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NOx emissions from diesel passenger vehicles affect the atmospheric environment. It is difficult to evaluate the NOx emissions influenced by environmental conditions such as humidity and temperature, traffic conditions, driving patterns, etc. In the authors’ previous study, real-driving experiments were performed on city and highway routes using a diesel passenger car with only an exhaust gas recirculation system. A statistical prediction model of NOx emissions was considered for simple estimations in the real world using instantaneous vehicle data measured by the portable emissions measurement system and global positioning system. The prediction model consisted of explanatory variables, such as velocity, acceleration, road gradient, and position of transmission gear. Using the explanatory variables, NOx emissions on the city and highway routes was well predicted using a diesel vehicle without NOx reduction devices.

Enemy threats during Operation Desert Storm drove many Allied ground attack aircraft to medium altitude to deliver their weapons. Although many Precision Guided Munitions (PGMs) proved to be highly accurate at increased heights, the majority of bombs dropped from medium altitude were low-drag general purpose bombs (LDGP). The aircraft carrying the LDGPs traded accuracy for altitude in order to reduce the risk to aircraft and crews. This event helped highlight the need to drop LDGP weapons more accurately. Additionally, there are environments where even many current PGMs are ineffective. During the Gulf War, for example, smoke from the Kuwaiti oil fires obscured the skies, prohibiting the use of laser guided bombs. Currently there is a program in testing that is designed to fill these operational gaps. The Joint Direct Attack Munition (JDAM) is being developed to convert our inventory of LDGPs into PGMs and modeling/simulation is playing a vital role in its design.

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.

This paper describes a case study led by Science Applications International Corporation (SAIC) of Dayton, OH USA and Dearborn Group Inc. to prove the feasibility of employing Telematics technologies to the vehicle test and measurement industry. Many test functions can be automated through the use of secure wireless technologies. For example, vehicle data can be dynamically monitored on the vehicle and data meeting pre-determined criteria could be downloaded via the wireless communications center. Additionally, central, real-time wireless monitoring of vehicle fleets provides the vehicle fleet manager with the ability to manage multiple tests simultaneously, thus improving efficiencies and potentially reducing manpower costs and compressing test schedules.

Proliferation of real time inter-vehicular communications provides new sources for exploitable positioning data. Vehicles can, under numerous situations, have GPS satellite shortages but there will always be vehicles in their vicinity, with a different set of satellites, to provide them with useful navigation information. In this paper, we propose a cooperative positioning technique making use of reliable positions of some vehicles to enhance positioning estimates of some others. We will exploit the useful inter-vehicle data flow to extract good position measurements from vehicles with good GPS satellite LOS (line of sight), in order to enhance low positioning accuracy of other vehicles, in the neighborhood. The integration of such information will be done using geometric data fusion approach.

Hawker de Havilland has undertaken research to utilize the benefits of using industrial robots for high accuracy drilling and trimming of composite components in preparation for assembly. Central to this development is the metrology system used to provide online position and orientation feedback to the robot control. Feedback is required for both digital calibration of the robotic work cell and error correction for any robot deflection. This paper presents a comparative study between two off-the-shelf metrology technologies; one being Indoor Infrared Global Positioning System (IRGPS), and the other Laser Tracking. The experiments use the task of robotic drilling in carbon composites to evaluate the effectiveness and limitations of each metrology system by measure of achievable drilling accuracy and ease of application.

Although usability is a broad subject, the literature relating to in-car navigation devices tends to focus primarily on efficiency of use. In this paper, we explore the neglected issue of learnability, that is the ease with which users learn to use, as well as some justification of its importance as a distinct issue. We present an evaluation method designed to identify learnability related problems with in-car navigation devices, along with a complete set of basic tasks with which to apply it to. Our method is applied to a device, identifying various learnability problems. Recommendations for builders of in-car systems are made based on these observations.

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.

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.

In this paper we present the concept of green routing or eco routing and how one would implement this using attributes defined in real time traffic information and digital road map. The routing referred to this paper applies mainly to road networks where one wants to get from point A to point B by minimizing the fuel consumed in a vehicle. The new model of speed profile prediction which yields stop and go speed at the start, end and zero of the acceleration under sub-node and link conditions is described. The results for the lowest fuel consumption show about 8.5% of simulated routes and 5% (9%) of vehicle test routes saving in fuel, when compared to that for the existing route planning course (recommended and short distance route), even traveling time about 10% longer time. These results clearly show prediction accuracy of fuel consumption between modeling and measurement is below the average about 12%.

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).

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.

Android Automotive OS (AAOS) has been gaining popularity in recent years, with several OEMs across the world already deploying it or planning to in the near future. Besides the benefit of a well-known, customizable and secure operating system for OEMs, AAOS allows third-party app developers to offer their apps on vehicles of several manufacturers at the same time. Currently, there are 55 apps for AAOS that can be categorized as media, navigation or point-of-interest apps. Specifically the latter two categories allow the third-parties to collect certain sensor data directly from the vehicle. Furthermore, the latest version of AAOS also allows the OEM to configure and collect In-Vehicle Infotainment (IVI) and vehicle data (called OEM telemetry). However, increasing connectivity and integration with the in-vehicle network comes at the expense of user privacy. Previous works have shown that vehicular sensor data often contains personally identifiable information (PII).