The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Reza Langari - One of the best experts on this subject based on the ideXlab platform.
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intelligent energy management agent for a parallel hybrid vehicle part i system architecture and design of the Driving Situation identification process
IEEE Transactions on Vehicular Technology, 2005Co-Authors: Reza Langari, Jong Seob WonAbstract:This two part paper proposes an intelligent energy management agent (IEMA) for parallel hybrid vehicles. IEMA incorporates a Driving Situation identification component whose role is to assess the Driving environment, the Driving style of the driver and the operating mode of the vehicle using long and short term statistical features of the drive cycle. This information is subsequently used by the torque distribution and charge sustenance components of IEMA to determine the power split strategy, which is shown to lead to enhanced fuel economy and reduced emissions. In Part I, the overall architecture of IEMA is presented and the Driving Situation identification process is described. It is specifically shown that a learning vector quantization (LVQ) network can effectively determine the Driving condition using a limited duration of Driving data. The overall performance of the system under a range of drive cycles is discussed in the second part of this paper.
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Intelligent energy management agent for a parallel hybrid vehicle - Part II: Torque distribution, charge sustenance strategies, and performance results
IEEE Transactions on Vehicular Technology, 2005Co-Authors: Jong Seob Won, Reza LangariAbstract:This paper represents the second part of a two-part paper on development of an intelligent energy management agent (IEMA) for parallel hybrid vehicles. In this part, energy management strategies for the torque distribution and charge sustenance tasks are established and implemented. Driving Situation awareness-based fuzzy rule bases are developed to make intelligent decisions on the power split function. A charge sustenance strategy is developed in parallel to maintain adequate reserves of energy in the storage device for supporting an extended range of Driving. Simulation study is conducted for the proposed IEMA and performance results are analyzed to evaluate its viability as a possible solution to and an extendable framework for energy management for parallel hybrid electric vehicles.
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a Driving Situation awareness based energy management strategy for parallel hybrid vehicles
SAE transactions, 2003Co-Authors: Reza Langari, Jong Seob WonAbstract:A concept of "Driving Situation awareness"-driven energy management system for parallel hybrid electric vehicles (HEVs) is introduced. The essential feature of the proposed energy management system is to assess the Driving environment (in terms of facility type combined with traffic congestion level) using long and short term statistical features of the drive cycle. Subsequently, this knowledge is provided to a system that makes intelligent decisions with respect to the torque distribution and charge sustenance tasks. Simulation work was carried out for the validation of proposed system, and the results reveal its viability for energy management of parallel hybrid vehicles.
Jong Seob Won - One of the best experts on this subject based on the ideXlab platform.
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intelligent energy management agent for a parallel hybrid vehicle part i system architecture and design of the Driving Situation identification process
IEEE Transactions on Vehicular Technology, 2005Co-Authors: Reza Langari, Jong Seob WonAbstract:This two part paper proposes an intelligent energy management agent (IEMA) for parallel hybrid vehicles. IEMA incorporates a Driving Situation identification component whose role is to assess the Driving environment, the Driving style of the driver and the operating mode of the vehicle using long and short term statistical features of the drive cycle. This information is subsequently used by the torque distribution and charge sustenance components of IEMA to determine the power split strategy, which is shown to lead to enhanced fuel economy and reduced emissions. In Part I, the overall architecture of IEMA is presented and the Driving Situation identification process is described. It is specifically shown that a learning vector quantization (LVQ) network can effectively determine the Driving condition using a limited duration of Driving data. The overall performance of the system under a range of drive cycles is discussed in the second part of this paper.
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Intelligent energy management agent for a parallel hybrid vehicle - Part II: Torque distribution, charge sustenance strategies, and performance results
IEEE Transactions on Vehicular Technology, 2005Co-Authors: Jong Seob Won, Reza LangariAbstract:This paper represents the second part of a two-part paper on development of an intelligent energy management agent (IEMA) for parallel hybrid vehicles. In this part, energy management strategies for the torque distribution and charge sustenance tasks are established and implemented. Driving Situation awareness-based fuzzy rule bases are developed to make intelligent decisions on the power split function. A charge sustenance strategy is developed in parallel to maintain adequate reserves of energy in the storage device for supporting an extended range of Driving. Simulation study is conducted for the proposed IEMA and performance results are analyzed to evaluate its viability as a possible solution to and an extendable framework for energy management for parallel hybrid electric vehicles.
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a Driving Situation awareness based energy management strategy for parallel hybrid vehicles
SAE transactions, 2003Co-Authors: Reza Langari, Jong Seob WonAbstract:A concept of "Driving Situation awareness"-driven energy management system for parallel hybrid electric vehicles (HEVs) is introduced. The essential feature of the proposed energy management system is to assess the Driving environment (in terms of facility type combined with traffic congestion level) using long and short term statistical features of the drive cycle. Subsequently, this knowledge is provided to a system that makes intelligent decisions with respect to the torque distribution and charge sustenance tasks. Simulation work was carried out for the validation of proposed system, and the results reveal its viability for energy management of parallel hybrid vehicles.
Inki Kim - One of the best experts on this subject based on the ideXlab platform.
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the effect of whole body haptic feedback on driver s perception in negotiating a curve
arXiv: Human-Computer Interaction, 2018Co-Authors: Erfan Pakdamanian, Lu Feng, Inki KimAbstract:It remains uncertain regarding the safety of Driving in autonomous vehicles that, after a long, passive control and inattention to the Driving Situation, how the drivers will be effectively informed to take-over the control in emergency. In particular, the active role of vehicle force feedback on the driver's risk perception on curves has not been fully explored. To investigate it, the current paper examined the driver's cognitive and visual responses to the whole-body haptic feedback during curve negotiations. The effects of force feedback on drivers' responses on curves were investigated in a high-fidelity Driving simulator while measuring EEG and visual gaze over ten participants. The preliminary analyses of the first two participants revealed that pupil diameter and fixation time on the curves were significantly longer when the driver received whole-body feedback, compared to none. The findings suggest that whole-body feedback can be used as an effective "advance notification" of hazards.
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the effect of whole body haptic feedback on driver s perception in negotiating a curve
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2018Co-Authors: Erfan Pakdamanian, Lu Feng, Inki KimAbstract:It remains uncertain regarding the safety of Driving in autonomous vehicles that, after a long, passive control and inattention to the Driving Situation, how the drivers will be effectively informe...
Erfan Pakdamanian - One of the best experts on this subject based on the ideXlab platform.
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the effect of whole body haptic feedback on driver s perception in negotiating a curve
arXiv: Human-Computer Interaction, 2018Co-Authors: Erfan Pakdamanian, Lu Feng, Inki KimAbstract:It remains uncertain regarding the safety of Driving in autonomous vehicles that, after a long, passive control and inattention to the Driving Situation, how the drivers will be effectively informed to take-over the control in emergency. In particular, the active role of vehicle force feedback on the driver's risk perception on curves has not been fully explored. To investigate it, the current paper examined the driver's cognitive and visual responses to the whole-body haptic feedback during curve negotiations. The effects of force feedback on drivers' responses on curves were investigated in a high-fidelity Driving simulator while measuring EEG and visual gaze over ten participants. The preliminary analyses of the first two participants revealed that pupil diameter and fixation time on the curves were significantly longer when the driver received whole-body feedback, compared to none. The findings suggest that whole-body feedback can be used as an effective "advance notification" of hazards.
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the effect of whole body haptic feedback on driver s perception in negotiating a curve
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2018Co-Authors: Erfan Pakdamanian, Lu Feng, Inki KimAbstract:It remains uncertain regarding the safety of Driving in autonomous vehicles that, after a long, passive control and inattention to the Driving Situation, how the drivers will be effectively informe...
Torbjorn Akerstedt - One of the best experts on this subject based on the ideXlab platform.
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the effects of Driving Situation on sleepiness indicators after sleep loss a Driving simulator study
Industrial Health, 2009Co-Authors: Anna Anund, Goran Kecklund, Albert Kircher, Andreas Tapani, Torbjorn AkerstedtAbstract:Almost all studies of sleepy Driving are carried out in Driving simulators and with monotonous road conditions (no interaction with other cars). The present study investigated indicators of sleepy Driving in a more challenging scenario after a night awake. 17 participants drove a high fidelity moving base Driving simulator experiment while sleepiness was monitored physiologically and behaviourally. Short periods of Situations of free Driving (no other vehicles) alternated with short periods of following another vehicle (car following) with and without the possibility to overtake. The result showed that a night of prior sleep loss increased sleepiness levels at the wheel (eye closure duration and lateral variability) compared to after a night of normal sleep. Blink duration while overtaking was significantly lower compared to the other Situations, it was at the same level as after night sleep. Speed when passing a stopped school bus was not significantly affected by sleepiness. However the warning caused a more rapid reduction of speed. In conclusion, a moderately challenging Driving contest did not affect sleepiness indicators, but a very challenging one did so (overtaking). This suggests that it is important to monitor the Driving Situation in field operational tests of sleepy Driving.
