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Delft University of Technology

1. Bormans, Robbert (author). Deep segmentation of the drivable path of a self-driving vehicle using external data: Influence of domain shift factors and depth information.

Degree: 2018, Delft University of Technology

Robot Care Systems (RCS) is involved in the development of the WEpod, an autonomous shuttle which can transfer up to six people. Based on a predefined map of the environment, the shuttle is able to navigate through mixed traffic its perception sensors such as camera, radar and lidar sensors. This study is acquired in collaboration with RCS and focuses on two parts: assessing the influence of different factors on the domain shift and assessing the importance of depth information in the transformation of scene understanding from image space to top view. For the WEpod, or any self-driving vehicle to safely travel over the road and through traffic, it is important to understand road scenes that appear in our daily life. This scene understanding is the base for a successful and reliable future of autonomous vehicles. Deploying a Convolutional Neural Network (CNN) in order to execute the task of semantic segmentation is a typical approach to attain such understanding of the surroundings. However, when a CNN is trained on a certain source domain and then deployed on a different (target) domain, the network will often execute the task poorly. This is the result of differences between the source and target domain and is referred to as domain shift. Although it is a common problem, the factors that cause these differences are not yet fully explored. We filled this research gap with the investigation of ten different factors. To explore these factors, a base network was generated by a two-step fine-tuning procedure on an existing convolutional neural network (SegNet) which is pretrained on the CityScapes dataset (dataset for semantic segmentation). Fine-tuning on part of the RobotCar dataset (road scenery dataset recorded in Oxford, UK) is followed by a second fine-tuning step. The latter is done on part of the KITTI dataset (road scenery dataset recorded throughout Germany). Experiments are conducted in order to obtain the influence of each factor on a successful domain adaptation (i.e. negligible domain shift). The influence of factors on the domain shift based on semantic segmentation is assessed by comparing the result of every factor to the result on the base network. Results consist of the F1-measure and Jaccard index for drivable path segmentation and occupancy segmentation although the emphasis lies on the drivable path segmentation. Significant positive influence on the estimation of drivable path for the WEpod domain was obtained when the ground truth labels only consisted of two labels (i.e. drivable path and non-drivable path) instead of three classes. This performance gain is signed by an increase of 8 percent points for both the IoU and the F1 metric. Making all images intrinsically consistent, and thus removing all geometric differences between the camera sensors, resulted in a larger increase of performance metrics. Compared to the baseline, both the Jaccard index and F1 metric increased with 10 percent points. The training order is a main contributor for domain adaptation with an increase of… Advisors/Committee Members: Lindenbergh, Roderik (mentor), Gaisser, Floris (mentor), Kooij, Julian (graduation committee), Hanssen, Ramon (mentor), Delft University of Technology (degree granting institution).

Subjects/Keywords: Drivable Path; Domain Adaptation; Convolutional Neural Networks; Top View Transformation; Self-driving car

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

APA (6th Edition):

Bormans, R. (. (2018). Deep segmentation of the drivable path of a self-driving vehicle using external data: Influence of domain shift factors and depth information. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f3a713cc-f4f8-4e54-a8cb-136ce18ef849

Chicago Manual of Style (16th Edition):

Bormans, Robbert (author). “Deep segmentation of the drivable path of a self-driving vehicle using external data: Influence of domain shift factors and depth information.” 2018. Masters Thesis, Delft University of Technology. Accessed November 27, 2020. http://resolver.tudelft.nl/uuid:f3a713cc-f4f8-4e54-a8cb-136ce18ef849.

MLA Handbook (7th Edition):

Bormans, Robbert (author). “Deep segmentation of the drivable path of a self-driving vehicle using external data: Influence of domain shift factors and depth information.” 2018. Web. 27 Nov 2020.

Vancouver:

Bormans R(. Deep segmentation of the drivable path of a self-driving vehicle using external data: Influence of domain shift factors and depth information. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Nov 27]. Available from: http://resolver.tudelft.nl/uuid:f3a713cc-f4f8-4e54-a8cb-136ce18ef849.

Council of Science Editors:

Bormans R(. Deep segmentation of the drivable path of a self-driving vehicle using external data: Influence of domain shift factors and depth information. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:f3a713cc-f4f8-4e54-a8cb-136ce18ef849

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