Deep Learning-Powered IoT Solutions for Real-Time Environment Perception and Navigation in Autonomous Vehicles With NLP Features

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 187 - 204

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 for Risk Reduction in Vehicle Supply Chain Management

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 391 - 404

Published: May 1, 2025

The use of machine learning for customer profile, predictive analytics, and cluster analysis, AI-powered audience segmentation is revolutionizing campaigns to raise awareness car safety. By identifying target demographics, driving patterns, risk variables, this strategy guarantees highly customized marketing campaigns. AI can send safety messages by grouping audiences according concerns using behavioral modeling clustering algorithms. Proactive outreach made possible which forecasts engagement levels accident probability. improving precision marketing, technique that are seen the appropriate people at moment. Additionally, dynamic content adaption automatic campaign optimization AI-driven segmentation, maximizes impact. Through integration data real-time tracking, automated outreach, companies public drive meaningful change.

Language: Английский

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ML Tools for Safety in Automotive Financial Risk Management

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 405 - 422

Published: May 1, 2025

In the rapidly evolving field of automotive finance, managing risk is crucial for maintaining financial stability and security. Machine Learning (ML) tools have demonstrated significant potential in enhancing predictive capabilities management models, enabling more accurate forecasting, real-time monitoring, mitigation strategies. This study explores application an advanced ML method, specifically Deep Neural Networks (DNN), predicting risks industry. The DNN, with its ability to handle complex, non-linear relationships large datasets, integrated Automotive Risk Management Software (ARMS), tool designed dynamic assessment. By leveraging these tools, finance institutions can gain deep insights into market trends, customer behavior, risks, which helps optimizing decisions related credit scoring, loan defaults, asset depreciation.

Language: Английский

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NLP Voice Assistance for IoT Autonomous Vehicles Using ML Algorithms for Seamless Navigation

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 359 - 374

Published: May 1, 2025

The goal of this research project is to ascertain whether or not combining Internet Things (IoT) and Natural Language Processing (NLP) technology could improve the caliber voice-assisted navigation in self-driving cars. system under consideration utilizes machine learning techniques provide smooth an easy-to-understand voice-based interface for car administration. preprocessing step includes speech-to-text conversion, which process converting spoken commands into text additional analysis. preparatory processing another name stage. Utilizing time-series data analysis essential completing feature selection process. Finding important patterns that are necessary navigational judgments requires a study vehicle's sensor data, GPS, speed, ambient inputs. To find trends, required. Sequence models—more especially, Recurrent Neural Networks (RNNs) Long Short-Term Memory (LSTM) networks—are used during classification

Language: Английский

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AI-Based Economic Models for Evaluating Vehicle Safety Costs and Benefits

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 221 - 236

Published: May 1, 2025

This research focuses on artificial intelligent efficient models for the analysis of risks and asset values vehicles applying globalization, feature reduction, time series methods. The concerns increasing pressure to evaluate economic effects protective features in a constantly changing car environment. Normalization normalizes disparate data, creating common framework which cost benefit variables. Defined subspaces eliminate noise unnecessary data by outlining strength predominant thus enabling reduction computational load models. countless look at past present provide future long-standing trends safety paying as well consequences. given provides clear understanding evaluating cost-effectiveness proposed measures, including rates accident insurance cost, costs adoptive technologies.

Language: Английский

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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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