Technology peripherals
AI
Artificial intelligence, the beginning of thinking about autonomous driving
Artificial intelligence, the beginning of thinking about autonomous driving
The development of autonomous driving, as a technology closely related to everyone's food, clothing, housing and transportation, has developed rapidly in the past few years and has become the focus of attention. However, the realization of autonomous driving technology requires the support of many technologies, one of which is artificial intelligence technology.
01 Overview of Artificial Intelligence
Artificial Intelligence (AI) is Refers to the ability of a computer system to perform tasks similar to those required by human intelligence. It is a complex technology that learns by inputting large amounts of data into the algorithm, constantly adjusting and improving its own algorithm, thereby continuously optimizing its performance. It can be applied to a variety of fields, including natural language processing, image recognition, speech recognition, machine translation, autonomous driving, smart home, medical care, finance, energy and environment.
Artificial intelligence can be divided into two categories: weak artificial intelligence and strong artificial intelligence. Weak artificial intelligence (also known as narrow artificial intelligence) refers to artificial intelligence systems that can only show human-like intelligence in specific task areas. For example, speech recognition systems, autonomous driving systems, etc. Strong artificial intelligence (also called generalized artificial intelligence) refers to an artificial intelligence system that can show human-like intelligence in various task fields like humans. At present, strong artificial intelligence has not yet been realized and is still in the research and exploration stage.
The development of artificial intelligence technology mainly relies on technologies such as big data, machine learning, deep learning and natural language processing. By inputting large amounts of data into algorithms, artificial intelligence systems can continuously improve their performance and efficiency through self-learning and improvement. Deep learning technology is an algorithm that imitates the neural network structure of the human brain. It can simulate the way human vision and language are processed, thereby achieving automatic recognition and classification of images, sounds, text and other information.
Although artificial intelligence technology has made many achievements, there are still many challenges and obstacles, such as data privacy, algorithm opacity, ethical issues, security issues, etc. Therefore, the development of artificial intelligence technology needs to gradually solve these problems and ensure its safety, transparency, reliability and responsibility.
02 Artificial intelligence assists the development of autonomous driving
Autonomous driving technology is a complex technology involving multiple fields. Artificial intelligence technology is an important part of it. In autonomous driving, artificial intelligence is mainly responsible for realizing autonomous decision-making and intelligent perception. Among them, autonomous decision-making involves making the best decision based on various factors in various driving situations. These factors include road conditions, traffic conditions, weather conditions, the actions of pedestrians and other vehicles, and various other factors. Intelligent perception is mainly responsible for realizing the perception of the surrounding environment, including the acquisition and analysis of the position, speed, direction and other information of vehicles and pedestrians. This information will provide support for autonomous vehicles to make the best decisions and actions.
In autonomous driving technology, artificial intelligence technology mainly consists of deep learning, computer vision and natural language processing technologies.
Among them, deep learning is one of the important technologies in autonomous driving technology. Deep learning is a machine learning method that achieves various tasks by learning a large amount of data. In autonomous driving technology, deep learning technology is mainly used in image recognition, object recognition and behavior prediction. For example, deep learning technology can recognize different types of vehicles and pedestrians by learning from image and video data, and make the best decision based on information such as their location and speed.
In addition, computer vision technology is also an important part of autonomous driving technology. Computer vision technology is mainly used to analyze and process image and video data. In autonomous driving technology, computer vision technology is mainly used to realize the perception and recognition of the environment around the vehicle. For example, computer vision technology can enable the recognition and analysis of elements such as roads, lanes, road signs, and traffic lights, as well as the perception of the positions and movements of other vehicles and pedestrians.
Natural language processing technology is also an important part of autonomous driving technology. Natural language processing technology is mainly used to understand and analyze human language. In autonomous driving technology, natural language processing technology can be used to realize communication between the vehicle and the driver, such as the recognition and execution of voice instructions, and to realize natural interaction between the driver and the vehicle. The development of natural language processing technology is The intelligent upgrade of the smart cockpit provides the possibility.
In short, artificial intelligence technology plays an important role in autonomous driving technology. It is the core technology to achieve autonomous decision-making and intelligent perception. By using technologies such as deep learning, computer vision, and natural language processing, autonomous driving technology can perceive and identify the surrounding environment and make optimal decisions and actions.
03 Autonomous driving speeds up the development of artificial intelligence
The development of autonomous driving technology has a profound impact on the development of artificial intelligence technology. On the one hand, the rapid development of autonomous driving technology has promoted the development of artificial intelligence technology. In the application of autonomous driving technology, various types of sensors and devices collect a large amount of data, which can be used to train and optimize artificial intelligence algorithms. For example, by learning from a large amount of image and video data, accurate identification and behavior prediction of vehicles and pedestrians can be achieved, thereby making artificial intelligence technology more intelligent and advanced, and promoting the development of artificial intelligence technology.
On the other hand, the development of autonomous driving technology has also promoted further research and improvement of artificial intelligence technology. For example, in the research of autonomous driving technology, artificial intelligence technology needs to solve a series of problems such as how to perceive and identify the environment around the vehicle, how to make the best decisions and actions, and how to communicate with the driver and other vehicles. These problems require in-depth research and solution by artificial intelligence technology, thus promoting the development of artificial intelligence technology.
The development of autonomous driving technology will promote the further development of artificial intelligence technology. Autonomous driving technology can effectively improve the safety and convenience of transportation, and will have a profound impact on the transportation industry and related occupations. The development of autonomous driving is inseparable from the blessing of artificial intelligence technology. Through its application in autonomous driving technology, artificial intelligence technology can be more widely verified and applied, thus promoting the further development and optimization of artificial intelligence technology.
In short, autonomous driving technology will have a profound impact on the transportation industry and society as a whole. It will not only bring convenience and efficiency, but also bring new challenges and opportunities. For To promote the development of autonomous driving technology, it is necessary to continue to strengthen the research and development of artificial intelligence technology.
04 Prospects for the development of autonomous driving under artificial intelligence
Artificial intelligence has a profound impact on the development of autonomous driving, which is mainly reflected in the following Several aspects:
Improve the accuracy and reliability of autonomous driving technology
Artificial intelligence technology can improve autonomous driving Technical accuracy and reliability. For example, machine vision technology and deep learning technology can realize the perception and understanding of the environment around the vehicle, thereby improving the driving safety of the vehicle. In addition, artificial intelligence technology can predict the environment around the vehicle, thereby improving the driving efficiency and comfort of the vehicle.
Reduce the cost of autonomous driving technology
Artificial intelligence technology can reduce the cost of autonomous driving technology. Autonomous driving technology requires a large number of sensors, computer hardware and software and other equipment and resources, and artificial intelligence technology can realize the optimization and intelligent management of these equipment and resources through deep learning technology, thus reducing the cost of autonomous driving technology.
Accelerate the commercial application of autonomous driving technology
##Artificial intelligence technology can accelerate the commercial application of autonomous driving technology . Autonomous driving technology needs to face numerous laws and regulations, road standards, user habits and other issues, and artificial intelligence technology can help autonomous driving technology better adapt to market needs and user needs through the analysis and prediction of these issues. The commercial application of autonomous driving will also bring more problems:
#Brings new security and privacy issues
The commercial application of self-driving technology will also bring new security and privacy issues. For example, the sensors and computer systems of self-driving vehicles may be attacked, resulting in loss of control of the vehicle and safety issues. In addition, the sensors of autonomous vehicles may collect users’ personal information and location information, thus raising privacy issues.
Changing urban planning and road standards
The commercial application of autonomous driving technology will change urban planning and road standards . Autonomous vehicles require more complete road standards and traffic rules to control and manage vehicles. In addition, the use of autonomous vehicles will also affect urban traffic and traffic flow, requiring adjustments and optimization of urban planning and road standards.
Changing employment and human society
The commercial application of autonomous driving technology will change employment and human society. Autonomous driving technology can replace part of the work of human drivers, leading to unemployment problems and social changes. In addition, the commercial application of autonomous driving technology will also bring about new social issues and human behavior patterns, such as human trust and adaptability to autonomous driving technology.
Bring new technologies and industrial development
The commercial application of autonomous driving technology will bring new Technology and industrial development. For example, autonomous driving technology needs to face a variety of technical challenges and solutions, such as sensor technology, computer hardware and software technology, communication technology, etc. In addition, the commercial application of autonomous driving technology will also bring about new industrial chains and business models, such as the manufacturing and sales of autonomous vehicles, data collection and processing, Internet of Vehicles services, etc.
05 Conclusion
Autonomous driving technology is an important development direction of the future transportation industry, and artificial intelligence technology is the key technology to realize autonomous driving technology one. Artificial intelligence technology can improve the perception and understanding capabilities of autonomous vehicles, reduce the cost of autonomous driving technology, and accelerate the commercial application of autonomous driving technology.
However, the commercial application of autonomous driving technology still needs to face numerous technical, legal, road standards and user habits, etc. Therefore, various factors need to be comprehensively considered to promote autonomous driving. Development of technology. In the future development, autonomous driving technology will bring huge economic and social impacts, so policy guidance and social education need to be strengthened to achieve the sustainable development of autonomous driving technology and the progress of human society.
The above is the detailed content of Artificial intelligence, the beginning of thinking about autonomous driving. 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
1381
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
