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You searched for subject:(Vulnurable Road Users). Showing records 1 – 2 of 2 total matches.

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Delft University of Technology

1. Tzouras, Panagiotis G. (author). Implications of trams mixed-operations in urban areas on the safety of vulnerable road users.

Degree: 2019, Delft University of Technology

Tram is a sustainable mode of transport, which is able to transform the modern city centers through the various urban regeneration projects that usually come together with it. Tram tracks are often shared with either motorized traffic or pedestrians/cyclists. In this mixed traffic reality, tram accidents are very rare but severe at the same time. Tram driving is really a complex and very demanding task, since the driver should run on time, maintain his/her concentration, predict the behavior of other road users and protect the tram passengers from falls inside the vehicle cabin. In the past, a limited number of studies has attempted to examine tram safety, especially in urban areas, where trams interact with vulnerable road users (VRUs). Subjective notions of traffic safety, that are more connected with the behavior of tram drivers, such as: perceived safety and driving stress, have never been quantified. This thesis covers all the previously mentioned research gaps in order to explain tram safety problems. By utilizing the new knowledge, a list of practical recommendations, which can reinforce tram safety without downgrading system efficiency, is developed. For the development of statistical models related with perceived safety and driving stress, a stated preferences experiment was conducted in Athens. In the survey, tram drivers rated perceived safety and driving stress in a 7-point Likert scale. According to the estimated model of perceived safety, non-exclusive alignments, such as tram/pedestrian malls and mixed traffic operation, downgrade perceived safety. Furthermore, the existence of an unprotected pedestrian crossing and high volumes of VRUs influenced perceived safety negatively. Driving stress is affected mainly by arrival delay and load of standing passengers. Route familiarity is an additional important factor that influences driving stress. The existence of many random beta parameters in perceived safety and driving stress models confirms the subjective nature of these notions. No statistically significant correlation between these two previous notions was observed. Experienced tram drivers believe that they are ready to respond properly in a section that they perceive as unsafe, if they are familiar with it. If there is no familiarity, tram drivers lack confidence and therefore driving stress is increased. Objective safety was examined in the tram network of Amsterdam using accident records and spatial data, such as location of stations and pedestrian/cycling crossings, level of tram lines separation, cycling intensity, city attraction poles and city districts. In Amsterdam, more accidents per km appeared in tram/pedestrian malls; yet, most of the fatal accidents have occurred in tram tracks that are not shared with other road users. High concentrations of tram accidents were also observed around attraction poles and inside the city center, where the flow of VRUs is quite high. Lastly, as a practical recommendation, a consistent design of a tram line using the knowledge from the estimated perceived… Advisors/Committee Members: Hagenzieker, Marjan (mentor), Farah, Haneen (graduation committee), Papadimitriou, Eleonora (graduation committee), van Oort, Niels (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Tram; Traffic safety; Vulnurable Road Users; Perceived safety; Driving stress

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

APA (6th Edition):

Tzouras, P. G. (. (2019). Implications of trams mixed-operations in urban areas on the safety of vulnerable road users. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a1f0c922-a397-4761-8d4d-022a54fbb5f8

Chicago Manual of Style (16th Edition):

Tzouras, Panagiotis G (author). “Implications of trams mixed-operations in urban areas on the safety of vulnerable road users.” 2019. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:a1f0c922-a397-4761-8d4d-022a54fbb5f8.

MLA Handbook (7th Edition):

Tzouras, Panagiotis G (author). “Implications of trams mixed-operations in urban areas on the safety of vulnerable road users.” 2019. Web. 23 Jan 2021.

Vancouver:

Tzouras PG(. Implications of trams mixed-operations in urban areas on the safety of vulnerable road users. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 23]. Available from: http://resolver.tudelft.nl/uuid:a1f0c922-a397-4761-8d4d-022a54fbb5f8.

Council of Science Editors:

Tzouras PG(. Implications of trams mixed-operations in urban areas on the safety of vulnerable road users. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:a1f0c922-a397-4761-8d4d-022a54fbb5f8


Delft University of Technology

2. Katsaounis, Georgios (author). Extended Object Tracking of Pedestrians in Automotive Applications.

Degree: 2019, Delft University of Technology

Recent advances in sensor technology have lead to increased resolution of novel sensors, while tracking applications where distance between sensors and objects of interest is very small have gained research interest recently. In these cases, it is possible that multiple sensor detections are generated by each object of interest. Extended Object Tracking (EOT) approaches consist of algorithms which make use of multiple sensor detections per object to jointly estimate their kinematic and shape extent attributes within the Bayesian tracking framework. In the last decade, various EOT algorithms have been proposed for different types of tracking applications. This M.Sc. thesis project addresses the problem of extended tracking of a single pedestrian walking in the area of a stationary vehicle (referred as ego-vehicle in this report) during a real automotive scenario. The objective is to achieve accurate estimation of both the kinematic attributes (2D centroid position/velocity), as well as its shape extent in x-y plane. In more detail, PreScan software is enabled to design a simulation scenario that is very close to a real automotive application, in terms of motion characteristics of objects of interest and sensor data acquisition. In the considered scenario, different sensor modalities are mounted on the ego-vehicle, namely a Lidar sensor and a mono camera sensor. Moreover, OpenPose library is employed to to obtain pose detections of human body parts from obtained camera images. Concerning shape extent representation, the simplest and most popular approach in previous studies, in general and especially for VRUs tracking, is to assume an elliptical shape. In fact, the Random Matrix Model (RMM), proposed originally by Koch, 2008, is a state-of-the-art EOT state modeling approach that allows for joint estimation of centroid kinematics and physical extent for considered elliptical objects of interest. Based on that, a RMM-based filter using Lidar position measurements has been proposed by Feldmann, 2011. In this project, this algorithm is used as a baseline filter for comparison with our proposed algorithm. In addition, an alternative tracking algorithm is proposed in this study, which has the following differences with respect to the baseline filter: State Initialization of the filter: In our proposed version of the tracking algorithm, human pose detections of shoulders and ankles are are associated with obtained Lidar position measurements in order to provide initial values for the kinematic state (2D position/velocity) and shape parameters (ellipse orientation and semi-axes lengths) of the pedestrian.Measurement Update step of the filter: In our proposed version of the tracking algorithm, camera-obtained pose detections of pedestrian shoulders are associated with obtained Lidar position measurements in order to create an extra measurement, for pedestrian heading angle. Subsequently, a nonlinear filtering update step fusing Lidar-obtained point cloud data for pedestrian position and human-pose-obtained… Advisors/Committee Members: Alonso Mora, Javier (mentor), Domhof, Joris (mentor), Tasoglou, Athanasios (mentor), Gavrila, Dariu (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Extended Object Tracking; Vulnurable Road Users; Pedestrians; Environmental Perception; Automotive Applications; Lidar sensor; Mono camera sensor; Sensor Fusion; Random Matrix Model; Elliptical shape; OpenPose library; Human Pose Detections; position measurement; heading angle measurement; Extended Kalman Filter; Kalman Filter

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

APA (6th Edition):

Katsaounis, G. (. (2019). Extended Object Tracking of Pedestrians in Automotive Applications. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749

Chicago Manual of Style (16th Edition):

Katsaounis, Georgios (author). “Extended Object Tracking of Pedestrians in Automotive Applications.” 2019. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749.

MLA Handbook (7th Edition):

Katsaounis, Georgios (author). “Extended Object Tracking of Pedestrians in Automotive Applications.” 2019. Web. 23 Jan 2021.

Vancouver:

Katsaounis G(. Extended Object Tracking of Pedestrians in Automotive Applications. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 23]. Available from: http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749.

Council of Science Editors:

Katsaounis G(. Extended Object Tracking of Pedestrians in Automotive Applications. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:d7226685-9ffe-417f-9939-2167a9dfd749

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