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You searched for +publisher:"Delft University of Technology" +contributor:("Stapel, Jork"). Showing records 1 – 3 of 3 total matches.

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Delft University of Technology

1. el Hassnaoui, Mounir (author). Measuring driver perception during on-road eye-tracking: Combining gaze behaviour and vehicle’s road scene perception.

Degree: 2019, Delft University of Technology

Long before humans will completely trust fully automated vehicles, partial and conditional automation where the human driver is still in the loop will dominate the era of autonomous vehicles. However, more than 90% of traffic accidents are due to human errors, of which approximately half appear to be due to perceptual errors. Especially at busy and complex intersections that have a high density of visual stimuli. This poses a high demand for accurate measurement of the driver's situation awareness, for real-world driver monitoring. Eye tracking seems to be an ideal method to determine what the driver has or has not seen, since people tend to look at what they inquire information from. The main objective of this thesis assignment was therefore to develop a platform that combines the driver's gaze behaviour in combination with the vehicle's road scene perception, to set up a real-world driving experiment to gather such data on the road, and to come up with a proof of concept that gaze behaviour combined with situational knowledge can be predictive of SA. The platform developed consisted of an eye-tracker with four cameras constructed in the available Toyota Prius of the department of Intelligent Vehicles, which is equipped for self-driving. The driver's gaze was layered over the object identification data from the vehicle, to see which objects are looked at or fixated upon and which are not. A real-world driving experiment was then conducted in which participants (N = 14) performed a driving task and a recall task. The driving task consisted of 8 intersection crossings in which mostly left turns were made to manoeuvre the vehicle off a main priority road. After each crossing, the participants performed a recall task in which they had to select images of the object they encountered during the driving task. The results showed that 88.1% of all relevant objects they encountered were seen with central vision, of which 41.8% were recalled. The remainder 11.9% of all relevant objects that were not seen, have only been in peripheral view, of which 18.2% were recalled. These preliminary results indicate that at least 2.2% (18.2% of 11.9%) of relevant objects are perceived by the driver using peripheral vision. The variables seen, first saccade angle and first saccade moment contributed significantly to a prediction model that predicted whether a relevant object would be recalled by the driver. The variables fixation count, total glance duration and saccade count were not significant predictor variables. The conclusion was drawn that the results of this exploratory research confirmed that gaze behaviour combined with situational knowledge can be predictive of driver SA. However, a crucial recommendation for future research is an improved recall task procedure to obtain higher recall rates and therefore more accurate prediction of SA. This outcome could be of great value for future research and development of applications that assist or steer the driver's attention to possible threats or objects the driver is missing or not… Advisors/Committee Members: Happee, Riender (mentor), Stapel, Jork (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Eye Tracking; Autonomous driving; Situation Awareness; Real-world driving

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

el Hassnaoui, M. (. (2019). Measuring driver perception during on-road eye-tracking: Combining gaze behaviour and vehicle’s road scene perception. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:94542399-7a90-4af7-b526-a785683eb8cd

Chicago Manual of Style (16th Edition):

el Hassnaoui, Mounir (author). “Measuring driver perception during on-road eye-tracking: Combining gaze behaviour and vehicle’s road scene perception.” 2019. Masters Thesis, Delft University of Technology. Accessed March 08, 2021. http://resolver.tudelft.nl/uuid:94542399-7a90-4af7-b526-a785683eb8cd.

MLA Handbook (7th Edition):

el Hassnaoui, Mounir (author). “Measuring driver perception during on-road eye-tracking: Combining gaze behaviour and vehicle’s road scene perception.” 2019. Web. 08 Mar 2021.

Vancouver:

el Hassnaoui M(. Measuring driver perception during on-road eye-tracking: Combining gaze behaviour and vehicle’s road scene perception. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 08]. Available from: http://resolver.tudelft.nl/uuid:94542399-7a90-4af7-b526-a785683eb8cd.

Council of Science Editors:

el Hassnaoui M(. Measuring driver perception during on-road eye-tracking: Combining gaze behaviour and vehicle’s road scene perception. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:94542399-7a90-4af7-b526-a785683eb8cd


Delft University of Technology

2. Onkhar, Vishal (author). Algorithmic detection of eye contact in driver-pedestrian interactions.

Degree: 2020, Delft University of Technology

Pedestrians today are very vulnerable on urban roads. Clear communication between drivers and pedestrians is one way to reduce their plight. Non-verbal communication in particular plays an important role in road safety, and eye contact is a kind of non-verbal communication that has the potential to minimize on-road collisions. However, with the advent of automated vehicles, driver-pedestrian eye contact loses its meaning since there is no longer a driver. It is therefore useful to study and detect eye contact so that the knowledge obtained may be applied to automated vehicles of the future. To this end, the following research goals were adopted: (a) What is eye contact between a pedestrian and a driver in a car? How can eye contact be defined/operationalized using an algorithm?, (b) How accurate is the algorithm that operationalizes eye contact?, and (c) How is it possible to use two eye-trackers with inertial measurement units (IMUs) and pedestrian recognition in a Toyota Prius car to reconstruct the entire driver-pedestrian interaction through a 3-D animation? An indoor experiment, designed to resemble a driver-pedestrian interaction at a pedestrian crossing was conducted with 31 participants. Participants’ (pedestrians’) eyes were tracked using a Tobii Pro Glasses 2 eye-tracker and the researcher’s (driver’s) eyes were tracked using a Smart Eye Pro dx eye-tracker, both of which were synchronized. Participants’ locations were also tracked using a stereo camera equipped with pedestrian detection capabilities. Pedestrians imagined that they were on a real road and performed six types of trials where they stood on / crossed from the left / right side curb in front of the stationary vehicle while either making eye contact or not making eye contact with the driver. The order of the trials was randomized, and each trial consisted of 3 repetitions of a driver-pedestrian interaction. If the driver and pedestrian were looking at each other at the same time there was eye contact, otherwise there was no eye contact. Significant differences in the percentages of eye contact between pedestrians standing on the left (median duration of 0.42 s) and the right (median duration of 0.54 s) were found. No significant differences in the percentages of eye contact between pedestrians crossing from the left (median duration of 1.23 s) and the right (median duration of 1.39 s) were found. Eye contact instants within trials were algorithmically detected by finding the angle between the 3-D gaze direction vectors of the driver and the pedestrian, and comparing it to an ‘eye contact threshold’. Trials were classified as either involving eye contact or not involving eye contact based on their percentages of eye contact instants. The classification performance of the algorithm was quantified using two ground truths: (1) Imposed eye contact (in half of the trials, participants were instructed to make eye contact; in the other half, participants were instructed not to make eye contact), and (2) Manually annotated areas of interest (AOIs)… Advisors/Committee Members: de Winter, Joost (mentor), Bazilinskyy, Pavlo (graduation committee), Happee, Riender (graduation committee), Stapel, Jork (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Eye-tracking; Eye contact; Driver-pedestrian interaction; Human factors

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Onkhar, V. (. (2020). Algorithmic detection of eye contact in driver-pedestrian interactions. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b14b48ac-6df0-4a87-bb8d-5763b2697694

Chicago Manual of Style (16th Edition):

Onkhar, Vishal (author). “Algorithmic detection of eye contact in driver-pedestrian interactions.” 2020. Masters Thesis, Delft University of Technology. Accessed March 08, 2021. http://resolver.tudelft.nl/uuid:b14b48ac-6df0-4a87-bb8d-5763b2697694.

MLA Handbook (7th Edition):

Onkhar, Vishal (author). “Algorithmic detection of eye contact in driver-pedestrian interactions.” 2020. Web. 08 Mar 2021.

Vancouver:

Onkhar V(. Algorithmic detection of eye contact in driver-pedestrian interactions. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 08]. Available from: http://resolver.tudelft.nl/uuid:b14b48ac-6df0-4a87-bb8d-5763b2697694.

Council of Science Editors:

Onkhar V(. Algorithmic detection of eye contact in driver-pedestrian interactions. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:b14b48ac-6df0-4a87-bb8d-5763b2697694


Delft University of Technology

3. Pizzigoni, Edoardo (author). ACC target performance setting via NDS big data analysis.

Degree: 2019, Delft University of Technology

Advanced Driving Assistance Systems (ADAS) technologies like Adaptive Cruise Control (ACC) are becoming the normality for many users, and many major car manufacturers are introducing SAE level 2 and 3 automation systems into the market. The main advantage of Automated Vehicles (AV) will be the significant decrease in road accidents and casualties. However, a significant shift from conventional to automated vehicles must occur before it can have a positive impact on society. If the behaviour of the vehicle is not perceived as natural, the user will most likely not activate the ADAS features again. During this study a naturalistic dataset is used to investigate the driver behaviour, in the hope of bringing the current ACC logic to a more human-like behaviour that will feel more natural to the driver. The research question summarizes the final objective of this study: How can Naturalistic Driving Study (NDS) datasets be used in target performance setting for ACC systems? This study will answer the research question by studying human behaviour in the scene of following an accelerating vehicle. The main body of this thesis is divided in three chapters, one for each step of the research. First the information about the used datasets are provided together with the methodologies used to extract the relevant time-series data. Secondly driver behaviour models are created in order to mathematically characterize human behaviour. The strength of the created models is their ability to represent the full range of driver behaviour in terms of driving style. The aggressiveness parameter of the model can be easily adjusted to represent different percentiles of driver behaviour. This allows for a quick and effective tuning process: by changing a single parameter the driving style of the model can be fully modified. Finally, the driver behaviour models are implemented into a simulation environment. The models are simulated against an existing ACC logic in order to assess the difference in behaviour. The comparison highlighted two conclusions: first, the ACC logic behaves in a very conservative way compared to driver behaviour, especially when starting from standstill. Secondly, the kept by the ACC logic was not consistent throughout the speed range. This variation of the logic's driving style could result even more bothersome to the customer than its general conservative behaviour. The string stability of the driver behaviour models was also assessed. Although the proposed logic proved more stable than the regular ACC logic, it still cannot reach full string stability. Hopefully, with the method developed in this study, the process of getting accustomed to this new technology will become easier for the customer. Thanks to the driver behaviour models the motion of the vehicle can feel familiar and predictable, with the controller becoming part of the Human Machine Interface (HMI). As the customer gets more familiar with this technology his expectation will also increase and change, especially as the levels of automation start to… Advisors/Committee Members: Happee, Riender (mentor), Wang, Meng (graduation committee), Stapel, Jork (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: ACC; ADAS; driver modeling; driver behaviour; Automated vehicles; car following; naturalistic driving study

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pizzigoni, E. (. (2019). ACC target performance setting via NDS big data analysis. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:da09fb40-8142-4545-8440-2c91ef7434ff

Chicago Manual of Style (16th Edition):

Pizzigoni, Edoardo (author). “ACC target performance setting via NDS big data analysis.” 2019. Masters Thesis, Delft University of Technology. Accessed March 08, 2021. http://resolver.tudelft.nl/uuid:da09fb40-8142-4545-8440-2c91ef7434ff.

MLA Handbook (7th Edition):

Pizzigoni, Edoardo (author). “ACC target performance setting via NDS big data analysis.” 2019. Web. 08 Mar 2021.

Vancouver:

Pizzigoni E(. ACC target performance setting via NDS big data analysis. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 08]. Available from: http://resolver.tudelft.nl/uuid:da09fb40-8142-4545-8440-2c91ef7434ff.

Council of Science Editors:

Pizzigoni E(. ACC target performance setting via NDS big data analysis. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:da09fb40-8142-4545-8440-2c91ef7434ff

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