Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

UAV multispectral remote sensing for yellow rust mapping: opportunities and challenges

Wheat is threatened by various crop stresses in its life-cycle, where yellow rust is a severe disease significantly impacting wheat yield. This work aims to investigate the use of Unmanned Aerial Vehicle based multispectral remote sensing for winter wheat stress mapping caused by yellow rust disease. A simple unsupervised wheat yellow rust mapping framework is initially proposed by integrating Spectral Vegetation Indices generation, mutual information analysis and Otsu’s thresholding. A field experiment is carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where UAV multispectral images are collected at the diseased stage with visible symptoms. Experimental results on the labelled dataset initially show the effectiveness of the proposed unsupervised framework for yellow rust disease mapping. Limitations of the proposed algorithm and challenges of yellow rust detection for real-life applications are also discussed.</p