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This paper describes the risk of injury to the rider in a crash using a statistical model based on real-world accident data. We analyzed the road traffic accidents data in Los Angeles and Hanover. Logistic regression modeling technique was used to clarify the relationship among probabilities of minor, serious, fatal injury risk to the rider, and the influence of risk factors in accidents involving opposing vehicle contact point, motorcycle contact point, opposing vehicle speed, motorcycle speed, relative heading angle of impact, and helmet use. The odds ratio, which was adjusted for risk factors simultaneously, was estimated by using the developed technique, and was compared with the effects of risk factors individually. The results showed that there was a statistically significant relationship between minor and serious injuries and opposing vehicle speed, motorcycle speed and opposing vehicle contact point.

In France, the current evolution in the number of road accident victims is less favourable for the moped rider category than for the majority of others. In addition, the proportion of scooters in the moped park is currently considerably increasing. The aim of the research work presented is to consider if an explanation for the bad results for this category of users, in terms of road safety, can be found through the study of the dynamic performances of this type of vehicle. We thus carried out an experiment aimed at comparing the dynamic capacities of a “Classic” moped with those of a “Scooter” model. Two mopeds, one of each type, were fitted with an embedded measurement system. They were driven for several tests on a track by a professional test pilot. The results of this study are more in favour of the “Scooter” moped, which seems to offer equivalent, or even better, dynamic capacities than the “Classic” one.

With the improved safety performance of vehicles, the number of accidents has been decreasing. However, accidents due to driver distraction still occur, which means that there is a high need to determine whether a driver is properly looking at the surroundings. Meanwhile, with the trend toward partial automatic driving of vehicles in recent years, it is also urgently required that the state of the driver be grasped. Even if automatic driving is not installed, it is desired that the state of the driver be grasped and an application for control be performed depending on the state of the driver. Under these circumstances, we have built an algorithm that determines of the direction a driver is looking, to make a basic determination of whether or not the driver is in a state suitable for safe driving of the vehicle.

Abstract In the United States (USA), transportation is the largest single source of greenhouse gas (GHG) emissions, representing 27% of total GHGs emitted in 2020. Eighty-three percent of these came from road transport, and 57% from light-duty vehicles (LDVs). Internal combustion engine (ICE) vehicles, which still form the bulk of the United States (US) fleet, struggle to meet climate change targets. Despite increasingly stringent regulatory mechanisms and technology improvements, only three US states have been able to reduce their transport emissions to the target of below 1990 levels. Fifteen states have made some headway to within 10% of their 1990 baseline. Largely, however, it appears that current strategies are not generating effective results. Current climate-change mitigation measures in road transport tend to be predominantly technological.

General agreement exists that the ultimate goals of traffic accident research are to reduce fatality, mitigate injury and decrease economic loss to society. Although massive quantities of data have been collected in local, national and international programs, attempts by analysts to use these data to explore ideas or support hypotheses have been met by a variety of problems. Specifically, the coded variables in the different files are not consistent and little information on accident etiology is collected. Examples of the inadequacies of present data in terms of the collected and coded variables are shown. The vehicular, environmental and human (consisting of human factors and injury factors) variables are disproportionately represented in most existing data files in terms of recognized statistical evidence of accident causation. A systems approach is needed to identify critical, currently neglected variables and develop units of measurement and data collection procedures.

Motor vehicle crashes involving child Vulnerable Road Users (VRUs) remain a critical public health concern in the United States. While previous studies successfully utilized the crash scenario typology to examine traffic crashes, these studies focus on all types of motor vehicle crashes thus the method might not apply to VRU crashes. Therefore, to better understand the context and causes of child VRU crashes on the U.S. road, this paper proposes a multi-step framework to define crash scenario typology based on the Fatality Analysis Reporting System (FARS) and the Crash Report Sampling System (CRSS). A comprehensive examination of the data elements in FARS and CRSS was first conducted to determine elements that could facilitate crash scenario identification from a systematic perspective. A follow-up context description depicts the typical behavioral, environmental, and vehicular conditions associated with an identified crash scenario.

A program for investigating frontal force accidents has been underway for approximately 2 years at Wayne State University. It differs from most investigations in that each accident was analyzed in detail. Accidents in which the cause of injury could not be accurately ascertained were eliminated. Thus, a limited number of cases were investigated in detail rather than depending on statistics from a large number of accidents. It was necessary to establish a comprehensive scale for the detailed investigations because available rating scales did not provide fine enough injury increments. A degree of injury scale has been devised which can be modified as new data on injury are acquired. The scale ranges from very minor injuries to fatalities with the following six major categories of injury in increasing order of severity: minor, moderate, moderate-severe, severe,critical, fatal. Each category has several subdivisions with a detailed description of each.

Sound signature design is gaining more importance within global auto manufacturers. ‘Sportiness’ is one of the important point to consider while designing a sound character of a car for passionate drivers and those who love aggressive driving. Nowadays automobile manufacturers are more focused in developing a typical sound signature for their cars as a ‘unique design strategy’ to attract a niche segment of the market and to define their brand image. Exhaust system is one of the major aggregate determining the sound character of ICE vehicles which in turn has the direct influence on the customer perception of the vehicle and the Brand image and also the human comfort both inside and outside the cabin. This research work focuses on novel approaches to identify frequency range and order content by a detailed study of subjective feelings based on psycho-acoustics. Sound samples of various benchmark sporty vehicles have been studied and analyzed based on sound quality parameters.

The major contribution of this effort to the investigation of head injury is the design and construction of a machine having the following functional goals: 1. Delivery of a reproducible acceleration-time profile to a primate head. 2. Capability of increasing the acceleration magnitude while retaining a similar acceleration-time profile. 3. The path traversed by the head must be constrained during the acceleration. 4. The forces applied to the head must be distributed so as not to produce gross damage to the brain or skull. The machine that has evolved is designated as the Head Acceleration Device II (HAD-II). Basically, this machine consists of an axial cam cut on the face of a flywheel, and the cam follower imparts the motion of the cam through a linkage to a helmet in which the test subject's head has been potted. The helmet is rigidly pivoted causing the head to be rotated through an arc of 45 degrees.

This paper discusses a data sampling plan that provides an accident data file representative of the national population. It is a system for data processing, storage, and retrieval to allow early determination of trends in accident, injury, and fatality frequencies. The system of data collection discussed lies somewhere between the very detailed team approach and the routine police reporting as established independently by the states.

With the wealth of information available on the Internet, there is a pressing need for a standards-based platform that allows the mobile vehicle to access telematics services that leverage this information in a safe and consistent manner while minimizing driver distraction. This paper discusses a distributed architecture that enables the rapid development and deployment of such services by offloading data- and compute-intensive operations to a remote server, while executing location-sensitive and vehicle-centric services on the vehicle. These services may range from safety and diagnostics services to multi-media infotainment. It further examines how the various underlying technologies from different spheres such as location, wireless, satellite, Internet and m-Commerce are brought together to complement each other, as well as the relevant emerging standards in each area.

Driver behaviour can strongly affect fuel consumption, and driver training in eco-driving techniques has been shown to reduce fuel consumption by 10% on average. However the effects of this training can be short-lived, so there is an apparent need for continuous monitoring of driver behaviour. This study presents a driver advisory tool which encourages eco-driving, and its evaluation in the field. The system, developed by Ashwoods Automotive Ltd (UK) and the University of Bath (UK), is aimed at fleet operators of light commercial vehicles, where the driver is typically a company employee. A significant strength of the system is that it has been designed for easy integration with the vehicle CAN-bus, reducing complexity and cost. By considering the Inertial Power Surrogate (speed times acceleration) the core algorithm is able to identify behaviour which is likely to increase fuel consumption.

Commercial vehicle operators and governments around the world are looking for ways to cut down on fuel consumption for economic and environmental reasons. Two main factors affecting the fuel consumption of a vehicle are the drive route and the driver behavior. The drive route can be specified by information such as speed limit, road grade, road curvature, traffic etc. The driver behavior, on the other hand, is difficult to classify and can be responsible for as much as 35% variation in fuel consumption. In this work, nearly 600,000 miles of drive data is utilized to identify driving behaviors that significantly affect fuel consumption. Based on this analysis, driving scenarios and related driver behaviors are identified that result in the most efficient vehicle operation. A driver assistance system is presented in this paper that assists the driver in driving more efficiently by issuing scenario specific advice.

A method for detecting drivers' intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.

The research of driver behavior characteristics has been a focus of vehicle handling and stability performance. With the driver preview effort, many different driver preview models of direction control have been proposed and the simulations of driver-vehicle-road closed-loop system made. But in the simulation, most of the conventional models have the same precondition that the road was simply described as a pre-given preview course. How to simulate the driver dynamically deciding vehicle preview course based on the real road circumstance is the key to the further research of the driver model. In this paper, a new driver direction control model is established, which is called the Optimal Preview Lateral Acceleration (OPLA) Model and divided into three sub-models: driver’s information identification model, driver’s fuzzy decision model of vehicle preview course and driver’s performance first-order correction model.

Simple, effective, and appropriately placed visual information must be available to the driver as part of a well designed Human Machine Interface (HMI). Visual interfaces for Advanced Driver Assistance Systems (ADAS), secondary task control, and safety warnings should attempt to minimize both driver reaction time to warnings and the workload on the driver to comprehend a warning or respond to driving advice or information. A driving simulator study was designed and executed to assess the appropriateness and effectiveness of three display concepts. The study directly compared the driver warning reaction and overall workload for three visual HMIs: the conventional instrument panel and center-stack displays (IP/CS), an idealized heads up display (HUD), and the Communication Steering Wheel (CSW) display. Study participants were required to respond to secondary convenience control tasks (4 tasks); safety warnings (3 scenarios); and also a peripheral detection task (PDT).

Driving skills and driving experience develop differently between a civilian and a military service member. Since 2000, the Department of Defense reports that two-thirds of non-related to war fatalities among active duty service members were due to transportation-related incidents. In addition, vehicle crashes are the leading non-related to war cause of both fatalities and serious injuries among active duty Marines. A pilot safe driving program for Marines was jointly developed by the Richard Petty Driving Experience and Clemson University Automotive Safety Research Institute. The pilot program includes four modules based on leading causes of vehicle crashes, and uses classroom and behind the wheel components to improve and reinforce safe driving skills and knowledge. The assessment results of this pilot program conducted with 192 Marines in September 2011 at Camp LeJeune, NC are presented and discussed.

A computer code oriented to S.I. engine control and powertrain simulation is presented. The model predicts engine and driveline states, taking into account the dynamics of air and fuel flows into the intake manifold and the transient response of crankshaft, clutch, transmission gearing and vehicle. The whole model is integrated in the code O.D.E.C.S., now in use at Magneti Marelli, and is based on a hierarchical structure composed of different classes of models, ranging from black-box Neural Network to grey-box mean value models. By adopting the proposed approach, a satisfactory accuracy is achieved with limited computational demand, which makes the model suitable for the optimization of engine control strategies. Furthermore, in order to simulate the driver behavior during the assigned vehicle mission profile, two drive controllers have been implemented for throttle and brakes actuation, based on classical PID and fuzzy-logic theory.

Authors were challenged with a task of developing a full vehicle simulation model, with a target to simulate the electrical system performance and perform digital tests like Battery Charge Balance, in addition to the fuel efficiency estimation. A vehicle is a complicated problem or domain to model, due to the complexities of subsystems. Even more difficult task is to have a control algorithm which controls the vehicle model with the required control signals to follow the test specification. Particularly, simulating the control of a vehicle with a manual transmission is complicated due to many associated control signals (Throttle, Brake and Clutch) and interruptions like gear changes. In this paper, the development of a full vehicle model aimed at the assessment of electrical system performance of the vehicle is discussed in brief.

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.