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In order to improve the ignition capacity and burning rate for spark-ignited engines, pre-chamber jet ignition is a promising technique to achieve fast premixed combustion and low pollutant emissions. However, few studies focus on the interaction between multiple reacting (i.e. flamelet) or reacted (i.e. radical) jets, its effect on ignition, exotherm and flow behaviors also remain to be revealed. This paper investigated two types of jet interaction under different pre-chamber structures, including the jet-crossing and unequal nozzle designs. Optical experiments under different conditions were conducted in a constant volume combustion chamber with CH4 as fuel, using simultaneous high speed schlieren and OH* chemiluminescence method. Meanwhile, computational fluid dynamics (CFD) simulations with CH4 and NH3/CH4 blend fuels were carried out using Converge software to provide further insights of turbulent flow and ignition process.

Accurate sensing of road conditions is one of the necessary technologies for safe driving of intelligent vehicles. Compared with the structured road, the unstructured road has complex road conditions, and the response characteristics of vehicles under different road conditions are also different. Therefore, accurately identifying the road categories in front of the vehicle in advance can effectively help the intelligent vehicle timely adjust relevant control strategies for different road conditions and improve the driving comfort and safety of the vehicle. However, traditional road identification methods based on vehicle kinematics or dynamics are difficult to accurately identify the road conditions ahead of the vehicle in advance. Therefore, this paper proposes an unstructured road region detection and road classification algorithm based on machine vision to obtain the road conditions ahead.

Seatbelt and airbags provide effective occupant restraint, but are also potential to induce intrusive deformation and submarining injuries in motor vehicle crashes. To address these issues, this study puts forward a new restraint concept that applies restraint loads on shoulders and knees/femurs, i.e., the sturdiest regions of human body, via a combined use of shoulder bolster and knee bolster based on biomechanical computational analysis. The load characteristics of the two bolsters were optimized to obtain protection effectiveness superior to conventional use of seatbelt and airbag. Occupant kinematics and kinetics were taken into account, including the excursions of head, shoulders and knees, the accelerations of head and chest, and the compressions of thorax on several locations on the ribcage. The injury risk of rib fractures was monitored based on the strain levels of ribcage.

The development of intelligent transportation improves road efficiency, reduces automobile energy consumption, and improves driving safety. The core of intelligent transportation is the two-way information interaction between vehicles and the road environment. At present, road environmental information can flow to the vehicle, while the vehicle’s information rarely flows to the outside world. The electronic throttle and electronic braking systems of some vehicles use sensors to get the state of the accelerator and brake pedal, which can be transmitted to the outside environment through technologies such as the Internet of Vehicles. But the Internet of Vehicles technology has not been widely used, and it relies on signal sources, which is a passive way of information acquisition. In this paper, an active identification method is proposed to get the vehicle pedal on-off state as well as the driver’s operation behavior through existing traffic facilities.

The brake performance is one of the most important performances in the automotive active safety, and it is the main measure of automotive active safety. Thus, to develop a platform for the braking system is quite significant. Based on the object-oriented technology, the platform for braking system is developed by making use of Visual C++ 6.0 development tool. By using the VC++ development tool and doing secondary development on other softwares, the software possesses powerful features, such as brake plan selection, performance calculation, parametric modeling, finite element analysis and kinematics simulation, etc. An initial brake system can be designed, calculated and analyzed all in one. The living instance shows that the platform has friendly user interfaces, powerful functions and it can improve the precision and efficiency of brake design. The platform has been of great applied value and can also positively promote the design automation of vehicle's braking system.

In the process of truck overload and over-limit detection, it is necessary to detect the characteristics of the vehicle's size, type, and wheel number. In addition, in some vehicle vision-based load recognition systems, the vehicle load can be calculated by detecting the vibration frequency of specific parts of the vehicle or the change in the length of the suspension during the vehicle's forward process. Therefore, it is essential to quickly and accurately identify vehicle features through the camera. This paper proposes a vehicle feature recognition method based on image semantic segmentation and Python, which can identify the length, height, number of wheels and vibration frequency at specific parts of the vehicle based on the vehicle driving video captured by the roadside camera.

Vehicle Longitudinal Control (VLC) algorithm is the basis function of automotive Cruise Control system. The main task of VLC is to achieve a longitudinal acceleration tracking controller, performance requirements of which include fast response and high tracking accuracy. At present, many control methods are used to implement vehicle longitudinal control. However, the existing methods are need to be improved because these methods need a high accurate vehicle dynamic model or a number of experiments to calibrate the parameters of controller, which are time consuming and costly. To overcome the difficulties of controller parameters calibration and accurate vehicle dynamic modeling, a vehicle longitudinal control algorithm based on iterative learning control (ILC) is proposed in this paper. The algorithm works based on the information of input and output of the system, so the method does not require a vehicle dynamics model.

In the intelligent airbag system, the detection accuracy of occupant position is the precondition and plays a vital role to control airbag detonation time and inflated strength during the crash. Through accurately analyzing the seat surface pressure distributions of different occupant sitting position and types, an occupant position recognition approach which purely uses occupant pressure distribution information measured by seat pressure sensors is presented with the method of Support Vector Machine. In the end, the distribution samples with different occupant sitting position and types are used to train and test the recognition approach, and the good validity and accuracy are shown in the experiments.

Vehicle speed is an important factor to driving safety, which is directly related to the stability and braking performance of the vehicle. Besides, the precise measurement of the vehicle speed is the basis of some vehicle active safety systems. Even in the future intelligent transportation, high quality speed information will also play an important role. The commonly used vehicle speed measurement techniques are based on the wheel speed sensors, which are not accurate, especially when the wheels’ slip rate is not equal to zero. Focusing on these issues, image matching technology has been used to measure the vehicle speed in this paper. The image information of the road in the front of the vehicle is collected, and the pixel displacement of the vehicle is calculated by the matching system, thus accurately vehicle speed can be obtained. Compared with conventional speed measure technology, it has the advantages of wide measuring range, and high accuracy.

Downhill mountain roads are the accident prone sections because of their complexity and variety. Drivers rely more on driving experience and it is very easy to cause traffic accidents due to the negligence or the judgment failure. Traditional active safety systems, such as ABS, having subjecting to the driver's visual feedback, can’t fully guarantee the downhill driving safety in complex terrain environments. To enhance the safety of vehicles in the downhill, this study combines the characteristics of vehicle dynamics and the geographic information. Thus, through which the drivers could obtain the safety speed specified for his/her vehicle in the given downhill terrains and operate in advance to reduce traffic accidents due to driver's judgment failure and avoid the brake overheating and enhance the safety of vehicles in the downhill.

High speed on-off valves (HSVs) are widely used in advanced hydraulic braking actuators, including regenerative braking systems and active safety systems, which take crucial part in improving the energy efficiency and safety performance of vehicles. As a component involving multiple physical fields, the HSV is affected by the interaction of the fields-fluid, electromagnetic, and mechanical. Since the opening of the HSV is small and the flow speed is high, cavitation and vortex are inevitably brought out so that increase the valve’s noise and instability. However, it is costly and complex to observe the flow status by visual fluid experiments. Hence, in this article a visual multi-physics system simulation model of the HSV is explored, in which the flow field model of the HSV built by computational fluid dynamic (CFD) is co-simulated with the model of hydraulic actuator established by AMESim.

Combustion visualizations were carried out in a constant volume vessel to study the partially premixed combustion of a gasoline-like fuel using high speed imaging. The test fuel (G80H20) is composed by volume 80% commercial gasoline and 20% n-heptane. The effects of ambient gas composition, ambient temperature and injection pressure on G80H20 combustion characteristics were analyzed. Meanwhile, a comparison of the EGR effect on combustion process between G80H20 and diesel was made. Four ambient gas conditions that represent the in-cylinder gas compositions of a heavy-duty diesel engine with EGR ratios of 0%, 20%, 40% and 60% were used to simulate EGR conditions. Variables also include two ambient temperature (910K and 870K) and two injection pressure (20 MPa and 50 MPa) conditions.