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You searched for +publisher:"Delft University of Technology" +contributor:("Gavrila, Dariu"). Showing records 1 – 16 of 16 total matches.

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Delft University of Technology

1. Uittenbogaard, Ries (author). Moving object detection and image inpainting in street-view imagery.

Degree: 2018, Delft University of Technology

 In this thesis, a pipeline is created consisting of two parts. In the first part, the moving objects (cars, cyclists, pedestrians) are detected in street-view… (more)

Subjects/Keywords: Image inpainting; Moving object detection; Image segmentation; Generative Adversarial Networks; LIDAR

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APA (6th Edition):

Uittenbogaard, R. (. (2018). Moving object detection and image inpainting in street-view imagery. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5528c3a3-8ff8-4c96-b181-a9bbae0e6d28

Chicago Manual of Style (16th Edition):

Uittenbogaard, Ries (author). “Moving object detection and image inpainting in street-view imagery.” 2018. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:5528c3a3-8ff8-4c96-b181-a9bbae0e6d28.

MLA Handbook (7th Edition):

Uittenbogaard, Ries (author). “Moving object detection and image inpainting in street-view imagery.” 2018. Web. 27 Nov 2020.

Vancouver:

Uittenbogaard R(. Moving object detection and image inpainting in street-view imagery. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:5528c3a3-8ff8-4c96-b181-a9bbae0e6d28.

Council of Science Editors:

Uittenbogaard R(. Moving object detection and image inpainting in street-view imagery. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:5528c3a3-8ff8-4c96-b181-a9bbae0e6d28


Delft University of Technology

2. 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… (more)

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

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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 November 27, 2020. 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. 27 Nov 2020.

Vancouver:

Ammerlaan J(. Traffic Gesture Classification for Intelligent Vehicles. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2020 Nov 27]. 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

3. Hafner, Frank (author). Cross-Modal Re-identification of Persons between RGB and Depth.

Degree: 2018, Delft University of Technology

 Cross-modal person re-identification is the task to re-identify a person which was sensedin a first modality, like in visible light (RGB), in a second modality,… (more)

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APA (6th Edition):

Hafner, F. (. (2018). Cross-Modal Re-identification of Persons between RGB and Depth. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6797b0e2-5a20-444f-8d32-a73581e00ff5

Chicago Manual of Style (16th Edition):

Hafner, Frank (author). “Cross-Modal Re-identification of Persons between RGB and Depth.” 2018. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:6797b0e2-5a20-444f-8d32-a73581e00ff5.

MLA Handbook (7th Edition):

Hafner, Frank (author). “Cross-Modal Re-identification of Persons between RGB and Depth.” 2018. Web. 27 Nov 2020.

Vancouver:

Hafner F(. Cross-Modal Re-identification of Persons between RGB and Depth. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:6797b0e2-5a20-444f-8d32-a73581e00ff5.

Council of Science Editors:

Hafner F(. Cross-Modal Re-identification of Persons between RGB and Depth. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:6797b0e2-5a20-444f-8d32-a73581e00ff5


Delft University of Technology

4. Ai, Zhiwei (author). Semantic Segmentation of Large-scale Urban Scenes from Point Clouds.

Degree: 2019, Delft University of Technology

Deep learning methods have been demonstrated to be promising in semantic segmentation of point clouds. Existing works focus on extracting informative local features based on… (more)

Subjects/Keywords: Deep Learning; Point Clouds; Semantic Segmentation

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APA (6th Edition):

Ai, Z. (. (2019). Semantic Segmentation of Large-scale Urban Scenes from Point Clouds. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d

Chicago Manual of Style (16th Edition):

Ai, Zhiwei (author). “Semantic Segmentation of Large-scale Urban Scenes from Point Clouds.” 2019. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d.

MLA Handbook (7th Edition):

Ai, Zhiwei (author). “Semantic Segmentation of Large-scale Urban Scenes from Point Clouds.” 2019. Web. 27 Nov 2020.

Vancouver:

Ai Z(. Semantic Segmentation of Large-scale Urban Scenes from Point Clouds. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d.

Council of Science Editors:

Ai Z(. Semantic Segmentation of Large-scale Urban Scenes from Point Clouds. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:a9cedaac-42ae-4cb0-9c14-67bab8e96a6d


Delft University of Technology

5. van Schouwenburg, Sietse (author). Evaluating SLAM in an urban dynamic environment.

Degree: 2019, Delft University of Technology

 Simultaneous Localization And Mapping (SLAM) algorithms provide accurate localization for autonomous vehicles and provide essential information for the path planning module. However, SLAM algorithms as-… (more)

Subjects/Keywords: SLAM; simulation; computer vision; simulataneous localization and mapping; localization; mapping; visual SLAM; ORB SLAM; CARLA

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APA (6th Edition):

van Schouwenburg, S. (. (2019). Evaluating SLAM in an urban dynamic environment. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:af041e54-7660-4fb1-b68c-0af3aaf27c52

Chicago Manual of Style (16th Edition):

van Schouwenburg, Sietse (author). “Evaluating SLAM in an urban dynamic environment.” 2019. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:af041e54-7660-4fb1-b68c-0af3aaf27c52.

MLA Handbook (7th Edition):

van Schouwenburg, Sietse (author). “Evaluating SLAM in an urban dynamic environment.” 2019. Web. 27 Nov 2020.

Vancouver:

van Schouwenburg S(. Evaluating SLAM in an urban dynamic environment. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:af041e54-7660-4fb1-b68c-0af3aaf27c52.

Council of Science Editors:

van Schouwenburg S(. Evaluating SLAM in an urban dynamic environment. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:af041e54-7660-4fb1-b68c-0af3aaf27c52


Delft University of Technology

6. Wang, Ziqi (author). Depth-aware Instance Segmentation with a Discriminative Loss Function.

Degree: 2018, Delft University of Technology

This work explores the possibility of incorporating depth information into a deep neural network to improve accuracy of RGB instance segmentation. The baseline of this… (more)

Subjects/Keywords: Deep Learning; Computer Vision; instance segmentation; Intelligent Vehicles

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APA (6th Edition):

Wang, Z. (. (2018). Depth-aware Instance Segmentation with a Discriminative Loss Function. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:02bd3582-3304-4595-baa6-c6fcca755418

Chicago Manual of Style (16th Edition):

Wang, Ziqi (author). “Depth-aware Instance Segmentation with a Discriminative Loss Function.” 2018. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:02bd3582-3304-4595-baa6-c6fcca755418.

MLA Handbook (7th Edition):

Wang, Ziqi (author). “Depth-aware Instance Segmentation with a Discriminative Loss Function.” 2018. Web. 27 Nov 2020.

Vancouver:

Wang Z(. Depth-aware Instance Segmentation with a Discriminative Loss Function. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:02bd3582-3304-4595-baa6-c6fcca755418.

Council of Science Editors:

Wang Z(. Depth-aware Instance Segmentation with a Discriminative Loss Function. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:02bd3582-3304-4595-baa6-c6fcca755418


Delft University of Technology

7. Wout, Daan (author). Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework.

Degree: 2019, Delft University of Technology

A prevalent approach for learning a control policy in the model-free domain is by engaging Reinforcement Learning (RL). A well known disadvantage of RL is… (more)

Subjects/Keywords: Machine Learning; Interactive Learning; Gaussian Process; Regression; Feedback

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APA (6th Edition):

Wout, D. (. (2019). Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d6cff61f-8e74-4714-b713-f127c1392b7a

Chicago Manual of Style (16th Edition):

Wout, Daan (author). “Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework.” 2019. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:d6cff61f-8e74-4714-b713-f127c1392b7a.

MLA Handbook (7th Edition):

Wout, Daan (author). “Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework.” 2019. Web. 27 Nov 2020.

Vancouver:

Wout D(. Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:d6cff61f-8e74-4714-b713-f127c1392b7a.

Council of Science Editors:

Wout D(. Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:d6cff61f-8e74-4714-b713-f127c1392b7a


Delft University of Technology

8. Wymenga, Jan (author). Weather Condition Estimation in Automated Vehicles.

Degree: 2018, Delft University of Technology

This work presents a multi-sensor approach for weather condition estimation in automated vehicles. Using combined data from weather sensors (barometer, hygrometer, etc) and an in-vehicle… (more)

Subjects/Keywords: weather types; machine learning; intelligent vehicles; vision; driving; weather

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APA (6th Edition):

Wymenga, J. (. (2018). Weather Condition Estimation in Automated Vehicles. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94

Chicago Manual of Style (16th Edition):

Wymenga, Jan (author). “Weather Condition Estimation in Automated Vehicles.” 2018. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94.

MLA Handbook (7th Edition):

Wymenga, Jan (author). “Weather Condition Estimation in Automated Vehicles.” 2018. Web. 27 Nov 2020.

Vancouver:

Wymenga J(. Weather Condition Estimation in Automated Vehicles. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94.

Council of Science Editors:

Wymenga J(. Weather Condition Estimation in Automated Vehicles. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:421b3c6d-b85e-4876-a963-4094b35dea94


Delft University of Technology

9. Kossen, Rebecca (author). Fault Diagnosis of Self-Localization in Autonomous Vehicles Using a Model-Based Approach: The WEpods Case.

Degree: 2019, Delft University of Technology

 Autonomous driving is a development that has gained a lot of attention lately, because it can lead to major improvements in the mobility sector. One… (more)

Subjects/Keywords: Autonomous Vehicles; Fault Detection; Localization; model-based

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APA (6th Edition):

Kossen, R. (. (2019). Fault Diagnosis of Self-Localization in Autonomous Vehicles Using a Model-Based Approach: The WEpods Case. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b97942a0-61c3-4ce4-b961-8121230cba17

Chicago Manual of Style (16th Edition):

Kossen, Rebecca (author). “Fault Diagnosis of Self-Localization in Autonomous Vehicles Using a Model-Based Approach: The WEpods Case.” 2019. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:b97942a0-61c3-4ce4-b961-8121230cba17.

MLA Handbook (7th Edition):

Kossen, Rebecca (author). “Fault Diagnosis of Self-Localization in Autonomous Vehicles Using a Model-Based Approach: The WEpods Case.” 2019. Web. 27 Nov 2020.

Vancouver:

Kossen R(. Fault Diagnosis of Self-Localization in Autonomous Vehicles Using a Model-Based Approach: The WEpods Case. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:b97942a0-61c3-4ce4-b961-8121230cba17.

Council of Science Editors:

Kossen R(. Fault Diagnosis of Self-Localization in Autonomous Vehicles Using a Model-Based Approach: The WEpods Case. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:b97942a0-61c3-4ce4-b961-8121230cba17


Delft University of Technology

10. Bos, Evert (author). Including traffic light recognition in general object detection with YOLOv2.

Degree: 2019, Delft University of Technology

With an in vehicle camera many different things can be done that are essential for ADAS or autonomous driving mode in a vehicle. First, it… (more)

Subjects/Keywords: Traffic Light recognition; machine learning; YOLO; object detection; COCO; LISA

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APA (6th Edition):

Bos, E. (. (2019). Including traffic light recognition in general object detection with YOLOv2. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03

Chicago Manual of Style (16th Edition):

Bos, Evert (author). “Including traffic light recognition in general object detection with YOLOv2.” 2019. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03.

MLA Handbook (7th Edition):

Bos, Evert (author). “Including traffic light recognition in general object detection with YOLOv2.” 2019. Web. 27 Nov 2020.

Vancouver:

Bos E(. Including traffic light recognition in general object detection with YOLOv2. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03.

Council of Science Editors:

Bos E(. Including traffic light recognition in general object detection with YOLOv2. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03


Delft University of Technology

11. 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… (more)

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 (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 November 27, 2020. 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. 27 Nov 2020.

Vancouver:

Katsaounis G(. Extended Object Tracking of Pedestrians in Automotive Applications. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. 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


Delft University of Technology

12. YANG, MINGHAO (author). Efficient Neural Network Architecture Search.

Degree: 2019, Delft University of Technology

One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training. It can be treated as a Network… (more)

Subjects/Keywords: NAS; Deep Learning; ICML; Artificial Intelligence

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APA (6th Edition):

YANG, M. (. (2019). Efficient Neural Network Architecture Search. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9985c543-cb4e-4259-b6f8-b44ba433f1e3

Chicago Manual of Style (16th Edition):

YANG, MINGHAO (author). “Efficient Neural Network Architecture Search.” 2019. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:9985c543-cb4e-4259-b6f8-b44ba433f1e3.

MLA Handbook (7th Edition):

YANG, MINGHAO (author). “Efficient Neural Network Architecture Search.” 2019. Web. 27 Nov 2020.

Vancouver:

YANG M(. Efficient Neural Network Architecture Search. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:9985c543-cb4e-4259-b6f8-b44ba433f1e3.

Council of Science Editors:

YANG M(. Efficient Neural Network Architecture Search. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:9985c543-cb4e-4259-b6f8-b44ba433f1e3


Delft University of Technology

13. Glastra, Thom (author). Enabling GLOSA for on-street operating traffic light controllers.

Degree: 2020, Delft University of Technology

 The bottleneck of the maximum road volume in urban areas is the maximum capacity of the traffic flow on the intersection, which is coordinated with… (more)

Subjects/Keywords: Traffic flow; Traffic light controller; TLC; Predictive controller; Prediction model; Decentralized control; Model predictive control; green light optimal speed advise; GLOSA

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APA (6th Edition):

Glastra, T. (. (2020). Enabling GLOSA for on-street operating traffic light controllers. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:63a21739-a200-4375-8f19-641352e504b2

Chicago Manual of Style (16th Edition):

Glastra, Thom (author). “Enabling GLOSA for on-street operating traffic light controllers.” 2020. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:63a21739-a200-4375-8f19-641352e504b2.

MLA Handbook (7th Edition):

Glastra, Thom (author). “Enabling GLOSA for on-street operating traffic light controllers.” 2020. Web. 27 Nov 2020.

Vancouver:

Glastra T(. Enabling GLOSA for on-street operating traffic light controllers. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:63a21739-a200-4375-8f19-641352e504b2.

Council of Science Editors:

Glastra T(. Enabling GLOSA for on-street operating traffic light controllers. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:63a21739-a200-4375-8f19-641352e504b2


Delft University of Technology

14. GAO, Xinyu (author). Sensor Data Fusion of Lidar and Camera for Road User Detection.

Degree: 2018, Delft University of Technology

Object detection is one of the most important research topics in autonomous vehicles. The detection systems of autonomous vehicles nowadays are mostly image-based ones which… (more)

Subjects/Keywords: 3D object detection; Lidar; Camera; sensor fusion

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APA (6th Edition):

GAO, X. (. (2018). Sensor Data Fusion of Lidar and Camera for Road User Detection. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a

Chicago Manual of Style (16th Edition):

GAO, Xinyu (author). “Sensor Data Fusion of Lidar and Camera for Road User Detection.” 2018. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a.

MLA Handbook (7th Edition):

GAO, Xinyu (author). “Sensor Data Fusion of Lidar and Camera for Road User Detection.” 2018. Web. 27 Nov 2020.

Vancouver:

GAO X(. Sensor Data Fusion of Lidar and Camera for Road User Detection. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a.

Council of Science Editors:

GAO X(. Sensor Data Fusion of Lidar and Camera for Road User Detection. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:e310da67-98b2-4288-b656-15da36e3f12a


Delft University of Technology

15. Yu, Rui (author). Lane Change Intention Recognition Models Using Hidden Markov Models and Relevance Vector Machines.

Degree: 2019, Delft University of Technology

The development of intelligent vehicle and autonomous driving asked a higher requirement of ADAS on its functionality. Currently, ADAS systems are able to detect and… (more)

Subjects/Keywords: Intention Recognition; Lane Change; Hidden Markov Model; Relevance Vector Machine

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

APA (6th Edition):

Yu, R. (. (2019). Lane Change Intention Recognition Models Using Hidden Markov Models and Relevance Vector Machines. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a4ea860a-46d1-498b-9742-152e92f55ace

Chicago Manual of Style (16th Edition):

Yu, Rui (author). “Lane Change Intention Recognition Models Using Hidden Markov Models and Relevance Vector Machines.” 2019. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:a4ea860a-46d1-498b-9742-152e92f55ace.

MLA Handbook (7th Edition):

Yu, Rui (author). “Lane Change Intention Recognition Models Using Hidden Markov Models and Relevance Vector Machines.” 2019. Web. 27 Nov 2020.

Vancouver:

Yu R(. Lane Change Intention Recognition Models Using Hidden Markov Models and Relevance Vector Machines. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:a4ea860a-46d1-498b-9742-152e92f55ace.

Council of Science Editors:

Yu R(. Lane Change Intention Recognition Models Using Hidden Markov Models and Relevance Vector Machines. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:a4ea860a-46d1-498b-9742-152e92f55ace


Delft University of Technology

16. Jargot, Dominik (author). Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving.

Degree: 2019, Delft University of Technology

Nowadays, autonomous driving is a trending topic in the automotive field. One of the most crucial challenges of autonomous driving research is environment perception. Currently,… (more)

Subjects/Keywords: 3D object detection; Thesis; Intelligent Vehicles; Deep Learning; Machine Learning; Camera; Lidar

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

APA (6th Edition):

Jargot, D. (. (2019). Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6389d77c-007d-455f-8e84-10a4f9b57a9d

Chicago Manual of Style (16th Edition):

Jargot, Dominik (author). “Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving.” 2019. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:6389d77c-007d-455f-8e84-10a4f9b57a9d.

MLA Handbook (7th Edition):

Jargot, Dominik (author). “Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving.” 2019. Web. 27 Nov 2020.

Vancouver:

Jargot D(. Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:6389d77c-007d-455f-8e84-10a4f9b57a9d.

Council of Science Editors:

Jargot D(. Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:6389d77c-007d-455f-8e84-10a4f9b57a9d

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