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You searched for +publisher:"Delft University of Technology" +contributor:("Flohr, Fabian"). Showing records 1 – 2 of 2 total matches.

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Delft University of Technology

1. Ammerlaan, Jelle (author). Traffic Gesture Classification for Intelligent Vehicles.

Degree: 2020, Delft University of Technology

Self-driving vehicles have shown rapid development in recent years and continue to move towards full autonomy. For high or full automation, self-driving vehicles will have to be able to address and solve a broad range of situations, one of which is interaction with traffic agents. For correct and save maneuvering through these situations, reliable detection of agents followed by an accurate classification of the traffic gestures used by agents is essential. This problem has received limited attention in literature to date. The objective of this work is to establish and investigate a working traffic gesture pipeline by leveraging the latest developments in the fields of computer vision and machine learning. This work investigates and compares how well state-of-the-art methods translate to traffic gesture recognition and what application specific problems are encountered. Multiple configurations based on skeletal features, estimated using OpenPose, and classified using recurrent neural networks (RNN) were investigated. Skeleton estimation using OpenPose and feature representations were evaluated using an action recognition dataset with motion capture ground-truth. Three RNN network architectures, varying in complexity and size, were evaluated on traffic gestures. The robustness of the developed system regarding viewpoint variation is explored, combined with the viability of transfer learning for traffic gestures. To train and validate these methods, a new traffic gesture dataset is introduced, on which an mAP of 0,70 is achieved. The results show that the proposed methods are able to classify traffic gestures within reasonable computation time and illustrate the value of transfer learning for gesture recognition. These promising results validate the methodology used and show that this direction warrants further research. Advisors/Committee Members: Flohr, Fabian (mentor), Kooij, Julian (mentor), Gavrila, Dariu (graduation committee), de Winter, Joost (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Machine Learning; Intelligent Vehicles; Traffic Gestures; Pose estimation; Gesture recognition

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

APA (6th Edition):

Ammerlaan, J. (. (2020). Traffic Gesture Classification for Intelligent Vehicles. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6272db65-b324-40cf-83aa-6d7caf3c7917

Chicago Manual of Style (16th Edition):

Ammerlaan, Jelle (author). “Traffic Gesture Classification for Intelligent Vehicles.” 2020. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:6272db65-b324-40cf-83aa-6d7caf3c7917.

MLA Handbook (7th Edition):

Ammerlaan, Jelle (author). “Traffic Gesture Classification for Intelligent Vehicles.” 2020. Web. 23 Jan 2021.

Vancouver:

Ammerlaan J(. Traffic Gesture Classification for Intelligent Vehicles. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 23]. Available from: http://resolver.tudelft.nl/uuid:6272db65-b324-40cf-83aa-6d7caf3c7917.

Council of Science Editors:

Ammerlaan J(. Traffic Gesture Classification for Intelligent Vehicles. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:6272db65-b324-40cf-83aa-6d7caf3c7917


Delft University of Technology

2. van Beelen, Ruben (author). Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation.

Degree: 2019, Delft University of Technology

One of the most promising ideas in autonomous vehicle control systems is letting the vehicle drive autonomously outside the normal, linear, operating region and letting it "drift". By doing so, the maneuverability of the vehicle could be enhanced. To enable systems that can control this behaviour, estimation of certain vehicle states is needed with high accuracy and high frequency.In this project, a new solution to this problem is proposed by combining a mixed dynamic-kinematic observer with a single camera that produces velocity measurements based on tracking the ground plane. To improve filtering of the camera velocity measurements, the measurement error covariance matrix is updated online based on a model of the camera measurement error. Evaluation of the new methodology was done on data recorded from a 1:10 scale test vehicle and performance was assessed based on ground truth data obtained using a Motion Capture System.In normal driving conditions with correctly identified vehicle parameters, an observer without camera performs better by 25% in terms of RMSE on lateral velocity and sideslip angle estimation. However, the online adaptation of the covariance matrix results in an estimate that is at least 45% more accurate in terms of RMSE than the same observer without online covariance adaptation. Next to that, experiments show that the proposed observer with camera has better robustness to uncertainty in model parameters by almost a factor five in terms of RMSE than the observer without camera.When the grip of the tires is physically lowered and the vehicle is drifting, the proposed observer can track large sideslip angles (>30°), where the state-of-the-art observer without camera is not able. The state-of-the-art observer has an increase in RMSE of 75% on all estimated quantities in comparison to the proposed methodology. These results show that adding a camera to an existing sideslip angle observer greatly enhances robustness of the observer to uncertainty in model parameters and violation of model assumptions. This comes dat the cost of losing some accuracy in normal driving conditions.

Systems and Control

Advisors/Committee Members: Hellendoorn, Hans (mentor), Corno, M (graduation committee), Batselier, Kim (graduation committee), Pan, Wei (graduation committee), Flohr, Fabian (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: sideslip estimation; computer vision; vehicle dynamics control; covariance calculation

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

APA (6th Edition):

van Beelen, R. (. (2019). Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:c6fe2ac1-8b96-4557-9d5f-8a1c4b885bb3

Chicago Manual of Style (16th Edition):

van Beelen, Ruben (author). “Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation.” 2019. Masters Thesis, Delft University of Technology. Accessed January 23, 2021. http://resolver.tudelft.nl/uuid:c6fe2ac1-8b96-4557-9d5f-8a1c4b885bb3.

MLA Handbook (7th Edition):

van Beelen, Ruben (author). “Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation.” 2019. Web. 23 Jan 2021.

Vancouver:

van Beelen R(. Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 23]. Available from: http://resolver.tudelft.nl/uuid:c6fe2ac1-8b96-4557-9d5f-8a1c4b885bb3.

Council of Science Editors:

van Beelen R(. Adaptive Observer for Automated Emergency Maneuvers: Fusing cost-efficient onboard sensors with computer vision into a robust estimate of sideslip angle using online covariance calculation. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:c6fe2ac1-8b96-4557-9d5f-8a1c4b885bb3

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