Visual Abductive Reasoning Meets Driving Hazard Prediction: Problem Formulation and Dataset

This paper addresses the problem of predicting hazards that drivers may encounter while driving a car. We formulate it as a task of anticipating impending accidents using a single input image captured by car dashcams. Unlike existing approaches to driving hazard prediction that rely on computational simulations or anomaly detection from videos, this study focuses on high-level inference from static images. The problem needs predicting and reasoning about future events based on uncertain observations, which falls under visual abductive reasoning. To enable research in this understudied area, a new dataset named the DHPR (Driving Hazard Prediction and Reasoning) dataset is created. The dataset consists of 15K dashcam images of street scenes, and each image is associated with a tuple containing car speed, a hypothesized hazard description, and visual entities present in the scene. These are annotated by human annotators, who identify risky scenes and provide descriptions of potential accidents that could occur a few seconds later. We present several baseline methods and evaluate their performance on our dataset, identifying remaining issues and discussing future directions. This study contributes to the field by introducing a novel problem formulation and dataset, enabling researchers to explore the potential of multi-modal AI for driving hazard prediction.Comment: Main Paper: 10 pages, Supplementary Materials: 25 page

Analysis of incident light angles on nano-grating structure for minimizing reflection losses in GaAs solar cells

Subwavelength grating (SWG) structures make an excellent alternative antireflective (AR) coating due to its capacity to reduce the reflection losses in GaAs solar cells. The SWG structures allow the gradual change in refractive index that confirms an excellent AR coating and the light trapping properties when compare with planar thin film structures. Finite-difference time domain (FDTD) method is used to simulate the reflection losses of the SWG structure in GaAs solar cells. The FDTD simulation results show that the slightly change of incident angle affect the reflection losses of all nano-grating structure. The simulation results also confirmed that the reflection loss of nano-grating structure maintained optimum within ~±5° of incident angle tolerance for the grating height over 300-nm for minimizing the reflection losses in GaAs solar cells