Search Results

Transport vehicles consume a large amount of fuel with low efficiency, which is significantly affected by drivers' behaviors. An assessment system of eco-driving pattern for buses could identify the deficiencies of driver operation as well as assist transportation enterprises in driver management. This paper proposes an assessment method regarding drivers' economic efficiency, considering driving conditions. To this end, assessment indexes are extracted from driving economy theories and ranked according to their effect on fuel consumption, derived from a database of 135 buses using multiple regression. A layered structure of assessment indexes is developed with application of AHP, and the weight of each index is estimated. The driving pattern score could be calculated with these weights.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

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.

Pedestrian upper leg impact protection is a challenging requirement in the Euro NCAP assessment. In upper legform to bonnet leading edge tests, the legform impact force, the legform intrusion and the injury parameters (impact force and bending moment measured on the upper legform) are highly related to design of vehicle front-end styling and structure, as well as clearance underneath bonnet leading edge. In the course of impact, the contact area variation has significant influence on the stress distribution and consequently the force and the bending moment on the upper legform. Using finite element simulations of upper legform impact with a typical sedan, the deformation of the legform and the vehicle structure, and the variation of the contact force distribution are characterized and analyzed.

Automotive safety is always the focus of consumers, the selling point of products, the focus of technology. In order to achieve automatic driving, interconnection with the outside world, human-automatic system interaction, the security connotation of intelligent and connected vehicles (ICV) changes: information security is the basis of its security. Functional safety ensures that the system is operating properly. Behavioral safety guarantees a secure interaction between people and vehicles. Passive security should not be weakened, but should be strengthened based on new constraints. In terms of information safety, the threshold for attacking cloud, pipe, and vehicle information should be raised to ensure that ICV system does not fail due to malicious attacks. The cloud is divided into three cloud platforms according to functions: ICVs private cloud, TSP cloud, public cloud.

An energy management strategy is needed to optimally allocate the driver's power demands to different power sources in the fuel cell hybrid vehicles. The driver's power demand is modelled as a Markov process in which the transition probabilities are estimated on the basis of the observed sample paths. The Markov Decision Process (MDP) theory is applied to design a stochastic energy management strategy for fuel cell hybrid vehicles. This obtained control strategy was then tested on a real time simulation platform of the fuel cell hybrid vehicles. In comparison to the other 3 strategies, the constant bus voltage strategy, the static optimization strategy and the dynamic programming strategy, simulations in the Beijing bus driving cycle demonstrate that the obtained stochastic energy management strategy can achieve better performance in fuel economy in the same demand of dynamic.

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.

The issue of car-to-pedestrian impact safety has received more and more attention. For leg protection, a legform impactor with 2 degrees-of-freedom (DOF) proposed by EEVC is required in current regulations for injury assessment, and the Japan Automobile Manufacturers Association Inc. (JAMA) and Japan Automobile Research Institute (JARI) have developed a more biofidelic pedestrian legform since 2000. However, studies show that those existing legforms may not be able to cover some car-to-pedestrian impact situations. This paper documents the development of a new pedestrian legform with 4 DOFs at the knee-joint. It can better represent the kinematics characteristics of human knee-joint, especially under loading conditions in omni-direction impacts. The design challenge is to solve the packaging problem, including design of the knee-joint mechanisms and layout of all the sensors in a limited space of the legform.

The traditional vacuum booster is gradually replaced by Brake-by-Wire system (BBW) in modern passenger car, especially Electric Vehicle (EV). Some mechanical and hydraulic components are replaced by electronic components in Brake-by-Wire system. Using BBW system in modern passenger vehicles can not only improve the automotive safety performance, reliability and stability, but also promote vehicle maneuverability, comfort, fuel economy and environmental protection. Although vehicle's braking performance is greatly improved by using BBW, the system will inevitably consume some energy of the vehicle power supply, thus introducing unexpected drawback in comparison with the traditional vacuum assist braking system, since it doesn't need any electric power. Therefore, the analysis of energy consumption on typical main cylinder booster based BBW system under typical driving cycles will contribute to advanced design of current advanced braking system.

Highway traffic safety has been the most serious problem in current society, statistics show that about 70% to 90% of accidents are caused by driver operational errors. The autonomous emergency braking (AEB) is one of important vehicle intelligent safety technologies to avoid or mitigate collision. The AEB system applies the vehicle brakes when a collision is eminent in spite of any reaction by the driver. In some technologies, the system forewarns the driver with an acoustic signal when a collision is still avoidable, but subsequently applies the brakes automatically if the driver fails to respond. This paper presents the development and implementation of a rear-end collision avoidance system based on hierarchical control framework which consists of threat assessment layer, wheel slip ratio control layer and integrated-electro-hydraulic brake (IEHB) actuator control layer.

Vehicle automation is a fundamental approach to reduce traffic accidents and driver workload. However, there is a notable risk of pushing human drivers out of the control loop before automation technology fully matures. Cooperative driving (or vehicle co-piloting) is a novel paradigm which is defined as the vehicle being jointly navigated by a human driver and an automatic controller through shared control technology. Indirect shared control is an emerging shared control method, which is able to realize cooperative driving through input complementation instead of haptic guidance. In this paper we first establish an indirect shared control method, in which the driver’s commanded input and the controller’s desired input are balanced with a weighted summation. Thereafter, we propose a predictive model to capture driver adaptation and trust in indirect shared control.

Engine torque fluctuation is a great threat to vehicle comfort and durability. Former researches tried to solve this problem by introducing active damping system, which means the motor is controlled to produce torque ripple with just the opposite phase to that of the engine. By this means, the torque fluctuation produced by the motor and the engine can be reduced. In this paper, a new method is raised. An attempt is proposed by changing the traditional structure of the motor, making it produce ripple torque by itself instead of controlling the motor. In this way a special used ISG (Integrated Starter Generator) motor for HEV (Hybrid Electrical Vehicles) is made to achieve active damping. In order to study the possibility, a simulation, which focus on the motor instead of the whole system, is developed and series-parallel configuration is used in this simulation. As for the motor that used in this paper, four kinds of motors have been investigated and compared.

Occupant restraint systems are developed based on some baseline experiments. While these experiments can only represent small part of various accident modes, the current procedure for utilizing the restraint systems may not provide the optimum protection in the majority of accident modes. This study presents an approach to predict occupant injury responses before the collision happens, so that the occupant restraint system, equipped with a motorized pretensioner, can be adjusted to the optimal parameters aiming at the imminent vehicle-to-vehicle frontal crash. The approach in this study takes advantage of the information from pre-crash systems, such as the time to collision, the relative velocity, the frontal overlap, the size of the vehicle in the front and so on. In this paper, the vehicle containing these pre-crash features will be referred to as ego vehicle. The information acquired and the basic crash test results can be integrated to predict a simplified crash pulse.

As the restraint technologies for front-seat occupant protection advance, such as seatbelt pre-tensioner, seatbelt load limiter and airbag, relative effectiveness of rear-seat occupant protection decreases, especially for the elderly. Some occupant protection systems for front-seat have been proved to be effective for rear-seat occupant protection as well, but they also have some drawbacks. Seatbelt could generate unwanted local penetrations to the chest and abdomen. And for rear-seat occupants, it might be difficult to install airbag and set deployment time. For crash protection, it is desirable that the restraint loads are spread to the sturdy parts of human body such as head, shoulders, rib cage, pelvis and femurs, as uniformly as possible. This paper explores a uniform restraint concept aiming at providing protection in wide range of impact severity for rear-seat occupants.

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.

This paper proposed a collision avoidance strategy that take over the control of ego vehicle when faced with urgent collision risk. To improve the applicability of collision avoidance strategy in complex scenarios, the theory of ICS (Inevitable Collision State) is introduced to evaluate the collision risk and compute the trigger flag of the system, and vehicle dynamic is taken into account when modeling ego vehicle to predict ego vehicle’s following moving. Vehicle specific characteristics including reaction time of the braking system and the braking force increasing process are taken into account. In order to reduce injury caused by collision accidents and minimize disruption to drivers, slight steering is added on top of emergency braking. The direction of the steering angle is determined according to IM (Imitating Maneuvers) The flow chart of the strategy is presented in the paper.

In vehicle accident, the bumper beam generally requires high stiffness for sufficient survival space for occupants while it may cause serious pedestrian lower extremity injuries. The aim of this study is to promote an aluminum-steel hybrid material double-hat bumper to meet the comprehensive requirements. The hybrid bumper is designed to improve the frontal crash and pedestrian protection performances in collision accidents. Finite element (FE) models of the hybrid bumper was built, validated, and integrated into an automotive model. The Fixed Deformable Barrier (FDB) and Transport Research Laboratory (TRL) legform model were used to obtain the vehicle crashworthiness and pedestrian lower leg injury indicators. Numerical results showed that the hybrid bumper had a great potential for crashworthiness performance and pedestrian protection characteristics. Based on this, a multi-objective optimization design (MOD) was performed to search the optimal geometric parameters.

The accuracy of road input identifiaction for autonomous vehicles (AVs) system, especially in state-based AVs control for improving road handling and ride comfort, is a challenging task for the intelligent transport system. Due to the high fatality rate caused by inaccurate state-based control algorithm, how to precisely and effectively acquire road rough information and chose the reasonable road-based control algorithm become a hot topic in both academia and industry. Uncertainty is unavoidable for AVs system, e.g., varying center of gravity (C.G.) of sprung mass, controllable suspension damping force or variable spring stiffness. To tackle the above mentioned, this paper develops a novel observer approach, which combines unscented Kalman filter (UKF) and Minimum Model Error (MME) theory, to optimize the estimation accuracy of the road rough for AVs system. A full-car nonlinear model and road profile model are first established.

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.

An autonomous emergency braking (AEB) system can detect emergency conditions using sensors (e.g., radar and camera) to automatically activate the braking actuator without driver input. However, during the hard braking phase, crash conditions for the restraint system can easily change (e.g., vehicle velocity and occupant position), causing an out-of-position (OOP) phenomenon, especially for unbelted occupants entering the airbag deployment range, which may lead to more severe injuries than in a normal position. A critical step in reducing the injury of unbelted occupants would be to design an AEB system while considering the effect of deployed airbags on the occupants. Thus far, few studies have paid attention to the compatibility between AEB and airbag systems for unbelted occupants. This study aims to provide a method that combines AEB and airbag systems to explore the potential injury reduction capabilities for unbelted occupants.