Uncertainty-Encoded Multi-Modal Fusion for Robust Object Detection in Autonomous Driving

Multi-modal fusion has shown initial promising results for object detection of autonomous driving perception. However, many existing fusion schemes do not consider the quality of each fusion input and may suffer from adverse conditions on one or more sensors. While predictive uncertainty has been applied to characterize single-modal object...

Towards Efficient Personalized Driver Behavior Modeling with Machine Unlearning

Driver Behavior Modeling (DBM) aims to predict and model human driving behaviors, which is typically incorporated into the Advanced Driver Assistance System to enhance transportation safety and improve driving experience. Inverse reinforcement learning (IRL) is a prevailing DBM technique with the goal of modeling the driving policy by...

Design challenges of direct-drive permanent magnet superconducting wind turbine generators

In recent years, permanent magnet superconducting (PMSC) generators have become a candidate for applying superconducting (SC) generators in large direct-drive wind turbines. This configuration keeps the SC armature winding and its cooling system stationary and eliminates rotational cooling couplings. However, the low excitation by permanent...

Matching images and text with multi-modal tensor fusion and re-ranking

A major challenge in matching images and text is that they have intrinsically different data distributions and feature representations. Most existing approaches are based either on embedding or classification, the first one mapping image and text instances into a common embedding space for distance measuring, and the second one regarding...