30,022 research outputs found

    The DRIVE-SAFE project: signal processing and advanced information technologies for improving driving prudence and accidents

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    In this paper, we will talk about the Drivesafe project whose aim is creating conditions for prudent driving on highways and roadways with the purposes of reducing accidents caused by driver behavior. To achieve these primary goals, critical data is being collected from multimodal sensors (such as cameras, microphones, and other sensors) to build a unique databank on driver behavior. We are developing system and technologies for analyzing the data and automatically determining potentially dangerous situations (such as driver fatigue, distraction, etc.). Based on the findings from these studies, we will propose systems for warning the drivers and taking other precautionary measures to avoid accidents once a dangerous situation is detected. In order to address these issues a national consortium has been formed including Automotive Research Center (OTAM), Koç University, Istanbul Technical University, Sabancı University, Ford A.S., Renault A.S., and Fiat A. Ş

    Driving automation: Learning from aviation about design philosophies

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    Full vehicle automation is predicted to be on British roads by 2030 (Walker et al., 2001). However, experience in aviation gives us some cause for concern for the 'drive-by-wire' car (Stanton and Marsden, 1996). Two different philosophies have emerged in aviation for dealing with the human factor: hard vs. soft automation, depending on whether the computer or the pilot has ultimate authority (Hughes and Dornheim, 1995). This paper speculates whether hard or soft automation provides the best solution for road vehicles, and considers an alternative design philosophy in vehicles of the future based on coordination and cooperation

    Design of Fault-Tolerant Control for Trajectory Tracking

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    International audienceThe paper proposes a fault-tolerant integrated control system with the brake and the steering for developing a driver assistance system. The purpose is to design a fault-tolerant control which is able to guarantee the trajectory tracking and lateral stability of the vehicle against actuator fault scenarios. Since both actuators affect the lateral dynamics of the vehicle, in the control design a balance and priority between them must be achieved. The method is extended with a fault-tolerant feature based on a robust LPV method, into which the detected fault information are incorporated. The control design is performed by using the Matlab/Simulink software and the verification of the designed controller is performed by using the CarSim software

    A preliminary safety evaluation of route guidance comparing different MMI concepts

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    In-vehicle technology for self-driving cars: Advantages and challenges for aging drivers

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    The development of self-driving cars or autonomous vehicles has progressed at an unanticipated pace. Ironically, the driver or the driver-vehicle interaction is a largely neglected factor in the development of enabling technologies for autonomous vehicles. Therefore, this paper discusses the advantages and challenges faced by aging drivers with reference to in-vehicle technology for self-driving cars, on the basis of findings of recent studies. We summarize age-related characteristics of sensory, motor, and cognitive functions on the basis of extensive age-related research, which can provide a familiar to better aging drivers. Furthermore, we discuss some key aspects that need to be considered, such as familar to learnability, acceptance, and net effectiveness of new in-vehicle technology, as addressed in relevant studies. In addition, we present research-based examples on aging drivers and advanced technology, including a holistic approach that is being developed by MIT AgeLab, advanced navigation systems, and health monitoring systems. This paper anticipates many questions that may arise owing to the interaction of autonomous technologies with an older driver population. We expect the results of our study to be a foundation for further developments toward the consideration of needs of aging drivers while designing self-driving vehicles.Korea (South). Ministry of Trade, Industry and Energy (Technology Innovation Program 1004761)Kookmin University. Faculty Research ProgramNew England University Transportation CenterSantos Family Foundatio

    In loco intellegentia: Human factors for the future European train driver

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    The European Rail Traffic Management System (ERTMS) represents a step change in technology for rail operations in Europe. It comprises track-to-train communications and intelligent on-board systems providing an unprecedented degree of support to the train driver. ERTMS is designed to improve safety, capacity and performance, as well as facilitating interoperability across the European rail network. In many ways, particularly from the human factors perspective, ERTMS has parallels with automation concepts in the aviation and automotive industries. Lessons learned from both these industries are that such a technology raises a number of human factors issues associated with train driving and operations. The interaction amongst intelligent agents throughout the system must be effectively coordinated to ensure that the strategic benefits of ERTMS are realised. This paper discusses the psychology behind some of these key issues, such as Mental Workload (MWL), interface design, user information requirements, transitions and migration and communications. Relevant experience in aviation and vehicle automation is drawn upon to give an overview of the human factors challenges facing the UK rail industry in implementing ERTMS technology. By anticipating and defining these challenges before the technology is implemented, it is hoped that a proactive and structured programme of research can be planned to meet them

    Front-End electronics configuration system for CMS

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    The four LHC experiments at CERN have decided to use a commercial SCADA (Supervisory Control And Data Acquisition) product for the supervision of their DCS (Detector Control System). The selected SCADA, which is therefore used for the CMS DCS, is PVSS II from the company ETM. This SCADA has its own database, which is suitable for storing conventional controls data such as voltages, temperatures and pressures. In addition, calibration data and FE (Front-End) electronics configuration need to be stored. The amount of these data is too large to be stored in the SCADA database [1]. Therefore an external database will be used for managing such data. However, this database should be completely integrated into the SCADA framework, it should be accessible from the SCADA and the SCADA features, e.g. alarming, logging should be benefited from. For prototyping, Oracle 8i was selected as the external database manager. The development of the control system for calibration constants and FE electronics configuration has been done in close collaboration with the CMS tracker group and JCOP (Joint COntrols Project)(1). (1)The four LHC experiments and the CERN IT/CO group has merged their efforts to build the experiments controls systems and set up the JCOP at the end of December, 1997 for this purpose.Comment: 3 pages, 4 figures, Icaleps'01 conference PSN WEDT00

    The Adaptive Automation Design

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