Table of Contents
Electric Scooter on the Line of Fire" >Electric Scooter on the Line of Fire
Roads and rails are equally reckless" >Roads and rails are equally reckless
Ensuring trains are safe and on the right track" >Ensuring trains are safe and on the right track
The road transport revolution has begun" >The road transport revolution has begun
With artificial intelligence, risk-free transportation could become a reality" >With artificial intelligence, risk-free transportation could become a reality
Home Technology peripherals AI Artificial Intelligence is the key to improving safety in the transportation industry!

Artificial Intelligence is the key to improving safety in the transportation industry!

Apr 12, 2023 am 10:43 AM
AI Smart transportation

Artificial Intelligence is the key to improving safety in the transportation industry!

Last week, e-scooter giant Lime announced plans to trial a new custom computer vision platform to detect dangerous behavior by users riding on sidewalks. Such safety mechanisms, capable of alerting cyclists to infractions or even slowing down, are much needed given a string of dangerous accidents that have cast aspersions on the popular mode of urban transport.

Artificial intelligence can play an important role not only in electric scooters. Fatal rail accidents occur with alarming frequency. And road traffic-related accidents remain the leading cause of premature death globally, especially among young people. Fortunately, solutions inspired by artificial intelligence and computer vision are emerging that are designed to improve safety across all modes of transportation—for pedestrians, cyclists, drivers, and passengers alike. Good news.

Electric Scooter on the Line of Fire

LimeVision, billed by its owner as the industry’s first artificial intelligence-enabled computer vision platform, is scheduled to launch It will be tested on nearly 400 electric scooters in Chicago and San Francisco next month, and in six cities by the end of the year. According to company president Joe Kraus, the camera-based technology underpinning LimeVision has the potential to outperform competing GPS platforms in other applications that improve scooter safety.

Such innovations are very welcome and may even be outdated for a mode of transportation that has recently been found to be more likely to cause accidents than motorcycles. According to a study conducted by the University of California, Los Angeles, there are 115 injuries for every 1 million e-scooter riders. Among motorcyclists, the figure dropped to 104 per million, compared with 15 for cyclists. Not only do scooters pose a safety hazard to riders, their proliferation on sidewalks also poses dire, potential dangers to the elderly, visually impaired and other vulnerable groups.

Roads and rails are equally reckless

As one of the newest transportation options on the block, it’s easy to blame scooters— — but overall, transportation could benefit from AI-driven safety upgrades. The risks of rail travel were on full display late last month when two fatal accidents involving Amtrak trains occurred within days of each other. The first occurred in Northern California, killing three people; the second occurred in Missouri, killing four people and seriously injuring about 150 others. Both accidents occurred at intersections without guardrails or lights, but implementing these safety measures can be extremely costly.

Road traffic is more harmful to human health. A recent United Nations report found that more than 1.3 million people die each year in road traffic crashes, making it the leading cause of premature death among people aged 5 to 29. According to a recent study, while road injuries and deaths have fallen slightly in rich countries over the past 30 years, this has been offset by a subsequent spike in rates in low- and middle-income countries (LMICs) - with 93% of deaths occurring. these improvements. As a result, the United Nations has pledged to halve this number by 2030.

Ensuring trains are safe and on the right track

Technology looks set to play an important role in achieving the goal of avoiding road and rail accidents, while human Intelligence is at the forefront of some of the most promising innovations. For example, the privately owned Brightline Railroad has proven itself to be the deadliest railroad in the United States, in part because its locomotives operate at speeds of 79 miles per hour in densely populated areas with populations unaccustomed to high-speed passenger rail; as a result, often Some people trespassed on the railway, and many people died.

Given that Brightline intends to expand its line to Orlando and beyond — and that installing fencing along the tracks could cost more than $200,000 per mile — another solution must be found. The company’s decision-makers believe they have succeeded by partnering with Remark Holdings, a technology and artificial intelligence company whose smart security platform is capable of detecting intruders and identifying orbital anomalies from a distance. This innovation should help Reduce accident rates.

The road transport revolution has begun

The road transport sector is also enjoying similar AI safety improvements. While most media headlines focus on how artificial intelligence will enable self-driving cars, technology companies are already targeting many low-hanging fruit. For example, machine vision can monitor the health and performance of vehicle hardware, optimize maintenance, and minimize accidents caused by mechanical failure. So-called "collaborative robots" can speed up the manufacturing process, while AI, 5G and thermal imaging technology can work together to detect potential threats and share information between different vehicles.

In addition, traffic management has already benefited greatly from artificial intelligence cameras installed at intersections, with 155,000 cameras expected to be installed by 2025. Meanwhile, Australian startup Acusensus launched the HeadsUp roadside camera network in 2019. The project was able to identify drivers' risky behavior, reducing mobile phone use by 80% and corresponding traffic accidents by 22%, and winning awards in the process. Given the recent passage of the US$1.2 trillion Infrastructure Investment and Jobs Act (IIJA), the time is ripe for an overhaul of road safety.

With artificial intelligence, risk-free transportation could become a reality

While a world without traffic accidents may seem like a pipe dream, Advances in technology could make it a viable reality in the foreseeable future. In fact, MIT research even speculates that AI could use historical data to predict future events with reasonable accuracy, thereby predicting accidents before they occur and allowing users to take appropriate actions to avoid them. With such incredible opportunities at our disposal, it’s time to fully incorporate artificial intelligence and make traffic-related injuries and deaths a thing of the past.

The above is the detailed content of Artificial Intelligence is the key to improving safety in the transportation industry!. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions 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 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

Seven Cool GenAI & LLM Technical Interview Questions 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

Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations 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

Five schools of machine learning you don't know about 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 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

SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc. 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

SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time 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

See all articles