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A braking force distribution strategy in integrated braking system composed of the main braking system and the auxiliary braking system based on braking pad wear control and hitch force control under non-emergency braking condition is proposed based on the Electronically Controlled Braking System (EBS) to reduce the difference in braking pad wear between different axles and to decrease hitch force between tractors and trailers. The proposed strategy distributes the braking force based on the desired braking intensity, the degree of the braking pad wear and the limits of certain braking regulations to solve the coupling problems between braking safety, economical efficiency of braking and the comfort of drivers. Computer co-simulations of the proposed strategy are performed.

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.

Studies show that the pedestrian population at high risk of injury consists of both young children and adults. The goal of this study is to gain understanding in the mechanisms that lead to injuries for children and adults. Multi-body pedestrian human models of two specific anthropometries, a 6year-old child and a 50th percentile adult male, are applied. A vehicle model is developed that consists of a detailed rigid finite element mesh, validated stiffness regions, stiff structures underlying the hood and a suspension model. Simulations are performed in a test matrix where anthropometry, impact speed and impact location are variables. Bumper impact occurs with the tibia of the 50th percentile adult male and with the thigh of the 6-year-old child. The head of a 50th percentile male impacts the lower windshield, while the 6-year-old child's head impacts the front part of the hood.

Described in this paper is a component test methodology to evaluate the door trim armrest performance in an Insurance Institute for Highway Safety (IIHS) side impact test and to predict the SID-IIs abdomen injury metrics (rib deflection, deflection rate and V*C). The test methodology consisted of a sub-assembly of two SID-IIs abdomen ribs with spine box, mounted on a linear bearing and allowed to translate in the direction of impact. The spine box with the assembly of two abdominal ribs was rigidly attached to the sliding test fixture, and is stationary at the start of the test. The door trim armrest was mounted on the impactor, which was prescribed the door velocity profile obtained from full-vehicle test. The location and orientation of the armrest relative to the dummy abdomen ribs was maintained the same as in the full-vehicle test.

A recent study of U.S. crash data has shown that children 0-23 months of age in forward-facing child restraint systems (FFCRS) are 76% more likely to be seriously injured in comparison to children in rear-facing child restraint systems (RFCRS). Motivated by the epidemiological data, seven sled tests of dummies in child seats were performed at the University of Virginia using a crash pulse similar to FMVSS 213 test conditions. The tests showed an advantage for RFCRS; however, real-world crashes include a great deal of variability among factors that may affect the relative performance of FFCRS and RFCRS. Therefore, this research developed MADYMO computational models of these tests and varied several real-world parameters. These models used ellipsoid models of Q-series child dummies and facet surface models of American- and Swedish- style convertible child restraints (CRS).

The driving behaviors of young drivers under the influence of anger are analyzed by driving simulator in this paper. A total of 12 subjects are enrolled during the experiment. Standardized videos are utilized to induce the driver's anger emotion. And the driver's electrocardiogram (ECG) signal is collected synchronously and compared before and after emotional trigger, which prove the validity of emotional trigger. Based on the result, the driver's driving performance under the straight road and the curve under normal state and angry state are compared and analyzed. The results of independent sample t-test show that there are significant differences in the running time of straight sections and the standard deviation of steering wheel angle in curves between normal and angry states. In conclusion, the longitudinal and lateral operation of drivers is unstable in angry state and the driver will be more destructive to the regular driving behavior.

A finite element (FE) model of the lower limb was developed to improve the understanding of injury mechanisms of thigh, knee, and leg during car-to-pedestrian impacts and to aid in the design of injury countermeasures for vehicle front-ends. The geometry of the model was reconstructed from CT scans of the Visible Human Project Database and commercial anatomical databases. The geometry and mass were scaled to those of a 50th percentile male and the entire lower limb was positioned in a standing position according to the published anthropometric references. A "structural approach" was utilized to generate the FE mesh using mostly hexahedral and quadrilateral elements to enhance the computational efficiency of the model. The material properties were selected based on a synthesis on current knowledge of the constitutive models for each tissue.

Research of Adaptive Cruise Control (ACC) is an important issue of intelligent vehicle (IV). As we all known, a real and experienced driver can control vehicle's speed very well under every traffic environment of ACC working. So a direct and feasible way for establishing ACC controller is to build a human-like longitudinal control algorithm with the simulation of driver behavior of speed control. In this paper, a novel fuzzy self-tuning control algorithm of ACC is established and this controller's parameters can be tuned on-line based on the evaluation indexes that can describe how the driver consider the quality of dynamical characteristic of vehicle longitudinal dynamics. With the advantage of the controller's parameter on-line self-tuning, the computational workload from matching design of ACC controller is also efficiently reduced.

Taking full advantage of available traffic environment information, making control decisions, and then planning trajectory systematically under structured roads conditions is a critical part of intelligent vehicle. In this article, a lane-changing decision-making method for intelligent vehicle is proposed based on acceleration field. Firstly, an acceleration field related to relative velocity and relative distance was built based on the analysis of braking process, and acceleration was taken as an indicator of safety evaluation. Then, a lane-changing decision method was set up with acceleration field while considering driver’s habits, traffic efficiency and safety. Furthermore, velocity regulation was also introduced in the lane-changing decision method to make it more flexible.

When performing trajectory planning for robotic applications, there are many aspects to consider, such as the reach conditions, joint and end-effector velocities, accelerations and jerk conditions, etc. The reach conditions are dependent on the end-effector orientations and the robot kinematic structure. The reach condition feasibility is the first consideration to be addressed prior to optimizing a solution. The ‘functional’ work space or work window represents a region of feasible reach conditions, and is a sub-set of the work envelope. It is not intuitive to define. Consequently, 2D solution approaches are proposed. The 3D travel paths are decomposed to a 2D representation via radial projections. Forward kinematic representations are employed to define a 2D boundary curve for each desired end effector orientation.

It has been reported that lower extremity injuries represent a measurable portion of all moderate-to-severe automobile crash- related injuries. Thus, a simple tool to assist with the design of leg and foot injury countermeasures is desirable. The objective of this study is to develop a mathematical model which can predict load propagation and kinematics of the foot and leg in frontal automotive impacts. A multi-body model developed at the University of Virginia and validated for blunt impact to the whole foot has been used as basis for the current work. This model includes representations of the tibia, fibula, talus, hindfoot, midfoot and forefoot bones. Additionally, the model provides a means for tensioning the Achilles tendon. In the current study, the simulations conducted correspond to tests performed by the Transport Research Laboratory and the University of Nottingham on knee-amputated cadaver specimens.

Vehicle safety systems may use occupant physiological information, e.g., occupant heights and weights to further enhance occupant safety. Determining occupant physiological information in a vehicle, however, is a challenging problem due to variations in pose, lighting conditions and background complexity. In this paper, a novel occupant height estimation approach is presented. Depth information from a depth camera, e.g., Microsoft Kinect is used. In this 3D approach, first, human body and frontal face views (restricted by the Pitch and Roll values in the pose estimation) based on RGB and depth information are detected. Next, the eye location (2D coordinates) is detected from frontal facial views by Haar-cascade detectors. The eye-location co-ordinates are then transferred into vehicle co-ordinates, and seated occupant eye height is estimated according to similar triangles and fields of view of Kinect.

Rupture of the thoracic aorta is a leading cause of rapid fatality in automobile crashes, but the mechanism of this injury remains unknown. One commonly postulated mechanism is a differential motion of the aortic arch relative to the heart and its neighboring vessels caused by high-magnitude acceleration of the thorax. Recent Indy car crash data show, however, that humans can withstand accelerations exceeding 100 g with no injury to the thoracic vasculature. This paper presents a method to investigate the efficacy of acceleration as an aortic injury mechanism using high-acceleration, low chest deflection sled tests. The repeatability and predictability of the test method was evaluated using two Hybrid III tests and two tests with cadaver subjects. The cadaver tests resulted in sustained mid-spine accelerations of up to 80 g for 20 ms with peak mid-spine accelerations of up to 175 g, and maximum chest deflections lower than 11% of the total chest depth.

Periprosthetic fractures refer to the fractures that occur in the vicinity of the implants of joint replacement arthroplasty. Most of the fractures during an automotive frontal collision involve the long bones of the lower limbs (femur and tibia). Since the prevalence of persons living with lower limb joint prostheses is increasing, periprosthetic fractures that occur during vehicular accidents are likely to become a considerable burden on health care systems. It is estimated that approximately 4.0 million adults in the U.S. currently live with Total Knee Replacement (TKR) implants. Therefore, it is essential to study the injury patterns that occur in the long bone of a lower limb containing a total knee prosthesis. The aim of the present study is to develop an advanced finite element model that simulates the possible fracture patterns that are likely during vehicular accidents involving occupants who have knee joint prostheses in situ.

Taking a good longitudinal braking performance on flat and level road of tractor and semi-trailer combination as a target, in order to achieve an ideal braking force distribution among axles, while the vehicle deceleration is just depend on the driver's intention, not affected by the variation of semi-trailer mass, the paper proposes a model based vehicle mass identification and braking force distribution strategy. The strategy identifies the driver's braking intention via braking pedal, estimates semi-trailer's mass during the building process of braking pressure in brake chamber, distributes braking force among axles by using the estimated mass. And a double closed-loop regulation of the vehicle deceleration and utilization adhesion coefficient of each axle is presented, in order to eliminate the bad effect of mass estimation error, and enhance the robustness of the whole algorithm. A simulation is conducted by utilizing MATLAB/Simulink and TruckSim.

This research investigates the variation of pedestrian stance in pedestrian-automobile impact using a validated multi-body vehicle and human model. Detailed vehicle models of a small family car and a sport utility vehicle (SUV) are developed and validated for impact with a 50th percentile human male anthropometric ellipsoid model, and different pedestrian stances (struck limb forward, feet together, and struck limb backward) are investigated. The models calculate the physical trajectory of the multi-body models including head and torso accelerations, as well as pelvic force loads. This study shows that lower limb orientation during a pedestrian-automobile impact plays a dominant role in upper body kinematics of the pedestrian. Specifically, stance has a substantial effect on the subsequent impacts of the head and thorax with the vehicle. The variation in stance can change the severity of an injury incurred during an impact by changing the impact region.

A multitude of recent studies are suggestive of the EV as a paramount representative of the NEV, its development direction is transformed from “individuals adapt to vehicles” to “vehicles serve for occupants”. The multi-mode drive control technology is relatively mature in traditional auto control sphere, however, a host of EV continues to use a single control strategy, which lacks of flexibility and diversity, little if nothing interprets the vehicle performances. Furthermore, due to the complex road environment and peculiarity of vehicle occupants that different requirement has been made for vehicle performance. To solve above problems, this paper uses the key technology of mathematical statistics process in MATLAB, such as the mean, linear fitting and discrete algorithms to clean up, screening and classification the original data in general rules, and based on short trips in the segments of kinematics analysis method to establish a representative of quintessential driving cycle.

The development of a collision severity model can serve as an important tool in understanding the requirements for devising countermeasures to improve occupant safety and traffic safety. Collision type, weather conditions, and driver intoxication are some of the factors that may influence motor vehicle collisions. The objective of this study is to use artificial neural networks (ANNs) to identify the major determinants or contributors to fatal collisions based on various driver, vehicle, and environment characteristics obtained from collision data from Transport Canada. The developed model will have the capability to predict similar collision outcomes based on the variables analyzed in this study. A multilayer perceptron (MLP) neural network model with feed-forward back-propagation architecture is used to develop a generalized model for predicting collision severity. The model output, collision severity, is divided into three categories - fatal, injury, and property damage only.

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.

Mobility assessment for combat vehicles is often a great challenge for the military due to various subjective attributes. The attributes' characteristics vary significantly depending on the vehicle type and its operating environments such as terrain, weather, and human factors. A clear definition and relationship between multiple attributes including human factors is necessary to assess mobility. To the best of authors' knowledge, many existing mobility assessment techniques use complex analytical methods and focus on individual attributes. In this paper, for the first time, the authors propose a novel approach to define vehicle mobility and its influencing attributes using qualitative linguistic fuzzy variables, which are defined as having values between 0 and 1. The authors also propose a fuzzy logic mobility (FLM) model and a simulation approach to assess a combat vehicle's mobility.