Technology peripherals
AI
How artificial intelligence, edge computing, IoT and the cloud are reshaping fleet management
How artificial intelligence, edge computing, IoT and the cloud are reshaping fleet management
Utilize a distributed computing environment that optimizes data exchange and data storage to save bandwidth for a fast data experience.
The advantages of connected cars may become the new standard when it comes to fleet management, especially as businesses look to modernize their vehicles. In fact, 86% of connected fleet operators say their investment in connected fleet technology has achieved a significant return on investment within one year through reduced operating costs.
Connected fleets use advanced telematics technology to provide additional advantages for the management and maintenance of vehicles. Another study showed a 13% reduction in fuel costs while improving preventive maintenance. It also showed a 40% reduction in emergency braking, suggesting that changing driving habits can both help extend the life of components and improve driver safety.
A large amount of data is difficult to handle
Fleets, insurance companies and after-sales maintenance companies are eager to utilize more intelligent telematics data. However, the amount of data generated continues to grow. As a result, these businesses have more data than ever to help make informed business decisions. Dealing with so much data creates new challenges to capture, digest and analyze all the information in a cost-effective way.
To generate the right insights, data must be tracked, managed, cleaned, protected and enriched throughout the process to make it truly effective and useful. As a result, those with fleets of vehicles are looking for new solutions to process and understand this data.
Embedded system technology has become the norm
Traditional telematics systems rely on embedded systems that are designed to access, collect, analyze (onboard) and control data from electronic devices to solve a series of problems. Embedded systems are already widely used, especially in home appliances, and the trend of using this technology to analyze vehicle data is growing.
Existing solutions on the market take advantage of the low latency of 5G. Using AI and GPU acceleration on AWS Wavelength or Azure Edge Zone, automotive OEMs can offload automotive processors to the cloud where feasible. This approach enables traffic between 5G devices and content or application servers hosted in the wavelength region to bypass the Internet, reducing variability and content loss.
To ensure optimal accuracy and richness of the data set and maximize usability, sensors embedded in the vehicle are used to collect the data and transmit it wirelessly between the vehicle and a central cloud agency, All of this is done in near real time. With a growing number of real-time oriented use cases such as roadside assistance, ADAS, active driver scoring and vehicle scoring reporting, low latency and high throughput are required for fleets, insurance companies and other businesses leveraging data become more and more important. Although 5G solves this problem to a large extent, the cost of transmitting this data to the cloud is still prohibitive. To maximize edge processing efficiency, advanced embedded computing capabilities must be identified within the car.
The rise of vehicle-to-cloud communications
To improve bandwidth efficiency and mitigate latency issues, it is best to do critical data processing at the edge (within the vehicle) and only share information relevant to the event to the cloud. In-vehicle edge computing is critical to ensuring connected vehicles can operate at scale, as applications and data are closer to the source, providing faster turnaround times and significantly improving system performance.
Agile technological progress has enabled effective and efficient communication between automotive embedded systems and sensors and cloud servers in the vehicle. Leveraging a distributed computing environment that optimizes data exchange and data storage, Automotive IoT improves response times and saves bandwidth for a fast data experience. Integrating this architecture with cloud-based platforms further helps in creating a robust end-to-end communication system for cost-effective business decisions and efficient operations. Overall, edge/cloud and embedded intelligence connect edge devices (sensors embedded in vehicles) to IT infrastructure, making way for a range of new user-centric applications based on real-world environments.
This technology has a wide range of applications across verticals, and OEMs can benefit by leveraging the insights gained from it. The most obvious use cases are aftermarket and vehicle maintenance, where efficient algorithms can analyze the health of a vehicle in near real-time to suggest remedial measures for impending vehicle failures in vehicle assets such as engine, oil, battery, tires, etc. Since most diagnostic work is performed on the fly, fleets can use this data to enable maintenance teams to maintain vehicles in a more efficient manner.
Additionally, insurance and extended warranties can benefit by providing proactive driver behavior analysis so that training modules tailored to individual driver needs can be created based on actual driving history and analysis. For fleets, proactively monitoring vehicle and driver ratings can lower a fleet operator’s TCO (total cost of ownership) by reducing losses due to theft and negligence while providing proactive training for drivers.
Powering the future of fleet management
AI analytics leveraging the Internet of Things, edge computing and the cloud are rapidly changing the way fleet management is performed, making it more efficient and effective than ever before. AI’s ability to analyze large amounts of information from telematics devices provides managers with valuable information to improve fleet efficiency, reduce costs and optimize productivity. The way fleets are managed is being changed by the intervention of artificial intelligence, covering all aspects from real-time analysis to driver safety management.
Artificial intelligence can increase the number of data sets OEMs process by collecting them in the cloud, thereby improving their predictive capabilities. This means that future self-driving cars will be safer and easier to use, with more precise routes and better real-time vehicle diagnosis.
The above is the detailed content of How artificial intelligence, edge computing, IoT and the cloud are reshaping fleet management. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
1379
52
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions
Jun 28, 2024 am 03:51 AM
This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP
Context-augmented AI coding assistant using Rag and Sem-Rag
Jun 10, 2024 am 11:08 AM
Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application
Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations
Jun 11, 2024 pm 03:57 PM
Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it
Seven Cool GenAI & LLM Technical Interview Questions
Jun 07, 2024 am 10:06 AM
To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S
SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc.
Aug 01, 2024 pm 09:40 PM
According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
