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Over the past decade there has been significant development in Automated Driving (AD) with continuous evolution towards higher levels of automation. Higher levels of autonomy increase the vehicle Dynamic Driving Task (DDT) responsibility under certain predefined Operational Design Domains (in SAE level 3, 4) to unlimited ODD (in SAE level 5). The AD system should not only be sophisticated enough to be operable at any given condition but also be reliable and safe. Hence, there is a need for Automated Vehicles (AV) to undergo extensive open road testing to traverse a wide variety of roadway features and challenging real-world scenarios. There is a serious need for accurate Ground Truth (GT) to locate the various roadway features which helps in evaluating the perception performance of the AV at any given condition. The results from open road testing provide a feedback loop to achieve a mature AD system.

Due to the variation of compartment design and occupant’s posture in self-driving cars, there is a new and major challenge for occupant protection. In particular, the studies on occupant restraint systems used in the self-driving car have been significantly delayed compared to the development of the autonomous technologies. In this paper, a numerical study was conducted to investigate the effectiveness of three typical restraint systems on the driver protection in three different scenarios.

There has been recent progress over the past 10 years in research comparing 6-year-old thoracic and abdominal response of pediatric volunteers, pediatric post mortem human subjects (PMHS), animal surrogates, and 6-year-old ATDs. Although progress has been made to guide scaling laws of adult to pediatric thorax and abdomen data for use in ATD design and development of finite element models, further effort is needed, particularly with respect to lateral impacts. The objective of the current study was to use the impact response data of age equivalent swine from Yaek et al. (2018) to assess the validity of scaling laws used to develop lateral impact response corridors from adult porcine surrogate equivalents (PSE) to the 3-year-old, 6-year-old, and 10-year-old for the thorax and abdominal body regions.

Evaluations of the dynamic and acoustic responses of panels, partitions, and walls are of concern across many industries, from building home appliances, planning meeting rooms, to designing airplanes and passenger cars. Over the past few decades, search efforts for developing new methodologies and technologies to enable NVH engineers to acquire and correlate dynamically the relationship between input excitations and vibro-acoustic responses of arbitrary-shaped panels has grown exponentially. The application of a particular methodology or technology to the evaluation of a specific structure depends intimately on the goals and objectives of the NVH engineers and industries.

This paper describes an integrated approach to the analysis of brake squeal with newly lattice brake disc design. The procedure adopted to define the lattice properties by considering the periodicity cell of lattice plates, present equations of motion and modes response of a periodic lattice disc in principal coordinates on the rotating disc which excited by distributed axial load. The non-linear contact problem is carried out based on a typical passenger car brake for vanned and lattice brake disc types as it undergoes a partial simulation of the SAE J2521 drag braking noise test. The experimental modal analysis (EMA) with impact hammer test is used to obtain the brake rotor modal properties and validated finite element Free- Free State and stability analysis. The fugitive nature of brake squeal is analyzed through the complex eigenvalue extraction technique to define dynamic instability.

Modularity in product architecture and its significance in product development have become an important product design topics in the last few decades. Several Product Modularity definitions and methodologies were developed by many researchers; however, most of the definitions and concepts have proliferated to the extent that it is difficult to apply one universal definition for modular product architecture and in product development. Automotive seat modular strategy and key factors for consideration towards modular seat design and assemblies are the main focus of this work. The primary objectives are focused on the most “natural segmentation” of the seat elements (i.e., cushions, backs, trims, plastics, head restraints, etc.) to enable the greatest ease of final assembly and greatest flexibility for scalable feature offerings around common assembly “hard-points.”

Thermal comfort in automotive seating has been studied and discussed for a long time. The available research, because it is focused on the components, has not produced a model that provides insight into the human-seat system interaction. This work, which represents the beginning of an extensive research program, aims to establish the foundation for such a model. This paper will discuss the key physiological, psychological, and biomechanical factors related to perceptions of thermal comfort in automotive seats. The methodology to establish perceived thermal comfort requirements will also be presented and discussed.

Contemporary manufacturing systems are still evolving. The system elements, layouts, and integration methods are changing continuously, and ‘collaborative robots’ (CoBots) are now being considered as practical industrial solutions. CoBots, unlike traditional CoBots, are safe and flexible enough to work with humans. Although CoBots have the potential to become standard in production systems, there is no strong foundation for systems design and development. The focus of this research is to provide a foundation and four tier framework to facilitate the design, development and integration of CoBots. The framework consists of the system level, work-cell level, machine level, and worker level. Sixty-five percent of traditional robots are installed in the automobile industry and it takes 200 hours to program (and reprogram) them.

The Dimensional Model of Driver Demand is extended to include Auditory-Vocal (i.e., pure “voice” tasks), and Mixed-Mode tasks (i.e., a combination of Auditory-Vocal mode with visual-only, or with Visual-Manual modes). The extended model was validated with data from 24 participants using the 2014 Toyota Corolla infotainment system in a video-based surrogate driving venue. Twenty-two driver performance metrics were collected, including total eyes-off-road time (TEORT), mean single glance duration (MSGD), and proportion of long single glances (LGP). Other key metrics included response time (RT) and miss rate to a Tactile Detection Response Task (TDRT). The 22 metrics were simplified using Principal Component Analysis to two dimensions. The major dimension, explaining 60% of total variance, we interpret as the attentional effects of cognitive demand. The minor dimension, explaining 20% of total variance, we interpret as physical demand.

This study investigated driver glances while engaging in infotainment tasks in a stationary vehicle while surrogate driving: watching a driving video recorded from a driver’s viewpoint and projected on a large screen, performing a lane-tracking task, and performing the Tactile Detection Response Task (TDRT) to measure attentional effects of secondary tasks on event detection and response. Twenty-four participants were seated in a 2014 Toyota Corolla production vehicle with the navigation system option. They performed the lane-tracking task using the vehicle’s steering wheel, fitted with a laser pointer to indicate wheel movement on the driving video. Participants simultaneously performed the TDRT and a variety of infotainment tasks, including Manual and Mixed-Mode versions of Destination Entry and Cancel, Contact Dialing, Radio Tuning, Radio Preset selection, and other Manual tasks. Participants also completed the 0-and 1-Back pure auditory-vocal tasks.

At the collision moment, a driver’s lower extremity will be in different foot position, which leads to the different posture of the lower extremity with various muscle activations. These will affect the driver’s injury during collision, so it is necessary to investigate further. A simulated collision scene was constructed, and 20 participants (10 male and 10 female) were recruited for the test in a driving simulator. The braking posture and muscle activation of eight major muscles of driver’s lower extremity (both legs) were measured. The muscle activations in different postures were then analyzed. At the collision moment, the right leg was possible to be on the brake (male, 40%; female, 45%), in the air (male, 27.5%; female, 37.5%) or even on the accelerator (male, 25%; female, 12.5%). The left leg was on the floor all along.

Many metrics have been used in an attempt to predict the effects of secondary tasks on driving behavior. Such metrics often give rise to seemingly paradoxical results, with one metric suggesting increased demand and another metric suggesting decreased demand for the same task. For example, for some tasks, drivers maintain their lane well yet detect events relatively poorly. For other tasks, drivers maintain their lane relatively poorly yet detect events relatively well. These seeming paradoxes are not time-accuracy trade-offs or experimental artifacts, because for other tasks, drivers do both well. The paradoxes are resolved if driver demand is modeled in two orthogonal dimensions rather than a single “driver workload” dimension. Principal components analysis (PCA) was applied to the published data from four simulator, track, and open road studies of visual-manual secondary task effects on driving.

In this age of the Internet of Things, people expect in-vehicle interfaces to work just like a smartphone. Our understanding of the reality of in-vehicle interfaces is quite contrary to that. We review the fundamental principles and metrics for automotive visual-manual driver distraction guidelines. We note the rise in portable device usage in vehicles, and debunk the myth of increased crash risk when conversing on a wireless device. We advocate that portable electronic device makers such as Apple and Google should adopt driver distraction guidelines for application developers (whether for tethered or untethered device use in the vehicle). We present two design implications relevant to safe driving. First, the Rule of Platform Appropriateness: design with basic principles of ergonomics, and with driver's limited visual, manual and cognitive capacity, in mind. Second, the Rule of Simplicity: thoughtful reduction in the complexity of in-vehicle interfaces.

Low back pain has a higher prevalence among drivers who have long term history of vehicle operations. Vehicle vibration has been considered to contribute to the onset of low back pain. However, the fundamental mechanism that relates vibration to low back pain is still not clear. Little is known about the relationship between vibration exposure, the biomechanical response, and the physiological responses of the seated human. The aim of this study was to determine the vibration frequency that causes the increase of muscle activity that can lead to muscle fatigue and low back pain. This study investigated the effects of various vibration frequencies on the lumbar and thoracic paraspinal muscle responses among 11 seated volunteers exposed to sinusoidal whole body vibration varying from 4Hz to 30Hz at 0.4 g of acceleration. The accelerations of the seat and the pelvis were recorded during various frequency of vibrations. Muscle activity was measured using electromyography (EMG).

As advanced electronic technology continues to be integrated into in-vehicle and portable devices, it is important to understand how drivers handle multitasking in order to maintain safe driving while reducing driver distraction. NHTSA has made driver distraction mitigation a major initiative. Currently, several types of Detection Response Tasks (DRTs) for assessing selective attention by detecting and responding to visual or tactile events while driving have been under development by an ISO WG8 DRT group. Among these DRTs, the tactile version (TDRT) is considered as a sensitive surrogate measure for driver attention without visual-manual interference in driving, according to the ISO DRT Draft Standard. In our previous study of cognitive demand, our results showed that the TDRT is the only surrogate DRT task with an acute sensitivity to a cognitive demand increase in an auditory-vocal task (i.e., n-Back verbal working memory task).

The development of a collision severity model can serve as an important tool in understanding the requirements for devising countermeasures to improve occupant safety and traffic safety. Collision type, weather conditions, and driver intoxication are some of the factors that may influence motor vehicle collisions. The objective of this study is to use artificial neural networks (ANNs) to identify the major determinants or contributors to fatal collisions based on various driver, vehicle, and environment characteristics obtained from collision data from Transport Canada. The developed model will have the capability to predict similar collision outcomes based on the variables analyzed in this study. A multilayer perceptron (MLP) neural network model with feed-forward back-propagation architecture is used to develop a generalized model for predicting collision severity. The model output, collision severity, is divided into three categories - fatal, injury, and property damage only.

This study provides an overview of a novel method for evaluating in-vehicle speech intelligibility using the Speech Intelligibility Index (SII). The approach presented is based on a measured speech signal evaluated at the sentence Speech Reception Threshold (sSRT) in a simulated driving environment. In this context, the impact of different band importance functions in the evaluation of the SII using the Hearing in Noise Test (HINT) in a driving simulator is investigated.

The Wayne State University (WSU) EcoCAR2 student team is participating in a design competition for the conversion of a 2013 Chevrolet Malibu into a plug-in hybrid. The team created a repeatable on-road test drive route using local public roads near the university that would be of similar velocity ranges contained in the EcoCAR2 4-Cycle Drive Schedule - a weighted combination of four different EPA-based drive cycles (US06 split into city and highway portions, all of the HWFET, first 505 seconds portion of UDDS). The primary purpose of the team's local on-road route was to be suitable for testing the team's added hybrid components and control strategy for minimizing petroleum consumption and tail pipe emissions. Comparison analysis of velocities was performed between seven local routes and the EcoCAR2 4-Cycle Drive Schedule. Three of the seven local routes had acceptable equivalence for velocity (R₂ ≻ 0.80) and the team selected one of them to be the on-road test drive route.

Wayne State University EcoCAR2 team is designing and modifying a GM-donated Chevrolet Malibu 2013 to a Parallel-Through-The-Road (PTTR) plug-in hybrid. A Freescale-donated Center Stack Unit (CSU) touchscreen display is used for Human Machine Interface (HMI). Surveys were conducted to better understand CSU functionality expectations. One required function was increasing driving efficiency. Other hybrid and electric vehicles HMI systems present driving and environmental settings efficiencies such as average fuel economy, lifetime fuel economy, electric charge used, fuel used, distances driven on each power source, instantaneous power gauge and instantaneous driver efficiency gauge. These offer drivers a large sum of information but with no provision to analyze and improve one's driving habits unless one has the required knowledge to understand the causes behind the values presented.

Up to now, there is no standard methodology that addresses how driver distraction is affected by perceptual demand and working memory demand - aside from visual allocation. In 2009, the Peripheral Detection Task (PDT) became a NHTSA recommended measure for driver distraction [1]. Then the PDT task was renamed as the Detection Response Task (DRT) because the International Standards Organization (ISO) has identified this task as a potential method for assessing selective attention in detection of visual, auditory, tactile and haptic events while driving. The DRT is also under consideration for adoption as an ISO standard surrogate test for driver performance for new telematics designs. The Wayne State University (WSU) driver imaging group [2, 3] improved the PDT and created the Enhanced Peripheral Detection Task I (EPDT-I) [4]. The EPDT-I is composed of a simple visual event detection task and a video of a real-world driving scene.