Poster: Multimodal Data Analytics and Machine Learning for Software-Defined Vehicles

The paradigm shift towards software-defined vehicles has been foreseen to give access to in-vehicular and external sensors, support intelligent applications and edge-based ML/AI methods, and provide new possibilities for intelligent traffic applications. The key challenges, however, include meaningfully combining, learning, and personalising this multimodal and multidimensional information. This poster highlights our ongoing work to define the vehicular data analysis landscape.