Current Issue : April - June Volume : 2014 Issue Number : 2 Articles : 5 Articles
This paper reviews the history of automotive technology development and human factors research, largely by decade, since the\r\ninception of the automobile. The human factors aspects were classified into primary driving task aspects (controls, displays, and\r\nvisibility), driver workspace (seating and packaging, vibration, comfort, and climate), driver�s condition (fatigue and impairment),\r\ncrash injury, advanced driver-assistance systems, external communication access, and driving behavior. For each era, the paper\r\ndescribes the SAE and ISO standards developed, the major organizations and conferences established, the major news stories\r\naffecting vehicle safety, and the general social context. The paper ends with a discussion of what can be learned from this historical\r\nreview and the major issues to be addressed. A major contribution of this paper is more than 180 references that represent the\r\nfoundation of automotive human factors, which should be considered core knowledge and should be familiar to those in the\r\nprofession....
In urban scenarios the mainmechanism to control vehicular flow are traffic signal controls at the junctions. The traditional system however failsto adjust the timing pattern in accordance to the time variability. Dynamic systems are an alternative which will alter the timing patterns according to the traffic demand. An adaptive traffic signal control system isdesigned and developed in this paper. The system we use here reduces the waiting time of the vehicles at the intersection and regulates the road traffic congestion by reducing the queue length. The simulation here showsthe data convergence time and the communication delay between vehicle and traffic signal which do not compromise the efficiency of the system...
This paper gives a theoretical framework to describe, analyze, and evaluate the driverââ?¬â?¢s overtrust in and overreliance on ADAS.\r\nAlthough ââ?¬Å?overtrustââ?¬Â and ââ?¬Å?overrelianceââ?¬Â are often used as if they are synonyms, this paper differentiates the two notions rigorously.\r\nTo this end, two aspects, (1) situation diagnostic aspect and (2) action selection aspect, are introduced.The first aspect is to describe\r\novertrust, and it has three axes: (1-1) dimension of trust, (1-2) target object, and (1-3) chances of observation.The second aspect, (2),\r\nis to describe overreliance on the ADAS, and it has other three axes: (2-1) type of action selected, (2-2) benefits expected, and (2-3)\r\ntime allowance for human intervention....
Communication between vehicles has recently been a popular research topic. Generally, the Vehicle-to-Vehicle (V2V), Vehicle-to-\r\nInfrastructure (V2I), and Infrastructure-to-Infrastructure (I2I) communications applications can be divided into two sections: (i)\r\nsafety applications and (ii) nonsafety applications. In this study, we have investigated the performance of IEEE 802.11p and IEEE\r\n802.11b based on real-world measurements and radio propagation models of V2V networks in different environments, including\r\nhighway, rural, and urban areas. Furthermore, we have investigated the most used V2V mobility models and simulation tools.\r\nComparative performance evaluations show that the IEEE 802.11p achieves higher network throughput, low end-to-end delay, and\r\nhigher delivery ratio compared to IEEE 802.11b.Overall, our main objective is to describe potential advantages, research challenges,\r\nand applications of V2V networks and show how IEEE 802.11p and IEEE 802.11b will perform under different radio propagation\r\nenvironments....
Road vehicle yaw stability control systems like electronic stability program (ESP) are important active safety systems used for\r\nmaintaining lateral stability of the vehicle. Vehicle yaw rate is the key parameter that needs to be known by a yaw stability control\r\nsystem. In this paper, yaw rate is estimated using a virtual sensor which contains kinematic relations and a velocity-scheduled\r\nKalman filter. Kinematic estimation is carried out using wheel speeds, dynamic tire radius, and front wheel steering angle. In\r\naddition, a velocity-scheduled Kalman filter utilizing the linearized single-track model of the road vehicle is used in the dynamic\r\nestimation part of the virtual sensor. The designed virtual sensor is successfully tested offline using a validated, high degrees of\r\nfreedom, and high fidelity vehicle model and using hardware-in-the-loop simulations. Moreover, actual road testing is carried out\r\nand the estimated yaw rate from the virtual sensor is compared with the actual yaw rate obtained from the commercial yaw rate\r\nsensor to demonstrate the effectiveness of the virtual yaw rate sensor in practical use....
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