Budapesti Műszaki és Gazdaságtudományi Egyetem - Villamosmérnöki és Informatikai Kar

Automatizálási és Alkalmazott Informatikai Tanszék

One of the most important engineering tasks in the development of autonomous vehicles is to create a high-quality representation of the vehicle's real-world environment, as it is necessary for the robot to be able to make ideal decisions and navigate without external assistance. Although some details of the implementation may depend on the specific functionality, many general steps can be identified when considering such a solution. Such task is the detection, tracking and fusion of surrounding objects in time and space. These objects, which in real life often have complex shapes, can be described by simple geometric shapes, avoiding unnecessary overcomplication of algorithms and helping to achieve optimal runtimes. A common general shape is a rectangle, which can represent a parking space or even another vehicle.

It may also require the use of some prior knowledge, as adjacent parking spaces are usually parallel and oriented in the same direction. This can be used to further refine the detections, which are inherently noisy and imprecise. The validation of this heuristic can be done using a classic approach in two steps, first by clustering the objects that belong together (DBSCAN, K-means, mean shift, etc.) and then by aligning the resulting clusters together. However, with the appropriate training data, these two steps can be performed simultaneously using neural networks, substituting complex geometric algorithms, and allowing a higher level of generalization.

The student's task is to solve the clustering and alignment steps using classic methods for rectangle input: find the ideal clustering algorithm, implement it, and then align the rectangles in a cluster along a chosen heuristic, thus achieving a better-quality output. It is also responsibility of the student to evaluate the implementation focusing on accuracy and runtime (runtime as a function of input size/complexity).

The student will be assisted by the Continental AI Development Center.

If you are interested in the topic, be sure to contact Dávid Sik by email before applying, indicating the selected topic, training level, major and the planned project subject.

Külső partner: Continental Autonomous Mobility Hungary

Maximális létszám: 1 fő

Sik Dávid

Tanársegéd

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