Thesis: Incremental Learning for Autonomous Driving Systems
1 month ago
At Semcon, we are seeking a highly skilled researcher to explore the feasibility and efficiency of implementing open world object detection in autonomous driving systems. The successful candidate will focus on the ability of open world object detection to handle edge cases and integrate incremental training mechanisms.
Key Responsibilities:- Investigate the performance of traditional object detection models in autonomous driving compared to open world object detection models when encountering edge cases.
- Develop strategies to ensure the robustness and reliability of an open world object detection system when faced with novel objects, such as unseen animals or unexpected obstacles.
- Optimize the computational and storage overhead introduced by incremental learning in open world object detection for real-time processing in an autonomous vehicle.
The ideal candidate will have a strong background in computer vision, machine learning, and autonomous driving systems. Experience with open world object detection and incremental learning is highly desirable.
We offer a dynamic and collaborative work environment, with opportunities for professional growth and development. If you are passionate about autonomous driving and open world object detection, we encourage you to apply.
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Incremental Learning for Autonomous Driving
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Gothenburg, Västra Götaland, Sweden Semcon Full timeJob Title: Thesis: Incremental Learning with Open World Object Detection Job Summary: As a researcher in autonomous driving, you will explore the feasibility and efficiency of implementing open world object detection in autonomous vehicles. Your focus will be on handling edge cases and integrating incremental training mechanisms. Research Questions: 1....
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Gothenburg, Västra Götaland, Sweden Semcon Full timeJob Description:Göteborg Apply by linking to the job portal below and tell us moreAutonomous driving systems rely heavily on accurate object detection to ensure safety and efficient navigation. While current systems are trained on vast datasets containing common obstacles and objects, they often struggle with edge cases—unexpected or rare objects that...
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