Abstract: Logistics and transportation companies have rapidly adopted electric vehicles (EVs) for service delivery over the past decade. The EV routing problem with time windows (EVRPTW) models commercial EV fleet operations. This paper proposes an…
Abstract: Technology has improved transportation. Data-driven approaches change the research direction for all control-based systems, such as transportation, robotics, IoT, and power systems. Recent transportation applications integrate data-driven applications with transportation systems. This paper reviews…
Abstract: An autonomous vehicle’s behavior prediction function predicts nearby vehicle states based on current and past environmental observations. This raises their risk awareness. In simple driving scenarios with short prediction horizons, conventional behavior prediction solutions…
Abstract: Intelligent driving systems must detect lane markings. It helps with vehicle positioning, forehead car detection, and road condition information to prevent lane departure. Extreme lighting, missing lane markings, and obstacles make lane marking detection…
Abstract: Deep Learning models complex phenomena. Deep Learning is also used in Intelligent Transportation Systems for vehicular perception and traffic analysis. In short-term traffic forecasting, Deep Learning’s ability to produce good results has created a…
Abstract: We detect semantic lines in natural scenes. Many methods treat semantic line detection as a special case of object detection. These methods neglect line characteristics and perform poorly. Lines are simpler geometrically than complex…
Abstract: Driver assistance and intelligent autonomous vehicles will benefit from automatic human activity recognition and prediction. In this article, we present a novel single image driver action recognition algorithm inspired by human perception that selectively…
Abstract: Electronic medical records are used to build adverse event prediction models for resource management. Many machine learning methods struggle with sparse, irregularly-sampled data. Carrying the last value forward or linear regression can interpolate missing…
Abstract: Deep networks estimate people densities in images for modern crowd counting methods. Few video sequences use temporal consistency, and those that do only apply weak smoothness constraints across frames. This paper proposes estimating people…
Abstract: Convolutional neural networks trained end-to-end to regress dense disparities estimate stereo image depth with unmatched accuracy. If large amounts of labelled samples are available for training, this is possible for most tasks. Real-world applications…
Abstract: Semantic segmentation has advanced rapidly with convolutional neural networks (CNNs). Despite their success, deep learning methods require large real-world datasets with pixel-level annotations. Many researchers use synthetic data with free annotations because pixel-level semantic…
Abstract: Quantifying deep neural classifier confidence is difficult but essential for safety-critical applications. This paper introduces the true class probability (TCP) as a model confidence target criterion. TCP outperforms maximum class probability (MCP) for confidence…
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