Guest Editorial Introduction to the Special Issue on Context Prediction of Autonomous Vehicles

The integration of advanced sensing, signal processing, deep learning, and edge computing into vehicles is enabling intelligent automated vehicles that can navigate autonomously in various environments. There are several exciting developments in new technologies that may contribute to the improvement of the robustness of autonomous vehicles and thus making them safer on the road. However, the development of suitable context prediction methodologies in order to provide proactive behavior for intelligent transportations remains a challenge. The reason is that future context information, hidden in the raw context traces left by users in the real world, is not immediately accessible to applications. Therefore, sophisticated context prediction approaches are required that could discover and mine patterns (e.g., of a driver’s behavior) from observed context history. The major challenge of a context prediction approach is in the prediction accuracy and prediction expressiveness. Neural networks along with deep-learning methods have shown noticeably better performance in comparison with previous methods regarding the accuracy of the outcomes. However, deep learning also issues more complexity and interpretability problems and, hence, arises serious challenges regarding the verifiability of these approaches. This Special Issue aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for context prediction of autonomous vehicles.