Evaluating Economic Benefits of AI-Powered Crash Prevention Technologies

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 237 - 250

Опубликована: Май 1, 2025

Economic impact of AI intervention in crash prevention systems through the use computer simulations using digital twins done with Siemens NX and Simcenter. The research shows how technology can virtually explore several angles fine-tune its algorithms, as well estimate practical risks cost factors. Trial outcomes indicate an overall collision rate reduction by 40%, 30% lower medical expense, 25% vehicle repair comparison to conventional AI-based approaches separately. Further, this approach retained 85% prediction accuracy besides cutting down false positive 15% hence, increasing system credibility. effectiveness Digital Twins for scenario testing calculation is underlined, thus potential proposed future development scalable. It ascertained that simulation-based assessments offer a stable paradigm comparing AI-driven safety features automobiles hence earning better road economic impacts.

Язык: Английский

---

Advanced Deep Learning for IoT Sensor Data Processing in Autonomous Vehicle Navigation Systems

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 1 - 14

Опубликована: Май 1, 2025

Enhanced autonomous vehicle navigation systems enabled by IoT have been availing high-speed real-time data from sensors for decision making process. This paper explores advanced deep learning approaches to improve the processing of IoT-based sensor data, focusing on three key stages: which includes: preprocessing, feature selection and classification. In order effectively reduce noise raw input, before analysis is made in next phase, some preprocessing techniques include using an outlier detection method provide better input removing values getting a cleaner snapshot set. case selection, we use autoencoder-based that minimize determined relevant features enhancing model performance. Last but not least, CNNs are used classification since latter demonstrates capability recognizing spatial patterns across coming various especially context obstacle environment perception.

Язык: Английский

---

Machine Learning in Understanding Public Perceptions and Expectations in Accuracy of Automotive Safety

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 375 - 390

Опубликована: Май 1, 2025

This work focuses on the application of ML method in conceiving people's perception and expectations over efficacy automotive safety systems. Conducted with topological data analysis, research applies Sentiment Analysis BERT Apache Spark NLP Libraries to process big textual from surveys, social media, online reviews. The kind preprocessing involves Text Vectorization using Tokenizer maintain context information. BF MF are applied TF-IDF (Term Frequency-Inverse Document Frequency) identify leading terms motivating or discouraging public activity. various sentiments accurately categorized a BERT-Based Classifier high reliable results showing positivity, negativity, neutrality. system uses analyse real time it across large sets. approach is therefore useful for gaining an understanding concerns.

Язык: Английский

---

Machine Learning for Enhancing Workforce Safety in Automotive Manufacturing

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 345 - 358

Опубликована: Май 1, 2025

Predictive analytics is a powerful machine learning tool for enhancing worker safety in the auto manufacturing sector by proactively identifying hazards and preventing accidents. This work uses supervised models, such as random forests gradient boosting, to examine sensor data, operating logs, historical accident reports identify high-risk regions harmful trends. By using risk score systems, model assigns ratings specific behaviors regions, allowing preventative interventions. maintenance algorithms also assess machinery's state, reducing potentially dangerous equipment failures. The inclusion of real-time assessment dashboards ensures that supervisors receive automatic alerts, enabling timely corrective action. strategy optimizes workplace manual oversight improving decision-making with AI-driven insights. continuously from new predictive flexible dynamic approach minimization, regulatory compliance, workforce protection.

Язык: Английский

---

Predictive Economic Modeling for Adoption of Safety Features in Vehicles

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 453 - 468

Опубликована: Май 1, 2025

This research applies the Discrete Choice Models (DCM) in developing a predictive economic model on incorporation of safety features cars. In this way, applying innovative machine learning methods and modeling, we estimate people's judgement about price cars, their income, number family members, potential advantages technologies. is developed Python with practices SciPy Statsmodels to determine probability implementing complicated technologies, including automatic emergency braking lane assist. Price has been found be inversely proportional adoption, higher income earners bigger families are more likely use vehicles advanced features. About effectiveness model, following evaluation parameters presented accuracy 80%, F1-score 0.75. The insights obtained from particularly useful vehicle manufacturers policymakers targeting enhance usage hence improving customer advancing technology.

Язык: Английский

---