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1. On page 111, the authors have described a method to assess driver distraction. In this method, participants maintained a white square size on a forward display by using a game gas pedal of like in car-following situation. The size of the white square is determined by calculating the distance to a virtual lead vehicle. The formulas to correct are used to explain variation of acceleration of the virtual lead vehicle. The authors inadvertently incorporated old formulas they had used previously. In the experiments discussed in the article, the corrected formulas were used. Therefore, there is no change in the results. The following from the article:

A driving simulator capable of duplicating the critical sensations incurred during a spin, or when a driver is engaged in drift cornering, was constructed by Mitsubishi Heavy Industries, Ltd., and Hiromichi Nozaki of Kogakuin University. Specifically, the simulator allows independent movement along three degrees of freedom and is capable of exhibiting extreme yaw and lateral acceleration behaviors. Utilizing this simulator, the control characteristics of drift cornering have become better understood. For example, after a J-turn behavior experiment involving yaw angle velocity at the moment when the drivers attention transitions to resuming straight ahead driving, it is now understood that there are major changes in driver behavior in circumstances when simulator motions are turned off, when only lateral acceleration motion is applied, when only yaw motion is applied, and when combined motions (yaw + lateral acceleration) are applied.

Driver distraction is a topic of considerable interest, with the public debate centering on the use of cell phones and texting while driving. However, the driver distraction/overload issue is really much larger. It concerns specific tasks such as entering destinations on navigation systems, retrieving songs on MP3 players, accessing web pages, checking stocks, editing spreadsheets, and performing other tasks on smart phones, as well as, more generally, using in-vehicle information systems. Five major problems related to distraction/overload research and engineering and their solutions are addressed in this paper.

Accident data show that the head and the chest are the most frequently injured body regions in side impact fatal accidents. Curtain side air bag (CSA) and thorax side air bag (SAB) have been installed by manufacturers for the protection devices for these injuries. In this research, first we studied the recent side impact accident data in Japan and verified that the head and chest continued to be the most frequently injured body regions in fatal accidents. Second, we studied the occupant seating postures in vehicles on the roads, and found from the vehicle's side view that the head location of 56% of the drivers was in line or overlapped with the vehicle's B-pillar. This observation suggests that in side collisions head injuries may occur frequently due to contacts with the B-pillar. Third, we conducted a side impact test series for struck vehicles with and without CSA and SAB.

Accident statistics shows pedestrian accident fatalities as one of the important concerns globally. In view of this, new test protocols for pedestrian safety have been drafted in regulation as well as in consumer group. Also as per new ENCAP requirements, pedestrian safety assessment is used as one of the four assessment criteria's (Adult protection, child safety, pedestrian safety, safety assist) in deciding the overall vehicle safety. Hence today importance of pedestrian safety is perceived as never before in vehicle development program. Basically pedestrian safety evaluation involves subsystem level (head form, upper leg form and lower leg form) impact tests representing human body parts, at specific region on test vehicle with injury limits to decide the severity of impact. In general these injuries are governed by vehicle styling, vehicle stiffness, hard points clearances from vehicle exterior like bonnet, bumper etc.

Prediction of drowsiness based on an objective measure is demanded in machine and vehicle operations, in which human error may cause fatal accidents. Recently, we focused on the pupil which is controlled by the autonomic nervous system, easily and non-invasively observable from the outside of the body. Prior to the large low frequency pupil-diameter fluctuation, which is known to associate with drowsiness, a Gradual Miosis was observed in most subjects. During this miosis period, the subjects were not yet aware of their drowsiness. We have developed a software system which automatically detects the Gradual Miosis in real time.

Approximately 600,000 fatalities occur each year as a result of pedestrians being impacted by motor vehicles (World Bank, 2008). Previous studies (Heller et al., 2009) have utilized databases such as the National Inpatient Sample (NIS) to gain a more thorough understanding of the common injury patterns that occur in real-world traffic collisions involving pedestrians in the United States. The NIS contains records on five to eight million hospital stays annually and provides a wealth of information regarding injuries to hospitalized pedestrian casualties in the U.S. Because of the large number of applicable records in the NIS and the randomized sampling procedure, the data can be used to complete analyses that are not possible with smaller databases such as the Pedestrian Crash Data Study (PCDS), which is not intended to be statistically representative of pedestrian crashes in general.

The statistical analysis of vehicle crash accident data is generally problematic. Data from commonly used sources is almost never without error and complete. Consequently, many analyses are contaminated with modeling and system identification errors. In some cases the effect of influential factors such as crash severity (the most significant component being speed) driver behavior prior to the crash, etc. on vehicle and occupant outcome is not adequately addressed. The speed that the vehicle is traveling at the initiation of a crash is a significant contributor to occupant risk. Not incorporating it may make an accident analysis irrelevant; however, despite its importance this information is not included in many of the commonly used crash data bases, such as the Fatality Analysis Reporting System (FARS). Missing speed information can result in potential errors propagating throughout the analysis, unless a method is developed to account for the missing information.

Pre-collision systems (PCS) use forward-looking sensors to detect the location and motion of vehicles ahead and provide a sequence of actions to help the driver either avoid striking the rear-end of another vehicle or mitigate the severity of the crash. The actions include driver alerts, amplification of driver braking as distance decreases (dynamic brake support, DBS), and automatic braking if the driver has not acted or has not acted sufficiently (crash imminent braking, CIB). Recent efforts by various organizations have sought to define PCS objective test procedures and test equipment in support of consumer information programs and potential certification. This paper presents results and insights from conducting DBS and CIB tests on two production vehicles sold in the US. Eleven scenarios are used to assess the systems' performance. The two systems' performance shows that commercial systems can be quite different.

Advanced driver assistance systems like cooperative adaptive cruise control (CACC) are designed to exploit information provided by vehicle-to-vehicle (V2V) and/or infrastructure-to-vehicle (I2V) communication systems to achieve desired objectives such as safety, traffic fluidity or fuel economy. In a day to day traffic scenario, the presence of unknown disturbances complicates achieving these objectives. In particular, CACC benefits in terms of fuel economy require the prediction of the behavior of a preceding vehicle during a finite time horizon. This paper suggests an estimation method based on actual and past inter-vehicle distance data as well as on traffic and upcoming traffic lights. This information is used to train a set of nonlinear, autoregressive (NARX) models. Two scenarios are investigated, one of them assumes a V2V communication with the predecessor, the other uses only data acquired by on-board vehicle sensors.

A key aim of research into cell phone tasks is to obtain an unbiased estimate of their relative risk (RR) for crashes. This paper re-examines five RR estimates of cell phone conversation in automobiles. The Toronto and Australian studies estimated an RR near 4, but used subjective estimates of driving and crash times. The OnStar, 100-Car, and a recent naturalistic study used objective measures of driving and crash times and estimated an RR near 1, not 4 - a major discrepancy. Analysis of data from GPS trip studies shows that people were in the car only 20% of the time on any given prior day at the same clock time they were in the car on a later day. Hence, the Toronto estimate of driving time during control windows must be reduced from 10 to 2 min.

This paper presents a novel methodology to develop and validate fuel consumption models of Refuse Collecting Vehicles (RCVs). The model development is based on the improvement of the classic approach. The validation methodology is based on recording vehicle drive cycles by the use of a low cost data acquisition system and post processing them by the use of GPS and map data. The corrected data are used to feed the mathematical energy models and the fuel consumption is estimated. In order to validate the proposed system, the fuel consumption estimated from these models is compared with real filling station refueling records. This comparison shows that these models are accurate to within 5%.

We present a method for the estimation of vehicle mass and road gradient for a light passenger vehicle. The estimation method uses information normally available on the vehicle CAN bus without the addition of extra sensors. A composite parameter estimation algorithm incorporating a nonlinear adaptive observer structure uses vehicle speed over ground and driving torque to estimate mass and road gradient. A system of filters is used to avoid deriving acceleration directly from wheel speed. In addition, a novel data fusion method makes use of the regressor structure to introduce information from other sensors in the vehicle. The dynamics of the additional sensors must be able to be parameterised using the same parameterisation as the complete vehicle system dynamics. In this case we make use of an Inertial Measurement Unit (IMU) which is part of the vehicle safety and Advanced Driver Assist Systems (ADAS).

The goal of reducing fuel consumption and CO2-Emission is leading to turbo-charged combustion engines that deliver high torque at low speeds (down speeding). To meet NVH requirements damper technologies such as DMF (Dual Mass Flywheel) are established, leading to reduced space for the clutch system. Specific measures need to be considered if switching over from SMF (Single Mass Flywheel) to DMF [8]. Doing so has an impact on thermal behavior of the clutch system, for example due to reduced and different distribution of thermal masses and heat transfer to the surroundings. Taking these trends into account, clutch systems within vehicle powertrains are facing challenges to meet requirements e.g. clutch life, cost targets and space limitation. The clutch development process must also ensure delivery of a clutch system that meets requirements taking boundary conditions such as load cycles and driver behavior into account.

As per the 2018 MoRTH accident report, there were 467,044 accidents, out of which 137,726 were fatal which resulted in 151,417 fatalities. In order to get an idea of the reasons for injuries and estimate the benefits of any intervention, a mathematical model should go a long way. This study is aimed at the development of such a model to predict the injuries sustained by the occupants of an M1 vehicle. We used a detailed accident database of 'Road Accident Sampling System India' (RASSI). RASSI, since 2011, has been collecting traffic accident data scientific across various locations in India. In the data, the occupant injuries are classified as No injury, Minor, Serious and Fatal We used the data of about 4700+ M1 occupants for the study & used almost 40 input parameters to determine the outcome. Based on the data, an algorithm was developed with an overall accuracy of about 67%. The parameters represented human, infrastructure, and environment.

In the last decade, the equivalent circuit model has been utilized to model lithium-ion batteries for electric vehicle applications. Different researchers have proposed a variety of equivalent circuit models from simple to complex ones. The parameters required to describe and build these equivalent circuit models are being extracted from the Hybrid Pulse Power Characterization (HPPC) Test data. This paper describes the process of the extraction of the equivalent circuit model parameters to build the battery models using different test methodologies such as HPPC and its modified versions. It also presents a case study with validated test results for a commercial light weight EV. Firstly, showing how the cell is characterized and then how the pack level cooling is developed to reach the required range based on an aggressive drive cycle.

To prevent the severe consequences of unsafe driving behaviors, it is crucial to monitor and analyze the state of the driver. Developing an effective driver state monitoring (DSM) systems is particularly challenging due to limited computation capabilities of embedded systems in automobiles and the need for finishing processing in real-time. However, most of the existing research work was conducted in a lab environment with expensive equipment while lacking in-car benchmarking and validation. In this paper, a DSM system that estimates driver's alertness and drowsiness level as well as performs emotion detection built with a cost-effective embedded system is presented. The proposed system consists of a mono camera that captures driver's facial image in real-time and a machine learning based detection algorithm that detects facial landmark points and use that information to infer driver's state.

Driving inattention caused by drowsiness has been a significant reason for vehicle crash accidents, and there is a critical need to augment driving safety by monitoring driver drowsiness behaviors. For real-time drowsy driving awareness, we propose a vision-based driver drowsiness monitoring system (DDMS) for driver drowsiness behavior recognition and analysis. First, an infrared camera is deployed in-vehicle to capture the driver’s facial and head information in naturalistic driving scenarios, in which the driver may or may not wear glasses or sunglasses. Second, we propose and design a multi-modal features representation approach based on facial landmarks, and head pose which is retrieved in a convolutional neural network (CNN) regression model. Finally, an extreme learning machine (ELM) model is proposed to fuse the facial landmark, recognition model and pose orientation for drowsiness detection. The DDMS gives promptly warning to the driver once a drowsiness event is detected.

Automotive clutches are prone to rigid body torsional vibrations during engagement, a phenomenon referred to as take-up judder. This is also accompanied by fore and aft vehicle motions. Aside from driver behaviour in sudden release of clutch pedal (resulting in loss of clamp load), and type and state of friction lining material, the interfacial slip speed and contact temperature can significantly affect the propensity of clutch to judder. The ability to accurately predict the judder phenomenon relies significantly on the determination of operational frictional characteristics of the clutch lining material. This is dependent upon contact pressure, temperature and interfacial slip speed. The current study investigates the ability to predict clutch judder vibration with the degree of complexity of the torsional dynamics model. For this purpose, the results from a four and nine degrees of freedom dynamics models are compared and discussed.

With the electrification of vehicles, new questions and problems are rising in the field of NVH. The in-cabin noise was reduced significantly due to the new drive system. Additionally, the spectral composition of this noise changed dramatically. While the reduction of the in-cabin sound pressure levels is generally welcomed by customers and engineers alike, the predominantly high-pitched tonal sounds of the electrical drives are normally perceived with less enthusiasm. Active sound design can help both in masking those noises, or at the least embed them in new harmonic contexts so their annoyance can be reduced. A variety of research in the field of traffic psychology shows that acoustical feedback can alter the driving behavior. Based on this, our idea is, that if certain sounds induce specific reactions in drivers, a specifically designed active sound design could be used to influence said behavior.