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Autonomous Trucks Market Projected to Reach $6.9 Billion by 2028, Reveals Latest BCC Research Study

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BOSTON, Feb. 19, 2024 /PRNewswire/ — Autonomous trucks are vehicles that can drive themselves without human intervention, using sensors, cameras, and artificial intelligence. They offer many benefits such as improved safety, efficiency, and cost savings for the global transportation industry.

Boston: “According to the latest BCC Research study, the demand for Autonomous Trucks: Global Markets is estimated to increase from $3.3 billion in 2023 to reach $6.9 billion by 2028, at a compound annual growth rate (CAGR) of 16.2% from 2023 through 2028.”

This comprehensive report offers a detailed overview of the global autonomous trucks market, leveraging 2022 as a baseline year and projecting estimated market data for the period spanning 2023 to 2028. Revenue forecasts during this timeframe are intricately segmented across various parameters including components, autonomy levels, propulsion types, truck categories, end-use applications, and regional considerations. By delving into major market trends and challenges, the report sheds light on the dynamic landscape that shapes the industry. Furthermore, it elucidates current market dynamics while providing in-depth profiles of key players and a nuanced understanding of their strategies aimed at bolstering their market presence. The report not only estimates the market size for autonomous trucks in 2023 but also offers comprehensive projections, painting a vivid picture of the expected market trajectory up to the year 2028. With its thorough analysis, the report serves as a valuable resource for stakeholders seeking insights into the rapidly evolving landscape of autonomous truck technology and its global market dynamics.

The integration of autonomous trucks is poised to revolutionize existing truck allocation structures, ushering in a new era of logistics models that facilitate both horizontal and vertical integration. This transformative shift holds the potential to address a multitude of challenges faced by shipping companies. Concurrently, manufacturers are actively engaged in developing low-emission Autonomous Vehicles (AVs) aimed at enhancing supply chain efficiency and mitigating transportation-related emissions within the freight transportation sector. Noteworthy achievements in this realm include TuSimple, Inc. receiving the EPA’s SmartWay High Performer award in 2021, a recognition reserved for the top 10% of fleets exhibiting the quietest CO2 emissions. In a regulatory context, the National Highway Traffic Safety Administration (NHTSA) took a pivotal step in April 2023 by amending reporting requirements for AV crashes. This amendment is anticipated to yield more accurate accident reports, furnishing crucial data to support future regulatory changes and further advancing the responsible integration of autonomous technologies into transportation systems.

Explore the comprehensive insights and strategic implications of this groundbreaking research. Click here to Learn More.

Key Drivers of Autonomous Trucks: Global Markets

Growing Emphasis on Enhanced Road Safety and Traffic Management: The advent of autonomous trucks heralds a significant paradigm shift towards heightened road safety and more efficient traffic control. Leveraging cutting-edge technologies such as sensors, cameras, and artificial intelligence, these vehicles have the potential to substantially mitigate human errors, thereby contributing to a safer road environment. Additionally, their ability to communicate with both fellow autonomous trucks and the infrastructure allows for optimized traffic flow, minimizing congestion and enhancing overall road system efficiency.

Reduced Emissions and Enhanced Fuel Efficiency in the Realm of Autonomous Trucks: Autonomous trucks emerge as powerful allies in the global effort to combat climate change, as they introduce innovative features like platooning, adaptive cruise control, and eco-driving. By leveraging these capabilities, these vehicles not only exhibit reduced fuel consumption but also significantly lower greenhouse gas emissions. Furthermore, the potential transition to electric propulsion represents a promising avenue for further diminishing their environmental footprint, aligning with the broader push towards sustainable transportation solutions.

Advancements in the Technological Landscape: The dynamic landscape of autonomous trucking is propelled by the rapid evolution and innovation of pivotal technologies, including Artificial Intelligence (AI), Light Detection and Ranging (LiDAR), 5G connectivity, and cloud computing. These technological strides play a pivotal role in elevating the performance, reliability, and security of autonomous trucks. Moreover, they pave the way for the emergence of novel business models and services, shaping a transformative future for the industry while bolstering its capacity to meet evolving demands.

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Report Synopsis

Report Metrics

Details

Base year considered

2022

Forecast Period considered

2023-2028

Base year market size

$2.9 billion

Market Size Forecast

$6.9 billion

Growth Rate

CAGR of 16.2% from 2023 to 2028

Segment Covered

By Component, Autonomy, Propulsion Type, Truck Type, End Use, and Region

Regions covered

North America, Europe, Asia-Pacific, and the Rest of the World (RoW)

Countries covered

U.S., Canada, Mexico, U.K., Germany, China, India, and Japan

Key Market Drivers

 

Growing Emphasis on Improved Road Safety and Traffic Control Reduced Emissions and Higher Fuel Efficiency of Autonomous Trucks Improved Technological Landscape

 

Autonomous Trucks Global Markets Segmentation:

By Component: Analyzing the Autonomous Truck Market’s Building Blocks

This segmentation delves into the intricate layers of the autonomous truck market, dissecting it based on the fundamental components that drive these cutting-edge vehicles. The bifurcation centers around the dichotomy of software and hardware. Software, the brain of the autonomous truck, encompasses programs and algorithms that orchestrate functions such as navigation, perception, and decision-making. Meanwhile, hardware comprises the sensory and operational apparatus, featuring devices like cameras, navigation systems, sensors, and Advanced Driver Assistance Systems (ADAS) components, collectively serving as the eyes and ears of these autonomous entities.

By Autonomy: Unraveling Degrees of Autonomy in the Trucking Realm

This market segmentation peels back the layers based on the degree of autonomy inherent in autonomous trucks, delineating the spectrum of human intervention required for seamless operation. Semi-autonomous trucks tread a middle ground, proficient in executing certain functions autonomously but necessitating human oversight and control. In contrast, fully autonomous trucks emerge as self-sufficient entities, adept at executing all functions autonomously, sans any human intervention, marking a paradigm shift in the landscape of autonomous transportation.

By Propulsion Type: Navigating the Power Sources of Autonomous Trucks

This segment intricately categorizes the market according to the type of propulsion systems propelling these autonomous marvels. Internal Combustion (IC) engines harness the combustion of fossil fuels for power generation, while electric trucks rely on batteries or fuel cells to store and convert electrical energy into propulsive force. Hybrid trucks ingeniously blend IC engines and electric motors, optimizing power and efficiency in a symbiotic fusion of traditional and contemporary technologies.

By Truck Type: Gauging Autonomy Based on Truck Size and Capacity

This segmentation classifies the market based on the size and capacity of autonomous trucks, offering a nuanced perspective. Light-duty trucks, with a Gross Vehicle Weight Rating (GVWR) below 6,000 lbs, find their niche alongside medium-duty trucks (6,000 to 26,000 lbs GVWR) and heavy-duty trucks (exceeding 26,000 lbs GVWR). This categorization provides a comprehensive understanding of the diverse landscape encompassing various truck types within the autonomous domain.

By End Use: Mapping Autonomous Truck Applications Across Industries

This segment unfolds based on the diverse industries and sectors harnessing the potential of autonomous trucks. Logistics and transportation, manufacturing, mining, construction, and an array of others form the tapestry of end-use applications. From the movement of goods and people to the production of goods, extraction of minerals, construction activities, and beyond, the autonomous truck’s impact extends across an array of sectors, encompassing ports, farming, oil and gas, chemicals, and agriculture.

By Region: Navigating the Global Terrain of Autonomous Truck Adoption

This geographical segmentation scrutinizes the global landscape, partitioning it into regions such as North America, Europe, Asia-Pacific, and the Rest of the World. Further sub-segmentation drills down into specific countries, providing a comparative analysis of market size, growth, and trends. This regional perspective offers invaluable insights into the diverse dynamics shaping the adoption and evolution of autonomous trucks across different corners of the globe.

This report on Autonomous Trucks: Global Markets provides comprehensive insights and analysis, addressing the following key questions:

1.  What is the projected market size and growth rate of the market?

The global market for autonomous trucks was projected to grow from $2.9 billion in 2022 to $6.9 billion in 2028, at a compound annual growth rate (CAGR) of 16.2% during the forecast period 2023-2028.

2.  What are the key factors driving the growth of the market?

The key factors driving the growth of the autonomous trucks market include growing emphasis on improved road safety and traffic control, reduced emissions and higher fuel efficiency of autonomous trucks, improved technological landscape, and growth of connected infrastructures.

3.  What segments are covered in the market?

Global autonomous trucks market is segmented based on component, autonomy, propulsion type, truck type, end use, and region.

4.  By End use, which segment will dominate the market by the end of 2028?

By the end of 2028, the logistics and transportation segment will continue to dominate the global autonomous trucks market. The segment will show a dominating position till the end of 2028, owing to lower load and long haulage costs, streamline traffic flows, shorter delivery time, and improve fuel efficiency by reducing freight and logistics costs.

5.  Which region has the highest market share in the market?

Europe holds the highest market share in the global autonomous trucks market. Presence of major OEMs such as Tesla, Inc., Robert Bosch GmbH, Mercedes Benz Group, and MAN Truck & Bus which are launching new models with advanced automation levels, implementation of various self-driving vehicle related rules and regulations by the European Union, and availability of advanced communication infrastructure drives the demand for adopting autonomous trucks in the region.

Some of the Key Market Players Are:

 AB VOLVO APTIV CATERPILLAR CONTINENTAL AG DENSO CORP. EINRIDE KODIAK ROBOTICS INC. MERCEDES-BENZ GROUP AG PACCAR INC. ROBERT BOSCH GMBH TESLA INC. TUSIMPLE HOLDINGS INC. WAYMO LLC

Directly Purchase a copy of the report with BCC Research.

For further information or to make a purchase, please get in touch with info@bccresearch.com.  

About BCC Research

BCC Research provides objective, unbiased measurement and assessment of market opportunities with detailed market research reports. Our experienced industry analysts’ goal is to help you make informed business decisions, free of noise and hype.

Contact Us
Corporate HQ: BCC Research LLC, 49 Walnut Park, Building 2, Wellesley, MA 02481, USA
Email: info@bccresearch.com,
Phone: +1 781-489-7301

For media inquiries, email press@bccresearch.com or visit our media page for access to our market research library.

Data and analysis extracted from this press release must be accompanied by a statement identifying BCC Research LLC as the source and publisher.

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BinBase Expands 2026 BIN Dataset with Instant Payout Intelligence for iGaming, Gambling, and Cross-Border Transfers

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BinBase updates its 2026 dataset with specialized Fast Funds, Visa Direct, and Mastercard MoneySend indicators to help iGaming operators and payout platforms execute seamless, instant card disbursements.

MIAMI, July 23, 2026 /PRNewswire-PRWeb/ — BinBase, a global provider of payment routing intelligence and card issuing data, has introduced specialized instant payout indicators as part of its upgraded 2026 BIN Database. Tailored for iGaming operators, online gambling platforms, crypto-to-fiat ramps, and payout aggregators, the updated dataset helps platform engineers streamline real-time card disbursements and Push-to-Card (P2C) transactions.

In high-velocity sectors such as online betting and gaming, instantaneous player payouts are a primary driver of customer retention. However, executing Push-to-Card transactions through protocols like Visa Direct and Mastercard MoneySend requires knowing whether the receiving card issuer supports Fast Funds for specific merchant category codes (MCCs). Attempting instant payouts on non-eligible cards leads to declined transactions, elevated processing fees, and poor user experiences.

The 2026 BinBase release solves this operational bottleneck by delivering dedicated attributes for real-time fund disbursements:

Fast Funds Eligibility: Granular indicators identifying domestic and cross-border Fast Funds support across global Visa and Mastercard ranges.Online Gambling Fast Funds (OG FF): Dedicated flags specifically identifying card ranges authorized to receive real-time gambling and betting payouts.Mastercard MoneySend & Visa Direct Indicators: Precise protocol compatibility markers (MS Ind & MT Ind) ensuring push transactions are routed only to eligible recipient cards.Direct Debit & Pull-Funds Support: Indicators for recurring collections and account-funding transactions.

“Player payouts in iGaming cannot wait for standard 2-to-3-day ACH settlements,” said a spokesperson for Damiko Inc. “By embedding our Fast Funds and Gambling FF flags into their payment engines, operators can instantly validate recipient cards before initiating a transfer, guaranteeing high success rates and instant liquidity for their users.”

Fintech engineers and payout architects can examine the full 29-field database schema and access a free 2026 sample dataset on GitHub.

To explore commercial licensing, bulk database downloads, or custom data feeds, visit BinBase at https://binbase.com.

About Damiko Inc

Damiko Inc is a US-based fintech data provider specializing in card issuer analytics, payment routing data, and global BIN database solutions. Operating through its flagship product, BinBase.com, the company supplies high-precision transaction intelligence to help merchants and payment facilitators worldwide optimize approval rates and mitigate processing fees.

Media Contact
Fedor Lavrikoff, BinBase, 1 7866133334, sales@binbase.com, www.binbase.com 

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Walnut Coding’s Young Coders Serve as ‘Instructors’ at Huawei Cloud Developer Training Camp

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Ages 8 and 15, students showcase AI-era project-building skills from concept to working application

BEIJING, July 23, 2026 /PRNewswire/ — Walnut Coding (the “Company”), a leading online platform for youth coding education, said two of its students – ages 8 and 15 – have joined the “instructor” lineup at Huawei Cloud Developer Training Camp, making them among the youngest “instructors” in the program’s history. The Company cites the pair as a prime example of how young learners can combine coding fundamentals with AI tools to turn ideas into working applications.

The two students, Bolin Du, 8, and Peiqi Gao, 15, built working applications using Huawei Cloud CodeArts, an AI coding assistant, then presented the projects to the training camp themselves – walking the audience through their design choices, technical builds, and debugging process.

The move lands at a moment when AI coding tools are forcing a rethink across the education sector. Tools that can generate functioning code from a plain-language prompt have undercut the traditional argument for teaching children to program – that they need the skill to build things themselves. Walnut Coding’s answer is that the more valuable skill now is judgment – knowing what problem to solve, breaking it into parts, and determining whether an AI’s output actually works.

Gao built a travel-planning application that generates routes, itineraries, and recommendations based on user input, handling the project end to end, from requirements and design through coding and debugging. Du, the younger of the two, built an interactive calendar application, using HTML for page structure, CSS for visual design, and JavaScript for interactive features. Both students then took on an instructor’s role at the camp, presenting their project goals and technical implementation to the audience – a step Walnut Coding says separated the work from a typical classroom assignment.

These were not classroom exercises but working projects, built and presented inside a professional developer-training environment. The experience demanded more from both students than simply producing something functional – they needed to articulate their reasoning, defend technical choices, and refine the final result under scrutiny. Their participation signals a broader shift underway in what youth coding education can deliver.

AI is making code generation easier, but it is also redrawing which skills actually matter. A student who relies only on one-click generation may get a rough prototype quickly, but still struggle to spot logical flaws, judge whether the output is reliable, or turn an abstract idea into a product that actually works. Students with programming foundations, by contrast, are better positioned to define requirements, evaluate what the AI produces, correct its errors, and treat the technology as a tool rather than a shortcut to lean on.

“AI can help children generate code faster, but it cannot decide for them what problem they should solve, nor can it make the final judgment about whether the result is truly effective,” said Pengxuan Zeng, founder and CEO of Walnut Coding. “What these two students demonstrated is not just coding technique, but the ability to define needs, break down tasks, verify outcomes, and turn an idea into a working product. That is why we believe young people still need to learn programming in the AI era.”

Walnut Coding structures its courses around that thesis, pairing student-led project work with teaching-assistant guidance and AI-assisted support. According to the Company, this data is continuously fed back into its systems to refine the personalization of AI-assisted feedback — a closed-loop process linking teaching, practice, feedback, and curriculum development.

The Company frames the payoffs less around producing professional software engineers than around a broader form of literacy. As AI continues to reshape how tasks get done, the ability to understand the technology, structure problems clearly and collaborate effectively with intelligent tools may prove one of the most durable skills a young learner can develop.

Walnut Coding says it plans to keep expanding opportunities for students to build practical projects, partner with industry technology platforms such as Huawei Cloud, and develop the core capabilities needed to build with technology in the AI era.

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MOREH Showcases High-Performance LLM Inference on AMD GPUs at AMD Advancing AI 2026

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SAN FRANCISCO, July 23, 2026 /PRNewswire/ — Moreh, an AI infrastructure software company, led by CEO Gangwon Jo, participated in AMD Advancing AI 2026, AMD’s flagship annual AI event held in San Francisco on July 22–23 (local time), where it demonstrated its distributed inference solution, the MoAI Inference Framework, running on AMD GPUs.

At the event, Moreh presented a live demonstration of the GLM-5.1 large language model (LLM) powered by the MoAI Inference Framework on a system equipped with 32 AMD Instinct™ MI300X GPUs across four nodes. Visitors experienced the chatbot firsthand, evaluating its response speed and service quality while observing performance across a range of real-world use cases.

Unlike conventional demonstrations that simply run an AI model, Moreh’s showcase displayed key inference service metrics in real time, including GPU utilization, Tokens Per Second (TPS), Time To First Token (TTFT), and Time Per Output Token (TPOT). This enabled attendees to directly verify both inference performance and GPU resource efficiency in a production-like service environment.

Global AI industry leaders and enterprise customers attending the event expressed strong interest in the system’s fast response times and stable performance. In particular, the live deployment of the computationally demanding GLM-5.1 model on AMD GPUs at production-grade service levels received positive feedback from visitors.

Moreh’s MoAI Inference Framework is widely recognized as the world’s first commercially deployed distributed inference solution built for the AMD ecosystem. Its distributed inference and heterogeneous computing technologies are designed to dramatically reduce AI service costs, enabling broader adoption of AI worldwide. The technology addresses one of the industry’s biggest challenges-the rapidly rising infrastructure and service costs caused by increasingly larger AI models-by delivering a more efficient inference infrastructure.

Moreh CEO Gangwon Jo stated, “This event provided an opportunity for global customers to verify firsthand that top-tier inference performance can be achieved on AMD GPU environments,” and added “We will continue advancing our AI infrastructure software so enterprises can operate AI services as efficiently as possible, regardless of the underlying GPU platform.”

Moreh develops its own AI infrastructure engine and has expanded its end-to-end AI capabilities through its foundation LLM subsidiary, Motif Technologies, covering both AI infrastructure and foundation models. The company is also strengthening its presence in the global AI market through strategic partnerships with leading technology companies, including AMD and Tenstorrent.

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