| 1. |
- Bärgman, Jonas, 1972, et al.
(författare)
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On data security and analysis platforms for analysis of naturalistic driving data
- 2011
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Ingår i: Proceedings of the 8th European Congress and Exhibition on Intelligent Transport Systems and Services, June 2011, Lyon.
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Konferensbidrag (refereegranskat)abstract
- Studies involving naturalistic driving data, of which Naturalistic Field Operational Tests (N-FOTs) are a subset, are becoming increasingly important for understanding the factors influencing accident causation as well as for the development and evaluation of active safety systems. The methodology project FESTA developed a handbook on how to plan and implement FOTs. This handbook has been extensively used as a guideline in the euroFOT project. However, “the devil is in the details” when implementing e.g. the platforms for data security and analysis in projects which deal with analysis of large amounts of sensitive naturalistic driving data, such as euroFOT. That is, although a guideline such as FESTA is used, how the details are implemented is what makes the implementation a success or not. This paper is a case description of the implementation of the data security and analysis platform used for euroFOT (and other naturalistic data projects) at the SAFER Vehicle and Traffic Safety Centre. The paper covers aspects ranging from physical access to analysis rooms and corresponding digital access, via the platforms for pre-processing of data, to the platforms for information extraction for hypothesis analysis and statistics. The considerations in the design and choice of these platforms include subjects (drivers) privacy concerns, industry commercial concerns, as well as the needs and requirements from the analysis.
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| 2. |
- Englund, Cristofer, et al.
(författare)
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Using random forests for data mining data and drowsy driver classification using FOT data
- 2012
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Ingår i: Proceedings of On the Move to Meaningful Internet Systems. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783642336140 ; , s. 748-758
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Konferensbidrag (refereegranskat)abstract
- Data mining techniques based on Random forests are explored to gain knowledge about data in a Field Operational Test (FOT) database. We compare the performance of a Random forest, a Support Vector Machine and a Neural network used to separate drowsy from alert drivers. 25 variables from the FOT data was utilized to train the models. It is experimentally shown that the Random forest outperforms the other methods while separating drowsy from alert drivers. It is also shown how the Random forest can be used for variable selection to find a subset of the variables that improves the classification accuracy. Furthermore it is shown that the data proximity matrix estimated from the Random forest trained using these variables can be used to improve both classification accuracy, outlier detection and data visualization.
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| 3. |
- Fors, Carina, et al.
(författare)
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Camera-based sleepiness detection : final report of the project SleepEYE
- 2011
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Two literature reviews were conducted in order to identify indicators of driver sleepiness and distraction. Three sleepiness indicators – blink duration, blink frequency and Perclos – were implemented in the camera system.The aims of the study were firstly to develop and evaluate a low cost 1-camera unit for detection of driver impairment, and secondly to identify indicators of driver sleepiness and to create a sleepiness classifier for driving simulators.The project included two experiments. The first was a field test where 18 participants conducted one alert and one sleepy driving session on a motorway. 16 of the 18 participants also participated in the second experiment which was a simulator study similar to the field test.The field test data was used for evaluation of the 1-camera system, with respect to the sleepiness indicators. Blink parameters from the 1-camera system was compared to blink parameters obtained from a reference 3-camera system and from the EOG. It was found that the 1-camera system missed many blinks and that the blink duration was not in agreement with the blink duration obtained from the EOG and from the reference 3-camera system. However, the results also indicated that it should be possible to improve the blink detection algorithm since the raw data looked well in many cases where the algorithm failed to identify blinks.The sleepiness classifier was created using data from the simulator experiment. In the first step, the indicators identified in the literature review were implemented and evaluated. The indicators also included driving and context related parameters in addition to the blink related ones. The most promising indicators were then used as inputs to the classifier.
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| 4. |
- Fors, Carina, et al.
(författare)
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Investigation of driver sleepiness in FOT data : final report of the project SleepEYE II, part 2
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Driver sleepiness contributes to a great number of motor vehicle accidents every year. In order to reduce the number of sleepiness related accidents, more knowledge on e.g. prevalence, countermeasures and driver behaviour is needed. Data from field operational tests (FOT) has a potential to provide such knowledge with high ecological validity. The objective of the project was to propose and evaluate methods for identification of driver sleepiness in FOT data. More specifically, the aim was to identify objective indicators of sleepiness – based on driving behaviour, eye blink behaviour and models of circadian rhythm – and to evaluate a subjective video scoring method for estimating driver sleepiness levels. Data from two separate projects were used: 1) the ViP-project SleepEYE, in which a controlled field test was conducted, and 2) euroFOT, which was a large scale FOT. In a first step the data quality of blink-based indicators obtained from a camera system was evaluated. It was concluded that the data quality had to be improved and thus, a new detection algorithm was devised and implemented. The new detection algorithm had an acceptable detection rate (approximately 50 %) when applied to data from the SleepEYE field test, but for euroFOT data the number of identified blinks was very low (< 5 blinks/min) in about half of the trips. There is thus a need for further improvements of the blink detection algorithm. An in-depth study on indicators of driver sleepiness was carried out using data collected in the SleepEYE experiment, with the purpose of employing the best indicators to study driver sleepiness in the euroFOT database. The most promising indicators were found to be mean blink duration and number of line crossings. A sleepiness classifier was suggested based on the distribution of the data (i.e. visual inspection). When applied to SleepEYE data the classifier was found to have good specificity while the sensitivity of the classifier was not so good. From euroFOT no true data on the drivers’ sleepiness levels were available and it was therefore not possible to evaluate the performance of the classifier. However, an explorative analysis showed that only very few data points were classified as sleepy. This may be reasonable since most trips were conducted during daytime, but it is a somewhat disappointing result for the project. A study was carried out on whether it is possible to use video recordings of drivers in order to estimate the drivers’ self-rated level of sleepiness. Forty participants rated 54 one-minute video clips of an equal number of sleepy and alert drivers on a scale with three levels (alert, first signs of sleepiness, very sleepy). The results of the study showed that performing such observer rated sleepiness (ORS) estimations on drivers is extremely difficult. The videos available in FOTs are usually of rather poor quality which, clearly limits the possibility of making reliable observer rated sleepiness estimations. In conclusion, studying driver sleepiness in (existing) FOT data is difficult, for several reasons: 1) eye camera based indicators suffer from detection errors and low detection rate, 2) driving-based indicators are influenced by e.g. road curvature and traffic density, 3) models of sleepiness cannot be used since no information on hours slept and time awake is available, and 4) video scoring is not reliable, at least not given the quality of the available video recordings. In future studies on driver sleepiness in FOTs sleepiness should be addressed in the FOT design. Some information about the drivers' sleep and sleepiness (ratings, sleep diaries, etc.) must be collected during the test; otherwise it will be very difficult to get any useful results.
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