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?