Perception-driven machine learning for AD/ADAS systems: Developing benchmarks for camera- and lidar-related perception?
Next-generation radar: How to include intelligent radars into AV sensor suits?
Automotive camera technology and computer vision algorithms: How to turn cameras into primary sensors for object recognition and classification, localization, decision making, trajectory planning, and vehicle control?
Open and modular verification and validation: How to manage rapidly changing test requirements?
Sensor fusion: How dot properly weight sensors at run-time?
Perception stress testing: How to automatically identify where perception is brittle using unlabelled data?
Vision geometry and deep neural networks: How to verify deep learning detection with vision geometric principles?
Reinforcement learning for vehicular path planning: How to make use of deep learning for Avs?
Full stack software suites for AI: how to develop hardware-agnostic and scalable solutions?
Self-supervised learning for Avs: How to collect and label data at scale?
Data processing and AI: How to develop software architectures and overcome hardware challenges?
Interpretable learning for Avs: How to use visual explanations that causally influence CNN output?
Sensor selection, design and innovation: Which are the latest technologies?
Ensuring safety in challenging weather & vision environments
The software-defined car: What is the role of SDV in ADAS?