IoT-Connected Vehicle Networks Using Machine Learning and NLP for Enhanced Traffic Management and Autonomous Driving Efficiency

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 267 - 284

Published: May 1, 2025

Current development of IoT-connected vehicle networks is progressing at a very fast rate which has enabled traffic management and use autonomous cars. In this paper, research questions developed out the Sensor Data Fusion, Feature Importance Techniques, Deep Learning Algorithms are used to improve Traffic Flow Optimization also increasing efficiency Autonomous driving. collected from LiDAR sensor, GPS, video display frame other environment sensors integrated present uniform high accuracy data. By applying approach Random Forest SHAP (SHapley Additive exPlanations), such input-driving factors as speed, density vehicles, climate conditions that have greatest impact on model selected minimize computational load. case flow, Long Short-Term Memory (LSTM) consider temporal dependencies for predictive modelling decision making Convolutional Neural Networks (CNNs) applies features cameras.

Language: Английский

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Predictive Analytics in Automotive Insurance for Financial Risk Mitigation

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 423 - 436

Published: May 1, 2025

The paper investigates how predictive analytics affects automotive insurance when combined with Driving Behavior Scoring which uses XGBoost machine learning techniques. Through the analysis of telematics data features including acceleration rates and braking patterns as well speed pattern changes driving frequency model develops dynamic risk scores. algorithm to divide drivers into low-risk versus high-risk categories through behavioral assessment. A real dataset consisting more than 100,000 records was used train validate reached a 89% accuracy level. scoring system allows providers design individual premium costs while also helping them prevent financial losses conduct exact underwriting procedures. methodology increases claim predictions creating feedback systems promote safety. integration behavior-based proves successful for improving management together performance in operations.

Language: Английский

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Predictive Models for Recruiting Talent in Autonomous Vehicle Safety Development

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 483 - 500

Published: May 1, 2025

The rapid advancement of autonomous vehicle (AV) technology necessitates innovative approaches to recruiting talent capable ensuring safety in AV systems. This study explores the application advanced predictive modeling for identifying ideal candidates development. Utilizing a deep learning-based natural language processing (NLP) approach, specifically BERT (Bidirectional Encoder Representations from Transformers), we analyze candidate profiles, resumes, and technical assessments predict role suitability. implementation this model is achieved through TensorFlow, an open-source learning framework. By leveraging BERT's contextual understanding TensorFlow's scalable architecture, proposed solution evaluates not only on proficiency but also experience domain-specific knowledge. results demonstrate significant improvements recruitment efficiency accuracy, providing transformative approach building high-caliber teams safety.

Language: Английский

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Economic Implications of AI-Powered Safety Features in Vehicles

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 91 - 108

Published: May 1, 2025

The analysis utilizes a Cost-Benefit Analysis framework to measure the economic effect of AI-driven vehicle safety elements on reduction costs linked accidents together with changes in insurance premiums and market value automobiles. Our actual traffic enables us monetary advantages provided by adaptive cruise control automatic emergency braking lane departure warning systems. Evidence shows that medical expenses repair legal liabilities have decreased dramatically which causes total accident-related societal expenses. research establishes how adopting AI measures creates direct links would provide financial benefits for both providers their customers. demonstrates despite substantial initial implementation generates return investment eclipses all throughout long-lasting time periods. gives vital information automotive manufacturers.

Language: Английский

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Banking Sector Innovations Supporting Safer Automotive Technologies

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 33 - 46

Published: May 1, 2025

Cars with Advanced Driver Assistance Systems, AI-driven risk assessment in banking is transforming automotive financing. This transformation underway. Traditional credit scoring ignores driving behavior and car safety. Because these algorithms use static financial factors. Standardizing techniques proves this. Auto safety ratings, telemetry data, accident can help lenders evaluate loan applications. Increasing accuracy aim. AI-powered models enable Financial institutions sell ADAS cars reduced interest rates better approvals, reducing defaults by 25%. Fraud detection using artificial intelligence enhances vehicle financing transparency prevents false claims feature misrepresentation. possible because AI detect fraud. Artificial reduces lenders' risks encourages safer purchases. Better buying decisions are possible. Innovative speeds adoption road

Language: Английский

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Real-Time Financial and Traffic for Marketing Management Through Deep Learning for IoT-Integrated Autonomous Vehicle System

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 437 - 452

Published: May 1, 2025

This research aims at analyzing how IoT and Deep Learning can be used to improve real-time financial traffic control in autonomous vehicle systems with special reference the marketing optimization. The offered common framework for routing makes use of Dynamic Route Optimization using Reinforcement (DRL) overcoming advanced issues optimized. Apache Kafka is efficient data streaming so as enhance interoperation Internet things sensors automobile where TensorFlow an perfect platform deep learning model execution. methodology also places significant emphasis on minimizing response time achieving capability capacity supporting large-scale environments. Using metrics such accuracy, latency reduction, reduced amount fuel consumed, we show efficiency DRL-based approach rather than heuristic- machine learning-based approaches. shows a revolution operation applications systems.

Language: Английский

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Leveraging Neural Networks for Consumer Trust in Safer Vehicles

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 285 - 304

Published: May 1, 2025

Consumer trust plays a pivotal role in the adoption of AI-enhanced vehicle safety systems. This study explores application neural networks, developed using TensorFlow, to analyze consumer sentiment and predict levels response features. By examining dataset 50,000 online reviews surveys, model identified key factors influencing trust, including transparency AI operations demonstrable reliability. Results show that vehicles emphasizing user-friendly interfaces clear benefits achieved 25% higher rating. Strategic insights for automotive manufacturers focus on building through AI-driven personalization performance assurance.

Language: Английский

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Deep Learning Tools for Strategic Planning in Vehicle Safety Standards

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 173 - 186

Published: May 1, 2025

The transformed strategic planning in automobile safety standards, with Convolutional Neural Networks (CNNs) becoming a potent instrument for examining crash trends and real-time driving data. Risk analysis predictive evaluations are made possible by CNNs, which well-known their capacity to extract features from image sensor-based inputs. In order provide an automated method of evaluation, this research investigates the use CNNs test simulations, accident detection, vehicle behavior monitoring. Manufacturers government agencies can improve procedures, enhance design, reduce rates utilizing CNN-based models. Proactive risk mitigation tactics fostered method's ability analyze road conditions driver real time. This assesses how well optimize facilitate data-driven decision-making, guarantee adherence changing laws. results demonstrate boosting intelligent transportation systems car frameworks.

Language: Английский

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Deep Learning for Real-Time Traffic Analysis and Decision-Making in IoT-Connected Autonomous Vehicles

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 61 - 74

Published: May 1, 2025

The current paper proposes a novel approach to real time traffic analysis and decision making of IoT connected autonomous car through deep learning. proposed methodology consists three main stages: namely; preprocessing, feature selection, classification. For data collected by multiple sensors cameras are analyzed using sliding windows temporal smoothing techniques boost the identification important time-dependent patterns. During selection stage in method, domain-specific features used employed select relevant such as vehicle speed, density, road conditions etc., so that model incorporates only inputs influence most. classification, recurrent neural networks include long short-term memory gated units learn characteristics behavior.

Language: Английский

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Intelligent Deep Learning Architectures for Real-Time Traffic Analysis and Decision Support in IoT-Enabled Autonomous Vehicles

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 143 - 158

Published: May 1, 2025

IoT self-driven cars have become the order of day in today's societies and as such self-driving smart traffic system is required. In this research paper, a strong theoretical framework that can use deep learning architectures to overcome difficulties video/image analysis has been described. The methodology incorporates more sophisticated processing techniques for data bid enhance quality inputs by formulating both noise normalization. Spatial content features are obtained with Convolutional Neural Networks (CNN) while temporal using Recurrent (RNN). Integrating CNN RNN structure achieve comprehensive spatiotemporal capability identifying anomaly, object categorizing, well trajectory forecasting. Thus, approach allows proper scaling flexibility when applied different conditions. proposed also edge computing real time deployment enhancing low latency decision support.

Language: Английский

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