Master Thesis Worker

4 weeks ago

Gothenburg, Sweden Aptiv Full time
Master of Science Thesis Worker at APTIV Gothenburg.

Title: Estimate trailers state and parameters using Machine Learning approach.

Brief Description: Trucks are fitted with side sensors to fulfill the "Blind Spot Information System" regulation which is detecting vulnerable road user, like pedestrian or bicycle, in the blind spot region.
However, this results in detections from the trailer appearing in the FOV of the radar when the truck makes a turn which leads to false positives.
It is critical to ensuring that object tracks are not created on ego vehicle trailers. The current algorithms make use of classical clustering techniques to create bounding boxes around the trailer and estimate the trailer parameters, but these systems are limited to few trailer types and can be unreliable.

Therefore, making use of machine learning to output better trailer estimates would be very useful, firstly, to validate the performance of the current trailer detection algorithm, and potentially, also replace the classical approach in favor for this newly developed Algorithm (using ML). The idea is to use ML for detecting the trailer (from detections) and estimating trailers state and parameters based on trailer classification for more accurate trailer measurements..

 It is required that you are on location in Gothenburg full time and period of the thesis is from January until June.

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