Transportation News and Research

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Vehiclectron: Monovision Sensor-Based 3D Vehicle Dimension Estimation

Researchers introduce Vehiclectron, a novel approach for precise 3D vehicle dimension estimation using monovision sensors and road geometry. This cost-effective solution utilizes object detection and core vectors to accurately estimate vehicle dimensions, offering potential applications in intelligent transportation systems and traffic flow management.

1 Sep 2023

Blockchain-Powered Traceability for Safer Grain and Oil Food Supply Chains

Researchers introduce a blockchain-based traceability model fortified with machine learning to ensure reliability in the intricate grain and oil food supply chain. By combining distributed ledger technology, IoT, and machine learning, the model enhances transparency, data accuracy, and recovery mechanisms, promising a safer and more transparent future for the grain and oil industry.

30 Aug 2023

Neural Networks Drive Robotic Cars to Victory in Autonomous Racing

Researchers present an AI-driven solution for autonomous cars, leveraging neural networks and computer vision algorithms to achieve successful autonomous driving in a simulated environment and real-world competition, marking a significant step toward safer and efficient self-driving technology.

30 Aug 2023

Transforming Food Supply Chains with Digital Twin Technology and Real-Time Strategies

Researchers have introduced a pioneering methodology utilizing digital twin technology to revolutionize real-time planning, monitoring, and control within medium-scale food processing companies. Published in Technological Forecasting and Social Change, the study highlights how this innovative approach can optimize food supply chains, enhance resource allocation, mitigate risks, and improve customer satisfaction.

28 Aug 2023

Revolutionizing Deep Learning Energy Measurement with FECoM

‌Researchers have introduced the Fine-grained Energy Consumption Meter (FECoM) framework to tackle the energy consumption challenges of Deep Learning (DL) models. This novel approach provides precise method-level energy measurement, offering a granular view of energy consumption and enabling energy-efficient development practices in various domains.

27 Aug 2023

Lens Flare Removal Breakthrough: Enhancing Object Detection in Autonomous Driving

Researchers have introduced a groundbreaking solution, the Class Attention Map-Based Flare Removal Network (CAM-FRN), to tackle the challenge of lens flare artifacts in autonomous driving scenarios. This innovative approach leverages computer vision and artificial intelligence technologies to accurately detect and remove lens flare, significantly improving object detection and semantic segmentation accuracy.

27 Aug 2023

Analog In-Memory Computing: A Breakthrough for Efficient AI Processing

Researchers have unveiled an innovative solution to the energy efficiency challenges posed by high-parameter AI models. Through analog in-memory computing (analog-AI), they developed a chip boasting 35 million memory devices, showcasing exceptional performance of up to 12.4 tera-operations per second per watt (TOPS/W). This breakthrough combines parallel matrix computations with memory arrays, presenting a transformative approach for efficient AI processing with promising implications for diverse applications.

25 Aug 2023

Revolutionizing Renewable Energy Forecasting with Graph Patch Informer

Researchers introduce the Graph Patch Informer (GPI) as a novel approach for accurate renewable energy forecasting (REF). Combining self-attention, graph attention networks (GATs), and self-supervised pre-training, GPI outperforms existing models and addresses challenges in long-term modeling, missing data, and spatial correlations. The model's effectiveness is demonstrated across various REF tasks, offering a promising solution for stable power systems and advancing renewable energy integration.

16 Aug 2023

Transforming Construction and Communities: AI and ML for Sustainable Development

Researchers examine the multifaceted applications of artificial intelligence (AI) and machine learning (ML) in revolutionizing construction processes and fostering sustainable communities. Covering the entire architecture, engineering, construction, and operations (AECO) domain, the study categorizes and explores existing and emerging roles of AI and ML in indoor and outdoor sustainability enhancements, construction lifecycles, and innovative integration with blockchain, digital twins, and robotics.

15 Aug 2023

Prediction and Planning in AI-Powered Automated Driving: A Comprehensive Review

This article delves into the transformational potential of automated driving (AD) systems on transportation, focusing on the integration of prediction and planning. While traditionally treated as separate tasks, recent insights advocate for an integrated approach to anticipate responses of other traffic participants. The review extensively covers cutting-edge deep learning models for prediction, planning, and their integration, highlighting strengths, limitations, and implications.

15 Aug 2023

Transforming Energy-Efficient Routes: Deep Reinforcement Learning for Electric Vehicle Routing Optimization

Researchers have introduced a transformative approach utilizing deep reinforcement learning (DRL) and a transformer-based policy network to optimize energy-efficient routes for electric logistic vehicles. By addressing the Energy-Minimization Electric Vehicle Routing Problem (EM-EVRP), this study aimed to reduce operating expenses for electric fleets while accommodating factors like vehicle dynamics, road features, and charging losses.

13 Aug 2023

Boosting Energy Efficiency in 6G Networks with Intelligent Reflecting Surfaces

Researchers delve into the transformative potential of Intelligent Reflecting Surfaces (IRSs) within 6G wireless networks, presenting a model that enhances communication efficiency and energy conservation. A case study involving smart ocean transportation illustrates the advantages of using IRSs, offering real-world insights into their practical implementation for Industry 5.0 applications.

10 Aug 2023

Intelligible Forecasting of Transportation Capacity Risk in Rail Transit Using Bayesian Networks

Researchers introduce a novel method for predicting transportation capacity risk in rail transit systems. By leveraging a linear Gaussian Bayesian network within a three-tier simulation model, the approach captures network, train flow, and passenger flow dynamics for accurate risk assessment. The proposed model, validated through a case study on Chongqing Rail Transit, demonstrates superior predictive capabilities compared to benchmark methods.

8 Aug 2023

Smart Detection of Natural Gas Pipeline Defects with Enhanced Pollination Algorithm

Researchers introduce an innovative solution for intelligent identification of natural gas pipeline defects. By enhancing the Flower Pollination Algorithm (FPA) with adaptive adjustments and Gaussian mutation, the Improved Flower Pollination Algorithm (IFPA) optimizes input weights for the Extreme Learning Machine (ELM). IFPA-ELM achieves impressive defect recognition rates of 97% and 96%, surpassing benchmarks and showcasing potential for advanced pipeline defect diagnosis.

8 Aug 2023

Hybrid Quantum Machine Learning for Cars: Navigating Natural Disasters Safely

This research paper introduces a novel approach using supervised hybrid quantum machine learning to improve emergency evacuation strategies for cars during natural disasters like earthquakes. The proposed method combines quantum and classical machine learning techniques, demonstrating superior accuracy and efficiency compared to conventional algorithms, and holds promise for real-world applications in dynamic environments.

2 Aug 2023

Machine Learning Uncovers Promising miRNA Biomarkers

Machine learning models identify miRNA biomarkers with potential clinical significance, shedding light on the complex landscape of cancer. The study reveals the relevance of specific miRNAs in cancer classification and highlights their potential as diagnostic and classification biomarkers.

20 Jul 2023

Revolutionizing Smart Transportation: Enhancing Incident Detection and Traffic Management with Hybrid Deep Learning

Researchers propose the Hybrid Deep Learning-based Automated Incident Detection and Management (HDL-AIDM) system, utilizing intelligent algorithms and deep learning techniques to enhance incident detection accuracy and optimize traffic management in smart transportation systems. The system combines the power of deep learning with data augmentation using Generative Adversarial Networks (GANs) and introduces an intelligent traffic management algorithm that dynamically adjusts traffic flow based on real-time incident detection data.

14 Jul 2023

Eyes on the Road: Detecting Driver Distraction in Australian Naturalistic Driving Study using Deep Learning

Researchers present a deep learning framework using pre-trained models and transfer learning to automate distraction detection in Australian Naturalistic Driving Study (ANDS) video data. By analyzing spatial and temporal correlations in the videos, the framework achieved promising results in identifying distractions from face and dashboard cameras. Further improvements and future work include expanding the training dataset and exploring approaches for robust distraction detection.

11 Jul 2023

Powering Superconductivity with AI: Unlocking Potential and Overcoming Challenges

This article reviews the transformative impact of artificial intelligence (AI) techniques such as deep learning and machine learning in the field of superconductivity. From condition monitoring and design optimization to intelligent modeling and estimation, AI offers innovative solutions to overcome challenges, accelerate commercialization, and unlock new opportunities in the realm of superconducting technologies and materials.

10 Jul 2023

Sky High Insights: Estimating Building Heights with DLIPHE Algorithm and Google Street View

Researchers propose DLIPHE, a novel algorithm that combines deep learning and image processing, to estimate building heights using static Google Street View images. The algorithm employs semantic segmentation and advanced techniques to identify buildings and extract their contours, enabling real-time and automatic height estimation for aerial devices. The study demonstrates promising results, highlighting the potential for DLIPHE to enhance communication paths for unmanned aerial vehicles (UAVs) and electric vertical take-off and landing aircraft (eVTOLs) in future urban networks.