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Far-side occupants are not addressed in current government regulations around the world even though they account for up to 40% of occupant HARM in side impact crashes. Consequently, there are very few crash tests with far-side dummies available to researchers. Sled tests are frequently used to replicate the dynamic conditions of a full-scale crash test in a controlled setting. However, in far-side crashes the complexity of the occupant kinematics is increased by the longer duration of the motion and by the increased rotation of the vehicle. The successful duplication of occupant motion in these crashes confirms that a sled test is an effective, cost-efficient means of testing and developing far-side occupant restraints or injury countermeasures.

The goal of this interview analysis was to explore and document the perceptions of two unique lane centering systems (S90’s Pilot Assist and CT6’s Super Cruise). Both systems offer a similar type of functionality (adaptive cruise control and lane centering), but have significantly different design philosophies and HMI (Human-Machine Interface) implementations. Twenty-four drivers drove one of the two vehicle models for a month as part of a field operational test (FOT) study. Upon vehicle return, drivers took part in a 60-minute semi-structured interview covering their perceptions of the vehicle’s various advanced driver-assistance systems (ADAS). Transcripts of the interviews were coded by two researchers, who tagged each statement with relevant system and perception code labels. For analysis, the perception codes were grouped into larger thematic bins of safety, comfort, driver attention, and system performance.

This investigation addresses and evaluates: (1) belted drivers in frontal crashes; (2) crashes divided into low, medium, and high severity; (3) air-bag-equipped passenger vehicles separated into either model years 1985 - 1997 (with airbags) or model years 1998 - 2008; (4) rate of Harm as a function of crash severity and vehicle model year; and (5) injury patterns associated with injured body regions and the involved physical components, by vehicle model year. Comparisons are made between the injury patterns related to drivers seated in vehicles manufactured before 1998 and those manufactured 1998 or later. The purpose of this comparative analysis is to establish how driver injury patterns may have changed as a result of the introduction of more recent safety belt technology, advanced airbags, or structural changes.

For children seated next to the struck side, real-world crash outcome was determined for the rear-seat of passenger vehicles over the entire range of side impact crash severities. The method was first to calculate the actual risk for an occupant based on field data. The data sources were non-rollover, tow-away crashes from the 1997 - 2009 National Automotive Sampling System. By limiting the struck passenger vehicle to model year 1985 or newer, field data were identified for a total of 588 children. In all crashes, the child was seated in the rear-seat area on the struck side of the passenger vehicle. A matrix of MADYMO model simulations calculated the response of child dummies over the entire range of the field data. Age-dependent, moderate-to-serious (AIS ≥ 2) injury risk curves were derived and evaluated for children in side impact. Risks to the children were calculated by combining the derived child risk curves with the MADYMO model simulations.

The fuel energy consumption of subsonic air transportation is examined. The focus is on identification and quantification of fundamental engineering design tradeoffs which drive the design of subsonic tube and wing transport aircraft. The sensitivities of energy efficiency to recent and forecast technology developments are also examined.

Vehicle steering control can provide assistance to drivers for lane keeping, automated trajectory following, or more extreme evasive maneuvers. An active torque control steering system is designed using Linear Quadratic Regulator (LQR), and its performance was evaluated using the commercial software CARSIM. The system is developed to maintain a desired trajectory for the vehicle in performing evasive maneuvers to avoid imminent crash scenarios. In order to better understand the behavior of the system with different controllers, a simple bicycle model of the vehicle was developed, and an LQR controller was developed to control vehicle steering torque. The controller uses yaw angle, yaw rate, velocity, and position of the vehicle to generate the required steering torque to follow the desired trajectory. An observer was developed to estimate non-measured parameters. Trajectories are generated to follow a lane change before reaching the obstacle.

This paper presents the results of a study of how people interacted with a production voice-command based interface while driving on public roadways. Tasks included phone contact calling, full address destination entry, and point-of-interest (POI) selection. Baseline driving and driving while engaging in multiple-levels of an auditory-vocal cognitive reference task and manual radio tuning were used as comparison points. Measures included self-reported workload, task performance, physiological arousal, glance behavior, and vehicle control for an analysis sample of 48 participants (gender balanced across ages 21-68). Task analysis and glance measures confirm earlier findings that voice-command interfaces do not always allow the driver to keep their hands on the wheel and eyes on the road, as some assume.

The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-toend framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large onroad dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.

Advanced driver assistance systems (ADAS) are an increasingly common feature of modern vehicles. The influence of such systems on driver behavior, particularly in regards to the effects of intermittent warning systems, is sparsely studied to date. This paper examines dynamic changes in physiological and operational behavior during lane departure warnings (LDW) in two commercial automotive systems utilizing on-road data. Alerts from the systems, one using auditory and the other haptic LDWs, were monitored during highway driving conditions. LDW events were monitored during periods of single-task driving and dual-task driving. Dual-task periods consisted of the driver interacting with the vehicle’s factory infotainment system or a smartphone to perform secondary visual-manual (e.g., radio tuning, contact dialing, etc.) or auditory-vocal (e.g. destination address entry, contact dialing, etc.) tasks.

The research question investigated in this study is what are the key attributes of foot and ankle injury in the between-rail frontal crash? For the foot and ankle, what was the type of interior surface contacted and the type of resulting trauma? The method was to study with in-depth case reviews of NASS-CDS cases where a driver suffered an AIS=2 foot or ankle injury in between-rail crashes. Cases were limited to belted occupants in vehicles equipped with air bags. The reviews concentrated on coded and non-coded data, identifying especially those factors contributing to the injuries of the driver's foot/ankle. This study examines real-world crash data between the years 1997-2009 with a focus on frontal crashes involving 1997 and later model year vehicles. The raw data count for between-rail crashes was 732, corresponding to 227,305 weighted, tow-away crashes.

In today's business climate rapid access to, and implementation of, new technology is essential to enhance competitive advantage. In the past, universities have been used for research contracts, but to fully utilize the intellectual resources of education institutions, it is essential to approach these relationships from a new basis: alliance. Alliances permit both parties to become active participants and achieve mutually beneficial goals. This paper will examine the drivers and challenges for industrial -- university alliances from both the industrial and academic perspectives.

The William Lehman Injury Research Center has conducted multi-disciplinary investigations of one hundred seventy-eight crashes involving adult occupants protected by safety belts and air bags. In all cases, serious injuries were suspected. Nine cases involved serious heart injuries. These cases are not representative of crashes in general. However, when used in conjunction with National Accident Sampling System; Crashworthiness Data System (NASS/CDS) they provide insight into the most severe injuries suffered by restrained occupants in frontal crashes. Heart injuries are rare, but when they occur they are usually life threatening. NASS/CDS shows that heart injuries comprise about 0.2% of the injuries in frontal tow-away crashes. In the NHTSA file of Special Crash Investigations (SCI) of air bag cases, heart injuries are reported in 1% of the occupants over 15 years of age. Twenty-five percent of the fatally injured occupants had heart injuries, and 83% of those with heart injury died.

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 nearly 77 million baby boomers, born between 1946 and 1964, can say that they are the automobile generation. Now turning 60 one every seven seconds, what are the new safety challenges and opportunities posed by the next generation of older adults? This paper presents a modified Haddon matrix to identify key product development, design and liability issues confronting the automobile industry and related stakeholders. The industry is now at a critical juncture to address the development of key technological innovations as well as the changing policy and liability environments being reshaped by an aging population.

This study tracks vehicle design changes and frontal crash test performance in NHTSA's NCAP and IIHS consumer information tests since the mid-90s for the Honda Accord and Toyota Camry. The objective was to provide insights into how passenger cars have changed in response to frontal consumer information tests. The history of major design changes for each model was researched and documented. The occupant injury measures from both NHTSA and IIHS were computed and the ratings compiled for several generations of both vehicles. Changes in vehicle crash pulse and occupant injury measures from both NCAP and IIHS tests, and from Canadian low speed rigid barrier tests, when available, were used to assess driver frontal protection for various vehicle generations. Loading of the rigid barrier in NCAP tests was used to evaluate front end stiffness changes over the years.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.