competitiveness of passenger car in meeting some customer requirements, which directly affects the driving experience and the desire of purchasing. In this paper, a framework of objective drivability evaluation with multi-source information fusion for passenger car is proposed. At first, according to vehicle powertrain system and optimization theory, certain vehicle performances, which are closely related to objective drivability are analyzed, including vehicle longitudinal acceleration, vehicle speed, engine torque, engine speed, gear position, accelerator pedal, brake signal and voltage signal. Then, combined with the evaluation criterion of signal-to-noise ratio (SNR), mean error (ME), root mean squared error (RMSE) and signal smoothness (SS), a de-noising method is developed for the drivability evaluation information.

Driver distraction detection is crucial to driving safety when autonomous vehicles are co-piloted. Recognizing drivers’ behaviors that are highly related with distraction from real-time video stream is widely acknowledged as an effective approach mainly due to its non-intrusiveness. In recently years, deep learning neural networks have been adopted to by-pass the procedure of designing features artificially, which used to be the major downside of computer-vision based approaches. However, the detection accuracy and generalization ability is still not satisfying since most deep learning models extracts only spatial information contained in images. This research develops a driver distraction model based on a two-stream, spatial and temporal, convolutional neural network (CNN).

On-road testing of driving performance of the urban bus was carried out, and a representative urban bus driving cycle was developed after on-road testing, according to the test results. Then, the vehicle simulation software AVL CRUISE was used to simulate the dynamic behavior of the urban bus. It involves the simulation of complete drive train system and the driver behavior. The model is validated by comparing the results of the simulation to the results of the field test. Then the developed driving cycle is evaluated by fuel consumption resulted from the simulation and engine bench test on fuel economy.

Studying traffic accidents by using naturalistic driving data has become increasingly appealing for its potential benefits in improving road safety. This paper presents findings from a field test which has been conducted on 50 taxis in the urban areas of Beijing for 10 months using Video Drive Recorders (VDRs). The VDR used in this study could record the information of vehicle front view video, vehicle states, as well as driver operations immediately before and after an event. The drivers were given no specific instructions during the test, and the instrumentation for data collection was unobtrusive. Important safety-relevant parameters, such as vehicle speed, pre-event maneuver, time headway, time-to-collision, and driver reaction time, were calculated with precision. Based on these parameters, an analysis into features and causes of rear-end conflicts is performed.

Studies show that driving in foggy environment is a security risk, and when driving in foggy environment, the drivers are easy to accelerate unconsciously. The safety information prompted to the driver is mainly from fog lights, road warning signs and the traffic radio. In order to increase the quality of the safety tips to prevent drivers from unintended acceleration and ensure the security of driving in foggy environment, the study proposes a safety speed assessment method for driving in foggy environment, combining the information of driving environment, vehicle’s speed and the multimedia system. The method uses camera which is installed on the front windshield pillar to collect the image about the environment, and uses the dark channel prior theory to calculate the visibility. And by using the environment visibility, the safety speed can be calculated based on the kinematics theory. And it is appropriate for vehicles which have different braking performance.

The drivers' hysteretic perception to surrounding environment will affect vehicular fuel economy, especially for the heavy-duty vehicles driving under complex conditions and long distance in mountainous areas. Unreasonable acceleration or deceleration on the slope will increase the fuel consumption. Improving the performance of the engine and the transmission system has limited energy saving potential, and most fuel-efficient driving assistant systems don't consider the road conditions. The main purpose of this research is to introduce an economic driving scheme with consideration of the prestored slope information in which the vehicle speed in mountainous slopes is reasonably planned to guide the driver's behavior for reduction of the fuel consumption. Economic driving optimization algorithm with low space dimension and fast computation speed is established to plan accurate and real-time economic driving scheme.

This research is based on the Controller Area Network (CAN) bus, and briefly analyzed its communication protocol with reference to the layered model of Open System Interconnect Reference Model (OSI). Subsequently, a data acquisition system was designed and developed including a Vehicle Communication Interface (VCI) and a laptop. After the overall architecture was built, the communication mechanism of the VCI was studied. Furthermore, the lap top app was built using the layered design followed by the implementation of a scheme for data collection and experimentation involving the test driving of a real car on road. Finally, the driving style was identified by means of fuzzy reasoning and solving ambiguity based on fuzzy theory; via training the acceleration sample and forecast using the excellent learning and generalization ability of Support Vector Machine (SVM) for high-dimensional, finite samples.

H-Bahn ("hanging railway") refers to the suspended, unmanned urban railway transportation system. Through the reasonable platform layout, H-Bahn can be easily integrated into the existing urban transit system. With the development of urban roads, the associated rail facilities can be conveniently disassembled, moved and expanded. The track beam, circuits, communication equipment, and sound insulation screen are all installed in a box-type track beam so that the system can achieve a high level of integration and intelligence. The carriage of the modern H-banh vehicle is connected with the bogies by two hanging devices. The vehicle is always running in the box-type track beam; therefore there are less possibilities of derailment. Consequently, the key work focuses on the running stability evaluation and curve negotiation performance analysis.

This paper presents a novel application of hydraulic electromagnetic energy-regenerative shock absorber (HESA) into commercial vehicle suspension system and vehicle road performance are simulated by the evaluating indexes (e.g. root-mean-square values of vertical acceleration of sprung mass, dynamic tire-ground contact force, suspension deflection and harvested power; maximum values of pitch angle and roll angle). Firstly, the configuration and working principle of HESA are introduced. Then, the damping characteristics of HESA and the seven-degrees-of-freedom vehicle dynamics were modeled respectively before deriving the dynamic characteristics of a vehicle equipped with HESA. The control current is fixed at 7A to match the similar damping effect of traditional damper on the basis of energy conversion method of nonlinear shock absorber.

The current paper proposes a hydraulic interconnected suspension system (HIS) based on a hydraulic energy-regenerative shock absorber (HESA) comparatively with the passive suspensions. The structure and working principles of the HIS system are introduced in order to investigate the damping performance and energy regeneration characteristics of the proposed system. Then, the dynamic characteristics of the HIS-HESA system have been investigated based on a 4-DOF longitudinal half vehicle model. In the simulation, two different road inputs were used in the dynamic characterization of the HIS-HESA; the warp sinusoidal excitation, and the random road signal. In addition, a comparative analysis was provided for the dynamic responses of the half vehicle model for both the HIS-HESA and the conventional suspension. Furthermore, a parametric analysis of the HIS-HESA has been carried out highlining the key parameters that have a remarkable effect on the HIS-HESA performance.

When vehicle traveling on the bumpy road or vehicle acceleration and deceleration, which will cause the body vibration of vehicle, at the same time, a large part of energy would be absorbed by the shock absorber transforms the mechanical energy into heat energy dissipated. In order to recycle the energy of vibration and keep the stability of running car, this paper provides the shock absorber of energy recovery that recycling the energy dissipated from the traditional absorber. The shock absorber includes rod and rodless chamber cavity, the two parts contain oil outlet and oil inlet, which connected to a bridge type loop of hydraulic to make pulsating oil pressure towards one direction, when the shock absorber vibration causes pulsating oil pressure, it drives hydraulic pump operation. Because the output shaft of the hydraulic pump fixedly attached to the input shaft of generator, so the generator produces electricity for recycling energy[1].

Dangerous driving behavior will cause serious traffic accidents, which will not only threaten life and property but also cause traffic congestion and reduce road capacity. Speed detection is an important detection method to identify whether a driver is driving dangerously. Traditional speed detection methods need additional sensors, which will increase the cost of speed measurement. This paper proposes a vehicle speed estimation algorithm based on the imaginary projection plane (IPP). The IPP will be established according to the height, field angle, and vertical tilt angle of the camera and will be used to establish the mapping relationship between the world coordinates and image coordinates of the vehicle. By combining YOLOv4 and DeepSORT, the vehicle license plate is detected and tracked, and the center point of the vehicle license plate is taken as the feature point of vehicle speed estimation. The vehicle speed is estimated according to the IPP.

The development of electrification is widely considered to be the key to the transportation industry. In recent years, the number of electric trucks on the road is increasing year by year, so the safety of electric trucks is of great importance. At present, the batteries of electric trucks are mostly arranged on the two sides of the trucks. The protective devices are only the guardrails fixed on the vehicle body, so the protective effect is poor. In view of this situation, this paper designed a battery protection device for electric truck. When the truck is hit in a side collision, the transverse guardrail first generates plastic deformation and absorbs kinetic energy. At this time, the collision force is transmitted to the energy-absorbing box along the moving direction of the side collision vehicle, and the energy-absorbing box is contracted to produce a buffer effect.

Road traffic accidents resulting from alcohol-impaired driving are increasing globally despite several measures, currently in place, to curb the trend. For this reason, recent research aims at integrating alcohol early-detection systems and driving simulator experiments to identify intoxicated drivers. However, driving simulator experiments on drunk driving have focused mostly on male participants than female drivers whose characteristics have scarcely been explored. Hence in this paper, vehicle dynamic control inputs on steering, braking, and acceleration performance of 75 licensed female drivers with an upshot of alcohol at four different blood alcohol concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%) were investigated. The participants completed simulated driving in a fixed-based simulator experiment coupled with real-time ecological scenarios to extract discrete responses.

Energy has the worldwide concern since the World War. Recently, the energy harvesting technology has got more attraction in different fields and applications. Hence, in a world where energy becomes rare and expensive, even the small quantities are worth to be harvested where it can be exploited in different applications. Vehicle suspension is one of the vibration power dissipation sources in which the undesired vibration is dissipated into heat waste. Accordingly, the principal motivation of this study is exploitation the conflict between the potentially harvested power and vehicle dynamics in automotive suspension system induced by road irregularity. Therefore, in terms of RMS conflict diagrams, the conflict between the potential power and vehicle dynamics are sufficiently and comprehensively defined considering a vehicle speed of 20 m/s.