Master projects/internships - Leuven | More than two weeks ago
Enhancing Autonomous Driving with Self-Supervised Scene Flow: Leveraging Affordable mmWave Radar for Precise 3D Perception
Topic Description
In the automotive industry, precise environment perception is crucial for autonomous driving and advanced driver-assistance systems (ADAS). Scene flow estimation involves determining the 3D motion vectors of all points in a scene, providing a detailed understanding of object movements and spatial relationships. It plays a vital role in understanding dynamic environments. Traditional approaches rely heavily on LiDAR or stereo cameras, which can be expensive and computationally demanding. Recent millimeter-wave (mmWave) radar technology advancements offer a cost-effective and robust alternative. mmWave radar offers several advantages for automotive sensing, including robustness to weather conditions, low cost, and compact size. This thesis explores self-supervised scene flow estimation using commercially available mmWave radar technology, complemented by camera data for depth completion, to enhance 3D perception in automotive applications. The successful completion of this project could significantly enhance the capabilities of automotive sensing systems, contributing to the advancement of autonomous driving technologies.
Type of work:
Required qualifications:
Type:
(the summer internship alone is not possible)
Responsible scientist(s):
Prof. Hichem Sahli (imec / VUB) hichem.sahli@imec.be
Dr. Seyed Hamed Javadi (imec) hamed.javadi@imec.be
Supervisor: Piet Wambacq (VUB)
References:
Type of Project: Combination of internship and thesis
Master's degree: Master of Engineering Science
Master program: Electromechanical engineering; Computer Science
Duration: 9 months
For more information or application, please contact Hichem Sahli (hichem.sahli@imec.be) and Hamed Javadi (hamed.javadi@imec.be)
Imec allowance will be provided for students studying at a non-Belgian university.