Analytic solution for the lightning current induced mutually coupled resistive filament wire model

When a lightning current flows between the lightning entry and exit points of a structure, the lightning current density varies in different parts of the structure depending on the shape of the structure and material variance. The structure can be discretized into parallel wires, called filament wires, running parallel to the current direction. Furthermore, using the filament wire method, we can calculate the current distribution among the wires. For a structure that has a low resistance material such as aluminum, current distribution can be calculated by considering self-inductance of the wire and mutual-inductance between wires but resistance is not considered. However, in modern aircraft, composite materials are used for parts of the structure because of their strength and weight. These composite materials have high resistance compared to metal, and resistance cannot be ignored. Thus, to solve a system of ordinary differential equations for a filament model, inhomogeneous structure, aperture, and resistance of each wire must be considered to obtain the correct current distribution of each part of the structure. However, the numerical solution of the filament wire model does not reveal the region of convergence and the accuracy of the given mathematical model. It also has high time complexity. This paper presents the analytic solution and stability condition for the mutually coupled resistive filament wire model using eigenvalues of given filament wire matrix model. The stability condition is rigorously calculated and the solution is also consistent with the numerical model

Impact of Cognitive Workload on Physiological Arousal and Performance in Younger and Older Drivers

Two groups, aged 25-35 and 60-69, engaged in 3 levels of a delayed auditory recall task while driving a simulated highway. Heart rate and skin conductance increased with each level of demand, demonstrating that these indices can correctly rank order cognitive workload. Effects were also observed on speed and SD of lane position, but they were subtle, nonlinear, and did not effectively differentiate. Patterns were quite consistent across age groups. These findings on the sensitivity of physiological measures replicate those from an onroad study using a similar protocol. Together, the results support the validity of using these physiological measures of workload in a simulated environment to model differences likely to be present under actual driving conditions

Safety Implication of Auditory and Visual In-Vehicle Interfaces for Older Drivers: The effect of surrogate in-vehicle information systems

This paper presents the findings of a simulator study that explain potential risks induced by visual and auditory interaction while driving. Differences in subjective distraction and driving performance of younger and older drivers were compared while interacting with two different types of surrogate user interface in a driving simulator. To assess the differences, 30 drivers, divided into younger (25–35) and older (60–69) age groups were participated. Each driving takes about 20 minutes, and participants perform a secondary task, i.e. n-back task or arrow task at a specified segment. Comparisons of younger and older drivers’ subjective ratings of difficulty and perceived distraction and driving performance were conducted. As a result, it was found that the effect of interaction types, i.e. visual and auditory, on younger and older drivers’ performance was significantly different.1

Implementation of a driver aware vehicle using multimodal information

There has been recent interest in an integrated approach to driver safety which focuses on the overlapping and interacting area of the role of driver, vehicle and road environment in driving safety. Many active safety systems such as adaptive cruise control, parking assistance and lane keeping system have been developed to target these intersecting regions. However, dynamic driver state was not properly taken into account in the safety systems because the selection of dominant attributes and modeling architectures for state detection are not fully established yet. In order to categorize driver state in terms of driver wellness, researchers in MIT suggested a modified inverted-U shaped curve which depicts the relationship between arousal level and driving performance. This paper demonstrates an implementation of a driver aware vehicle platform to detect driver state based on the MIT wellness concept. In order to detect driver state, various overt and covert measures such as driving performance, visual attention, physiological arousal and traffic situation should be collected and interpreted. The main focus of this paper is to provide implementation techniques for the synchronized data collection and integration of inputs from multiple domains

Detection of Cognitive and Visual Distraction Using Radial Basis Probabilistic Neural Networks

This paper suggests a real-time method for detecting a driver’s cognitive and visual distraction using lateral driving performance measures. The algorithm adopts radial basis probabilistic neural networks (RBPNNs) to construct classification models. In this study, combinations of two driving performance data measures, including the standard deviation of lane position (SDLP) and steering wheel reversal rate (SRR), were considered as measures of distraction. Data for training and testing the RBPNN models were collected under simulated conditions in which fifteen participants drove on a highway. While driving, they were asked to complete auditory recall tasks or arrow search tasks to create cognitively or visually distracted driving periods. As a result, the best performing model could detect distraction with an average accuracy of 78.0 %, which is a relatively high accuracy in the human factors domain. The results demonstrated that the RBPNN model using SDLP and SRR could be an effective distraction detector with easy-to-obtain and inexpensive inputs. © 2018, The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.1

The Effects of Distraction Type And Difficulty On Older Drivers' Performance And Behaviour : Visual Vs. Cognitive

Driver distraction is an important contributing factor to increase crash risks. The effect of secondary task engagement may vary by interaction types and driver age. Thus, it is essential to understand the impacts of distraction type and age on driving performance for improving the safety of in-vehicle secondary task design. This paper aims to assess potential risks induced by visual and auditory secondary tasks while driving. Thirty drivers, consisted of fifteen younger drivers aged 25 ∼ 35 and fifteen older aged 60 ∼ 69, were recruited and asked to drive in a simulator. They conducted two driving sessions, one for visually distracted driving and the other for cognitive distraction. The order in which secondary tasks were presented was counter-balanced. Driving performance and behaviour data were collected continuously using multiple measurement devices for vehicle speed, lane position, electrocardiogram, and gaze pattern. Differences in younger and older drivers’ performance while conducting the secondary tasks were compared. The result indicated that the effect of interaction types, i.e., visual and auditory, on older drivers’ performance was significant. More difficult secondary task creates greater age difference in driving performance. However, eye movement and physiological response were not significantly different between younger and older drivers. This result could suggest older drivers’ lower risk awareness of cognitive distraction. © 2021, KSAE.1

Comparative evaluation of fuel consumption estimation models

Increased fossil fuel consumptions present a huge environmental challenge to the world. In order to meet the consumers' demands for transportation and at the same time to provide more fuel efficient vehicles, scientists are constantly searching for effective emissions and fuel consumption estimation models to protect the environment, especially to design more efficient control algorithms at traffic signalized intersections (e.g., eco-adaptive control) and promote environmentally friendly driving behaviors (e.g., eco-driving). The purpose of this research was to assess three existing fuel consumption estimation models using actual fuel consumption rates based on field measurements. The three models are the Virginia Tech Microscopic Energy and Emissions Model (VT-Micro), the Comprehensive Modal Emission Model (CMEM) and the Motor Vehicle Emission Simulator (MOVES) Model, and the field measured fuel consumptions are from instantaneous light duty vehicle (LDV) fuel consumption (FC) rate data collected by the Daegu Gyeongbuk Institute of Science and Technology of Korea (DGIST). Both the VT-Micro and the CMEM explained DGIST data reasonably well. All three models adequately tracked DGIST total fuel consumption over a fixed time interval

Comparative evaluation of fuel consumption estimation models

Increased fossil fuel consumptions present a huge environmental challenge to the world. In order to meet the consumers' demands for transportation and at the same time to provide more fuel efficient vehicles, scientists are constantly searching for effective emissions and fuel consumption estimation models to protect the environment, especially to design more efficient control algorithms at traffic signalized intersections (e.g., eco-adaptive control) and promote environmentally friendly driving behaviors (e.g., eco-driving). The purpose of this research was to assess three existing fuel consumption estimation models using actual fuel consumption rates based on field measurements. The three models are the Virginia Tech Microscopic Energy and Emissions Model (VT-Micro), the Comprehensive Modal Emission Model (CMEM) and the Motor Vehicle Emission Simulator (MOVES) Model, and the field measured fuel consumptions are from instantaneous light duty vehicle (LDV) fuel consumption (FC) rate data collected by the Daegu Gyeongbuk Institute of Science and Technology of Korea (DGIST). Both the VT-Micro and the CMEM explained DGIST data reasonably well. All three models adequately tracked DGIST total fuel consumption over a fixed time interval