- Are you inspired by the prospect of shaping the future of autonomous driving?
- Are you fascinated by automating the design of neural networks under hardware constraints?
- Are you excited to work on perception tasks using the next-generation automotive radar?
- Then apply for the PhD position on Neural Architecture Search for 4D Imaging Radar Perception Networks!
Autonomous driving is a key application of artificial intelligence generally, and vehicle perception specifically. Contemporary autonomous driving systems utilize various sensors, such as cameras, radar, and LiDAR. Recent developments in automotive radar technology have led to the emergence of a new class of sensors, 4D Imaging Radar. This technology can be a key enabler for Level 4 and Level 5 autonomy due to its additional vertical information, high density, and robustness.
Consequently, this requires the development of novel deep-learning methods that can process Imaging radar data on resource-constrained devices, and perform standard automotive perception tasks, such as object detection or segmentation. The design of those methods can be guided via hardware-aware neural architecture search (NAS).
This PhD project is designed to research various NAS methods and specialize them for Imaging Radar data under the constraints of a given hardware platform. To this end, it will be necessary to not only consider mathematical and technical details of NAS, but also the understanding of embedded systems, signal processing, and working principles of 4D Imaging radar technology.
More specifically, research tasks will include:
- Reviewing relevant literature from the neural architecture search, radar-based perception, and hardware-aware neural network design.
- Defining radar-specific search space of neural network design parameters constrained to a deployment platform.
- Developing efficient search strategies incorporating realistic, relevant, and feasible hardware-aware metrics.
- Collaborating on ongoing research projects that aim to implement radar-based perception methods for next-generation ADAS and autonomous driving.
An ideal candidate will combine technical expertise in deep learning and embedded systems. You are strongly interested in the automation of neural architecture design. Next, you have good programming experience and are passionate about artificial intelligence, computer science, and autonomous driving.
The candidate will be integrated into the Mobile Perception Systems
(MPS) lab within a newly forming Automated Vehicle Test Facility (AVTF). They will be a member of the LTP ROBUST
consortium funded by NWO and of the EAISI
institute at TU/e.