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Sökning: WFRF:(Wahde Mattias)

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1.
  • Akerstedt, T, et al. (författare)
  • Reaction of sleepiness indicators to partial sleep deprivation, time of day and time on task in a driving simulator - the DROWSI project
  • 2010
  • Ingår i: Journal of Sleep Research. - 1365-2869 .- 0962-1105. ; 19:2, s. 298-309
  • Tidskriftsartikel (refereegranskat)abstract
    • Studies of driving and sleepiness indicators have mainly focused on prior sleep reduction. The present study sought to identify sleepiness indicators responsive to several potential regulators of sleepiness: sleep loss, time of day (TOD) and time on task (TOT) during simulator driving. Thirteen subjects drove a high-fidelity moving base simulator in six 1-h sessions across a 24-h period, after normal sleep duration (8 h) and after partial sleep deprivation (PSD; 4 h). The results showed clear main effects of TOD (night) and TOT but not for PSD, although the latter strongly interacted with TOD. The most sensitive variable was subjective sleepiness, the standard deviation of lateral position (SDLAT) and measures of eye closure [duration, speed (slow), amplitude (low)]. Measures of electroencephalography and line crossings (LCs) showed only modest responses. For most variables individual differences vastly exceeded those of the fixed effects, except for subjective sleepiness and SDLAT. In a multiple regression analysis, SDLAT, amplitude/peak eye-lid closing velocity and blink duration predicted subjective sleepiness bouts with a sensitivity and specificity of about 70%, but were mutually redundant. The prediction of LCs gave considerably weaker, but similar results. In summary, SDLAT and eye closure variables could be candidates for use in sleepiness-monitoring devices. However, individual differences are considerable and there is need for research on how to identify and predict individual differences in susceptibility to sleepiness.
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2.
  • Sandberg, D, et al. (författare)
  • Detecting driver sleeepiness using optimized non-linear combinations of sleepiness indicators
  • 2011
  • Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050. ; 12:1, s. 97-108
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper addresses the problem of detecting sleepiness in car drivers. First, a variety of sleepiness indicators (based on driving behavior) proposed in the literature were evaluated. These indicators were then subjected to parametric optimization using stochastic optimization methods. To improve performance, the functional form of some of the indicators was generalized before optimization. Next, using a neural network, the best performing sleepiness indicators were combined with a mathematical model of sleepiness, i.e., the sleep/wake predictor (SWP). The analyses were based on data obtained from a study that involved 12 test subjects at the moving-base driving simulator at the Swedish National Road and Transportation Research Institute (VTI), Linkping, Sweden. The data were derived from 12 1-h driving sessions for each test subject, with varying degrees of sleepiness. The performance measure (range [0,1]) for indicators was taken as the average of sensitivity and specificity. Starting with indicators proposed in the literature, the best such indicator, i.e., the standard deviation of the yaw angle, reached a performance score of 0.72 on previously unseen test data. It was found that indicators based on a given signal gave essentially equal performance after parametric optimization, but in no case was it better than 0.72. The best generalized indicator (the generic variability indicator) obtained a performance score of 0.74. SWP achieved a score of 0.78. However, by nonlinearly combining SWP with the generic variability indicator, a score of 0.83 was obtained. Thus, the results imply that a nonlinear combination of a measure based on driving behavior with a model of sleepiness significantly improves driver sleepiness detection.
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3.
  • Sandberg, D, et al. (författare)
  • The Characteristics of Sleepiness During Real Driving at Night - A Study of Driving Performance, Physiology and Subjective Experience
  • 2011
  • Ingår i: Sleep. - 0161-8105. ; 34:10, s. 1317-1325
  • Tidskriftsartikel (refereegranskat)abstract
    • Study Objectives: Most studies of sleepy driving have been carried out in driving simulators. A few studies of real driving are available, but these have used only a few sleepiness indicators. The purpose of the present study was to characterize sleepiness in several indicators during real driving at night, compared with daytime driving. Design: Participants drove 55 km (at 90km/h) on a 9-m-wide rural highway in southern Sweden. Daytime driving started at 09: 00 or 11: 00 (2 groups) and night driving at 01: 00 or 03: 00 (balanced design). Setting: Instrumented car on a real road in normal traffic. Participants: Eighteen participants drawn from the local driving license register. Interventions: Daytime and nighttime drives. Measurement and Results: The vehicle was an instrumented car with video monitoring of the edge of the road and recording of the lateral position and speed. Electroencephalography and electrooculography were recorded, together with ratings of sleepiness every 5 minutes. Pronounced effects of night driving were seen for subjective sleepiness, electroencephalographic indicators of sleepiness, blink duration, and speed. Also, time on task showed significant effects for subjective sleepiness, blink duration, lane position, and speed. Sleepiness was highest toward the end of the nighttime drive. Night driving caused a leftward shift in lateral position and a reduction of speed. The latter two findings, as well as the overall pattern of sleepiness indicators, provide new insights into the effects of night driving. Conclusion: Night driving is associated with high levels of subjective, electrophysiologic, and behavioral sleepiness.
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4.
  • Bellone, Mauro, 1982-, et al. (författare)
  • Electrification and Automation in Maritime Applications: Employing AI Techniques for Energy Optimization and Efficiency
  • 2019
  • Ingår i: IEEE Electrification Magazine. - 23255889 .- 23255897. ; 7:4, s. 22-31
  • Tidskriftsartikel (övrigt vetenskapligt)abstract
    • The region of Västra Götaland is an area in Western Sweden that has nearly 1.6 million inhabitants. In recent years, the number of trips made by the public transportation system has increased considerably, and everything indicates that it will continue to rise substantially in the coming years. Today, the region faces challenges in managing this expected increase in demand. A description of how public transportation is developing in Västra Götaland is given in the Transport Provision Program; it estimates that the number of journeys made by public transportation will double, a goal that is sought at both the national and local levels. The number of public transportation journeys made in the country would then be roughly 400 million by 2025. In response to this, capacity is expected to increase by 70% and the travel time is projected to decrease by 20-25%. Along with these developments, efforts are being made to transition to a more environmentally friendly means of transportation through the use of alternative fuels, electrification, and an increased level of automated systems (with the additional objective of reducing the number of accidents).
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5.
  • Bellone, Mauro, 1982-, et al. (författare)
  • Learning Traversability from Point Clouds in Challenging Scenarios
  • 2018
  • Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050. ; 4:1
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper aims at evaluating the capabilities to detect road traversability in urban and extra-urban scenarios ofsupport vector machine-based classifiers that use local descriptors extracted from point cloud data. The evaluation of the proposed classifiers is carried out by using four different kernels and comparing five point descriptors obtained from geometric and appearance-based features. A comparison among the performance of descriptors individually has demonstrated that the normal vector-based descriptor achieves an accuracy of 88%, outperforming by about 6%–15% all the other considered ones. To further improve the interpretation capabilities, the space of features is augmented by merging the components of each point descriptor, reaching 92% classification accuracy. A set of test scenarios have been acquired during an extensive experimental campaign using an all-terrain vehicle. Tests on real data show high classification performance for road scenarios and rural environments; the generality of the method makes it applicable for different types of mobile robots including, but not limited to, autonomous vehicles.
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6.
  • Benderius, Ola, 1985-, et al. (författare)
  • A simulation environment for analysis and optimization of driver models
  • 2011
  • Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 16113349 .- 03029743. - 978-364221798-2 ; 6777, s. 453-462
  • Konferensbidrag (refereegranskat)abstract
    • A simulation environment for evaluation and optimization of driver models is introduced and described. The simulation environment features models of vehicles and drivers, as well as a representation of the traffic environment (roads, buildings etc.). In addition, an optimization framework based on stochastic optimization algorithms has been implemented as an integral part of the simulation environment. Given observed (time series) data of driver behavior and, possibly, vehicle dynamics, the optimization framework can be used for inferring driver model parameters. The simulation environment has been evaluated in two scenarios, one involving emergency braking and one involving a double lane change.
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7.
  • Benderius, Ola, 1985-, et al. (författare)
  • Driver behaviour in unexpected critical events and in repeated exposures – a comparison
  • 2013
  • Ingår i: European Transport Research Review. - 1867-0717 .- 1866-8887. ; 6:1, s. 51-60
  • Tidskriftsartikel (refereegranskat)abstract
    • PurposeThis paper aims to determine how truck driver steering behaviour seen in repeated exposures to acritical event correlates to the behaviour resulting from an unexpected exposure to the same event.MethodsTest subjects were exposed to an unexpected critical event in a high-fidelity driving simulator. Next, a slightly modified version of the scenario was repeated several times for each subject. The driver behaviour was then analysed using standard statistical tests.ResultsIt was found that, in general, drivers keep most of their steering behaviour characteristics between test settings (unexpected and repeated). This is particularly interesting sincea similar kind of behaviour preservation is generally not found in the case of braking behaviour. In fact, onlyone significant difference was found between the two test settings, namely regarding time-to-collision at steering initiation.ConclusionsIn experiments involving both an unexpected event and several repeated events one can,at least in some cases, design the repeated event such that behavioural data collected from that setting can beused along with data from the unexpected setting. Using this procedure, one can significantly increase the amount of collected data, something that can strongly benefit, for example, driver modelling.
8.
  • Caltagirone, Luca, 1983-, et al. (författare)
  • Fast LIDAR-based road detection using fully convolutional neural networks
  • 2017
  • Ingår i: COPPLAR CampusShuttle cooperative perception & planning platform.
  • Konferensbidrag (refereegranskat)abstract
    • In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-Time applications.
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9.
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10.
  • Caltagirone, Luca, 1983-, et al. (författare)
  • LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks
  • 2019
  • Ingår i: Robotics and Autonomous Systems. - 0921-8890.
  • Tidskriftsartikel (refereegranskat)abstract
    • In this work, a deep learning approach has been developed to carry out road detection by fusing LIDAR point clouds and camera images. An unstructured and sparse point cloud is first projected onto the camera image plane and then upsampled to obtain a set of dense 2D images encoding spatial information. Several fully convolutional neural networks (FCNs) are then trained to carry out road detection, either by using data from a single sensor, or by using three fusion strategies: early, late, and the newly proposed cross fusion. Whereas in the former two fusion approaches, the integration of multimodal information is carried out at a predefined depth level, the cross fusion FCN is designed to directly learn from data where to integrate information; this is accomplished by using trainable cross connections between the LIDAR and the camera processing branches.  To further highlight the benefits of using a multimodal system for road detection, a data set consisting of visually challenging scenes was extracted from driving sequences of the KITTI raw data set. It was then demonstrated that, as expected, a purely camera-based FCN severely underperforms on this data set. A multimodal system, on the other hand, is still able to provide high accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI road benchmark where it achieved excellent performance, with a MaxF score of 96.03%, ranking it among the top-performing approaches.
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