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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.

Lateral distance from the center of a driver's seating position to the gas and brake pedals is one of the main design factors that relates to the ease of stepping on the pedals from one and the other. It is important to keep a certain distance between the pedals to prevent erroneous operations or to reduce the driver's anxiety. In this paper, we explain that the distance between the pedals is affected by the driver's seating height. In other words, if the driver sits lower, the accuracy of stepping on the pedals from the gas pedal to the brake pedal will increase compared to the higher seating position. In addition, we found out that providing auxiliary parts for the leg support enhances the accuracy of the pedal operations.

We have proposed a human mimetic machine, using electronics control technology, which is quasi-human like an android equipped with volition as a new technical concept for man-machine systems. This machine determines the driver's physiological and psychological conditions and, in response, controls surrounding stimuli, such as sounds and vibrations. In this way, the relationship between the driver and the vehicle is made more human like. To determine the significance of the human mimetic machine, we have been developing a biofeedback vigilance control system, which maintains an optimum vigilance level during driving by controlling stimuli. In the first stage of the study, we have developed an accurate method to determine vigilance level using multiple regression analysis of electroencephalograms.

An Advanced driving simulator has been developed at Mazda Yokohama Research Center. The primary use of this simulator is to research future driver-vehicle systems. In an emergency situation, a driver must respond rapidly to perceived motion and visual stimulus to avoid an accident. In such cases, because the time delay associated with the perception of motion cues is shorter than visual and auditory cues, the driver will strongly rely upon perceived motion to control the vehicle. Hence, a driving simulator to be used in the research of driver-vehicle interactions in emergency driving must include a high performance motion system capable of large amplitude lateral motion. The Mazda simulator produces motion cues in four degrees of freedom, provides visual and auditory cues, and generates control feel on the steering wheel. This paper describes the merit of the large amplitude motion system and the features of this newly developed driving simulator.

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.

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 thermal fluid field in a vehicle cabin model is analyzed by the mesh free method as well as mentioned in the Part 1. This paper focuses on the steady state indoor climate in the vehicle cabin including the effect of the buoyancy, the heat generation of the driver and heat conduction through the vehicle body surface under the maximum air-cooling condition soaked in a climate chamber in the summer condition for the demonstration of the mesh free method without not only the deformation of the 3D-CAD model but mesh generation. The solar radiation distribution and heat generation through the exhaust pipe from the engine room are simply included in the analysis. Simulated results are compared with experiments in the conditions of both moving and idling states. As a result, no significant difference in air temperature between simulation and experiments can be obtained in both conditions.

First, this paper describes a measurement of the human back-backrest interface contours and a reduction procedure of the measured contours to reconstruct the statistical back contours of American 50 and 95-percentile male. Second, the paper illustrates the difference of the back contour between the statistical male drivers and SAE 3-D Manikin. Finally, the advantage of using the back contour model in experiment is given. The AM 50 back contour model was used as a loader to obtain the backrest pressure distribution and proved an excellent tool for backrest comfort evaluation.

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.

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.

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.