V2X
Agentic AI and the Future of Communications for Autonomous Vehicle (V2X)
by Prashant Vajpayee (bio below), edited by Alan J Weissberger
Abstract:
Autonomous vehicles increasingly depend on Vehicle-to-Everything (V2X) communications, but 5G networks face challenges such as latency, coverage gaps, high infrastructure costs, and security risks. To overcome these limitations, this article explores alternative protocols like DSRC, VANETs, ISAC, PLC, and Federated Learning, which offer decentralized, low-latency communication solutions.
Of critical importance for this approach is Agentic AI—a distributed intelligence model based on the Object, Orient, Decide, and Act (OODA) loop—that enhances adaptability, collaboration, and security across the V2X stack. Together, these technologies lay the groundwork for a resilient, scalable, and secure next-generation Intelligent Transportation System (ITS).
Problems with 5G for V2X Communications:
There are several problems with using 5G for V2X communications, which is why the 5G NR (New Radio) V2X specification, developed by the 3rd Generation Partnership Project (3GPP) in Release 16, hasn’t been widely implemented. Here are a few of them:
- Variable latency: Even though 5G promises sub-milliseconds latency, realistic deployment often reflects 10 to 50 milliseconds delay, specifically V2X server is hosted in cloud environment. Furthermore, multi-hop routing, network slicing, and delay in handovers cause increment in latency. Due to this fact, 5G becomes unsuitable for ultra-reliable low-latency communication (URLLC) in critical scenarios [1, 2].
- Coverage Gaps & Handover Issues: Availability of 5G network is a problem in rural and remote areas. Furthermore, in fast moving vehicle, switching between 5G networks can cause delays in communication and connectivity failure [3, 4].
- Infrastructure and Cost Constraint: The deployment of full 5G infrastructure requires dense small-cell infrastructure, which cost burden and logistically complex solution especially in developing regions and along highways.
- Spectrum Congestion and Interference: During the scenarios of share spectrum, other services can cause interference in realm of 5G network, which cause degradation on V2X reliability.
- Security and Trust Issues: Centralized nature of 5G architectures remain vulnerable to single point of failure, which is risky for autonomous systems in realm of cybersecurity.
Alternative Communications Protocols as a Solution for V2X (when integrated with Agentic AI):
The following list of alternative protocols offers a potential remedy for the above 5G shortcomings when integrated with Agentic AI.
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While these alternatives reduce dependency on centralized infrastructure and provide greater fault tolerance, they also introduce complexity. As autonomous vehicles (AVs) become increasingly prevalent, Vehicle-to-Everything (V2X) communication is emerging as the digital nervous system of intelligent transportation systems. Given the deployment and reliability challenges associated with 5G, the industry is shifting toward alternative networking solutions—where Agentic AI is being introduced as a cognitive layer that renders these ecosystems adaptive, secure, and resilient.
The following use cases show how Agentic AI can bring efficiency:
- Cognitive Autonomy: Each vehicle or roadside unit (RSU) operates an AI agent capable of observing, orienting, deciding, and acting (OOAD) without continuous reliance on cloud supervision. This autonomy enables real-time decision-making for scenarios such as rerouting, merging, or hazard avoidance—even in disconnected environments [12].
- Multi-Agent Collaboration: AI agents negotiate and coordinate with one another using standardized protocols (e.g., MCP, A2A), enabling guidance on optimal vehicle spacing, intersection management, and dynamic traffic control—without the need for centralized orchestration [13].
- Embedded Security Intelligence: While multiple agents collaborate, dedicated security agents monitor system activities for anomalies, enforce access control policies, and quarantine threats at the edge. As Forbes notes, “Agentic AI demands agentic security,” emphasizing the importance of embedding trust and resilience into every decision node [14].
- Protocol-Agnostic Adaptability: Agentic AI can dynamically switch among various communication protocols—including DSRC, VANETs, ISAC, or PLC—based on real-time evaluations of signal quality, latency, and network congestion. Agents equipped with cognitive capabilities enhance system robustness against 5G performance limitations or outages.
- Federated Learning and Self-Improvement: Vehicles independently train machine learning models locally and transmit only model updates—preserving data privacy, minimizing bandwidth usage, and improving processing efficiency.
The figure below illustrates the proposed architectural framework for secure Agentic AI enablement within V2X communications, leveraging alternative communication protocols and the OODA (Observe–Orient–Decide–Act) cognitive model.
Conclusions:
With the integration of an intelligent Agentic AI layer into V2X systems, autonomous, adaptive, and efficient decision-making emerges from seamless collaboration of the distributed intelligent components.
Numerous examples highlight the potential of Agentic AI to deliver significant business value.
- TechCrunch reports that Amazon’s R&D division is actively developing an Agentic AI framework to automate warehouse operations through robotics [15]. A similar architecture can be extended to autonomous vehicles (AVs) to enhance both communication and cybersecurity capabilities.
- Forbes emphasizes that “Agentic AI demands agentic security,” underscoring the need for every action—whether executed by human or machine—to undergo rigorous review and validation from a security perspective [16]. Forbes notes, “Agentic AI represents the next evolution in AI—a major transition from traditional models that simply respond to human prompts.” By combining Agentic AI with alternative networking protocols, robust V2X ecosystems can be developed—capable of maintaining resilience despite connectivity losses or infrastructure gaps, enforcing strong cyber defense, and exhibiting intelligence that learns, adapts, and acts autonomously [19].
- Business Insider highlights the scalability of Agentic AI, referencing how Qualtrics has implemented continuous feedback loops to retrain its AI agents dynamically [17]. This feedback-driven approach is equally applicable in the mobility domain, where it can support real-time coordination, dynamic rerouting, and adaptive decision-making.
- Multi-agent systems are also advancing rapidly. As Amazon outlines its vision for deploying “multi-talented assistants” capable of operating independently in complex environments, the trajectory of Agentic AI becomes even more evident [18].
References:
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- Coll-Perales, B., Lucas-Estañ, M. C., Shimizu, T., Gozalvez, J., Higuchi, T., Avedisov, S., … & Sepulcre, M. (2022). End-to-end V2X latency modeling and analysis in 5G networks. IEEE Transactions on Vehicular Technology, 72(4), 5094-5109.
- Horta, J., Siller, M., & Villarreal-Reyes, S. (2025). Cross-layer latency analysis for 5G NR in V2X communications. PloS one, 20(1), e0313772.
- Cellular V2X Communications Towards 5G- Available at “pdf”
- Al Harthi, F. R. A., Touzene, A., Alzidi, N., & Al Salti, F. (2025, July). Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks. In Telecom (Vol. 6, No. 3, p. 47). MDPI.
- DSRC Safety Modem, Available at- “https://www.nxp.com/products/wireless-connectivity/dsrc-safety-modem:DSRC-MODEM”
- VANETs and V2X Communication, Available at- “https://www.sanfoundry.com/vanets-and-v2x-communication/#“
- Yu, K., Feng, Z., Li, D., & Yu, J. (2023). Secure-ISAC: Secure V2X communication: An integrated sensing and communication perspective. arXiv preprint arXiv:2312.01720.
- Study on integrated sensing and communication (ISAC) for C-V2X application, Available at- “https://5gaa.org/content/uploads/2025/05/wi-isac-i-tr-v.1.0-may-2025.pdf“
- Ramasamy, D. (2023). Possible hardware architectures for power line communication in automotive v2g applications. Journal of The Institution of Engineers (India): Series B, 104(3), 813-819.
- Xu, K., Zhou, S., & Li, G. Y. (2024). Federated reinforcement learning for resource allocation in V2X networks. IEEE Journal of Selected Topics in Signal Processing.
- Asad, M., Shaukat, S., Nakazato, J., Javanmardi, E., & Tsukada, M. (2025). Federated learning for secure and efficient vehicular communications in open RAN. Cluster Computing, 28(3), 1-12.
- Bryant, D. J. (2006). Rethinking OODA: Toward a modern cognitive framework of command decision making. Military Psychology, 18(3), 183-206.
- Agentic AI Communication Protocols: The Backbone of Autonomous Multi-Agent Systems, Available at- “https://datasciencedojo.com/blog/agentic-ai-communication-protocols/”
- Agentic AI And The Future Of Communications Networks, Available at- “https://www.forbes.com/councils/forbestechcouncil/2025/05/27/agentic-ai-and-the-future-of-communications-networks/”
- Amazon launches new R&D group focused on agentic AI and robotics, Available at- “Amazon launches new R&D group focused on agentic AI and robotics”
- Securing Identities For The Agentic AI Landscape, Available at “https://www.forbes.com/councils/forbestechcouncil/2025/07/03/securing-identities-for-the-agentic-ai-landscape/”
- Qualtrics’ president of product has a vision for agentic AI in the workplace: ‘We’re going to operate in a multiagent world’, Available at- “https://www.businessinsider.com/agentic-ai-improve-qualtrics-company-customer-communication-data-collection-2025-5”
- Amazon’s R&D lab forms new agentic AI group, Available at- “https://www.cnbc.com/2025/06/04/amazons-rd-lab-forms-new-agentic-ai-group.html”
- Agentic AI: The Next Frontier In Autonomous Work, Available at- “https://www.forbes.com/councils/forbestechcouncil/2025/06/27/agentic-ai-the-next-frontier-in-autonomous-work/”
About the Author:
Prashant Vajpayee is a Senior Product Manager and researcher in AI and cybersecurity, with expertise in enterprise data integration, cyber risk modeling, and intelligent transportation systems. With a foundation in strategic leadership and innovation, he has led transformative initiatives at Salesforce and advanced research focused on cyber risk quantification and resilience across critical infrastructure, including Transportation 5.0 and global supply chain. His work empowers organizations to implement secure, scalable, and ethically grounded digital ecosystems. Through his writing, Prashant seeks to demystify complex cybersecurity as well as AI challenges and share actionable insights with technologists, researchers, and industry leaders.
ITU-R WP 5D new reports on IMT for PPDR applications, Terrestrial IMT for Cellular-Vehicle-to-Everything, 6G Vision & more
At its March 2021 virtual meeting, ITU-R WP5D completed a revision of the report ITU-R M.2291-1 – The use of International Mobile Telecommunications (IMT) for broadband Public Protection and Disaster Relief (PPDR) applications includes the IMT-2020 and 5G aspects in this public safety focused report to update the current report which was only based on IMT-Advanced 3GPP LTE technology. This revision was completed by ITU-R WP 5D and forwarded to Study Group 5 for action when they next meet in November 2021.
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ITU-R M.[IMT.C-V2X] – The use of the terrestrial component of IMT systems for Cellular-Vehicle-to-Everything
WP 5D is also developing a draft new report ITU-R M.[IMT.C-V2X] – The use of the terrestrial component of IMT systems for Cellular-Vehicle-to-Everything is intended to addresses the mutual relationship between IMT technologies and Cellular-Vehicle-to-Everything (C-V2X) as a specific application and elements of functions in IMT technologies that are used to realize C-V2X application.
Further, the report provides details on Overview on Usage of IMT technology, use cases, relationship between IMT and C-V2X, characteristics and capabilities supported by IMT, and case studies associated with C-V2X for the various scenarios including eMBB, mMTC, and URLLC of terrestrial component of IMT.
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Development of draft new report ITU-R M.[IMT.FUTURE TECHNOLOGY TRENDS TOWARDS 2030 AND BEYOND]
The draft new Report ITU-R M.[IMT.FUTURE TECHNOLOGY TRENDS TOWARDS 2030 AND BEYOND] is intended as a precursor to a “beyond IMT-2020” vision document for 6G that ITU-R WP 5D intends to produce in 2022. This trends report will assess where the technology is, and the current uses are for IMT-2020/5G and seek to identify the gaps and technical enablers anticipated to be necessary in the 2030 timeframe.
Furthermore, the expectation is that this Report will energize the academic and technology community to engage in the research and developments necessary to underpin a “beyond IMT-2020 and 6G view) as just focusing on new uses cases is insufficient to build such a future and the technology evolution requires a long lead time to fruition.
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Development of draft new report ITU-R M.[IMT TERRESTRIAL BROADBAND REMOTE COVERAGE]
The draft new Report ITU-R M.[IMT TERRESTRIAL BROADBAND REMOTE COVERAGE] – Terrestrial IMT for remote sparsely populated areas providing high data rate coverage is intended to provide details on scenarios associated with the provisioning of enhanced mobile broadband services to remote sparsely populated and underserved areas with a discussion on enhancements of user and network equipment.
It will distinguish between extending coverage on already deployed network and defining a use/case for deployment environment and is meant to meant to evaluate technical solutions required to extend the coverage of IMT system rather than discussing deployment layout for rural environments. The completion dates have been extended to the 39th WP 5D meeting (October 2021).
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Development of a draft new report ITU-R M.[IMT 2020.TDD.SYNCHRONIZATION]
The draft new report ITU-R [IMT2020.TDD.SYNCHRONIZATION] is intended to address the study of the aspects of synchronization operations of multiple IMT-2020 TDD networks in close proximity using the same frequency band, including analyses of coexistence issues when IMT operators utilize different synchronization modes, performance evaluation under different synchronization modes, and coexistence mitigation strategies.
The Report considers the further impacts of the introduction of technical advancement such as active antenna systems, etc. The completion dates were extended to the 41st WP 5D meeting (June 2022).
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Source: Chairman’s Report 37th e-meeting of Working Party 5D (1-12 March 2021 – Virtual), April 13, 2021
ITU-R and 3GPP: Use of IMT for Cellular-Vehicle-to-Everything Applications
ITU-R WP5D is working on a preliminary draft report titled, “The use of the Terrestrial Component of IMT for [Cellular-Vehicle-to-Everything] Application.”
When completed (TBD), the report will address the perceived mutual relationship between IMT (International Mobile Telecommunications) technologies and Cellular-Vehicle-to-Everything (C-V2X) as a specific application and elements of functions in IMT technologies that are used to realize C-V2X applications.
Author’s Note:
Vehicle to everything (V2X) is a term that refers to high-bandwidth, low latency and highly reliable communication between a broad range of transport and traffic-related sensors. Many pundits and cheerleaders say that 5G mobile networks will be key to providing connectivity for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. Others say that the 4G-LTE V2X sidelink will do just fine.
Also, there are two different types of V2X systems – one based on IEEE 802.11 standards and another (cellular) based on 3GPP specifications. That’s illustrated in this chart:
The focus of this article is on the Cellular-V2X system, previously developed by 3GPP and now via the aforementioned new draft ITU-R report.
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The future ITU-R report will provide details and an overview on: Usage of IMT technology, Relationship between IMT and C-V2X, Characteristics and Capabilities supported by IMT, and Case Studies associated with C-V2X for the various scenarios including eMBB, mMTC, and URLLC of terrestrial component of IMT.
IMT usages relevant to vehicle communication are also indicated in the ITU-R M.2445 “ITS usage” report.
The C-V2X applications [described in the 3GPP Release 16 specifications], referred to as Vehicle-to-Everything (V2X), contain the following four different types:
– Vehicle-to-Vehicle (V2V)
– Vehicle-to-Infrastructure (V2I)
– Vehicle-to-Network (V2N)
– Vehicle-to-Pedestrian (V2P)
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Utilizing 5G to enhance automotive safety was a focus area for 3GPP Release 16. Release 14 C-V2X introduced a 4G-LTE sidelink (V2V, V2I, V2P) to support basic safety use cases. Release 16 builds on Release 14/15 by introducing a NR-based sidelink that will enable new advanced safety use cases while also paving the path for autonomous driving. Release 16 supports reliable and efficient multicast communication based on HARQ feedback and uses distance as a new dimension at the physical layer, which enables “on-the-fly” multicast groups based on distance and applications.
Relevant ITU-R Recommendations and Reports:
Recommendation ITU-R M.1890 Operational radiocommunication objectives and requirements for advanced Intelligent Transport Systems
Recommendation ITU-R M.2083 IMT Vision – Framework and overall objectives of the future development of IMT for 2020 and beyond
Recommendation ITU-R M.2084 Radio interface standards of vehicle-to-vehicle and vehicle-to-infrastructure two-way communications for Intelligent Transport System applications
Recommendation ITU-R M.2121 Harmonization of frequency bands for Intelligent Transport Systems in the mobile service
Report ITU-R M.2228 Advanced intelligent transport systems (ITS) radiocommunications
Report ITU-R M.2441 Emerging usage of the terrestrial component of International Mobile Telecommunication (IMT)
Report ITU-R M.2444 Examples of arrangements for Intelligent Transport Systems deployments under the mobile service
Report ITU-R M.2445 Intelligent transport systems (ITS) usage
Handbook on Land Mobile (including Wireless Access) – Volume 4: Intelligent Transport Systems
[Editor’s note: More references to be added]
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References:
https://blog.3g4g.co.uk/2020/07/an-introduction-to-vehicle-to.html
https://ieeexplore.ieee.org/document/9212349
https://www.gsma.com/iot/wp-content/uploads/2020/07/02_5GAA_Maxime-Flament.pdf
https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-M.2441-2018-PDF-E.pdf
http://www.eng.auburn.edu/~szm0001/papers/3gpp_v2x.pdf
GSA Silicon Summit: Focus on Edge Computing, AI/ML and Vehicle to Everything (V2X) Communications
Introduction:
Many “big picture” technology trends and future requirements were detailed at GSA’s Silicon Summit, held June 18, 2019 in Santa Clara, CA. The conference was a “high level” executive briefing for the entire semiconductor ecosystem- including software, middleware and hardware. Insights on trends, key issues, opportunities and technology challenges (especially related to IoT security) were described and debated in panel sessions. Partnerships and collaboration were deemed necessary, especially for start-ups and small companies, to advance the technology, products and services to be offered in this new age of AI, ML/DL, cloud, IoT, autonomous vehicles, (fake) 5G, etc. Companies involved in the development of next generation Mobility and Edge Intelligence systems architectures and solutions discussed what opportunities, advancements and challenges exist in those key areas.
With the rapid proliferation of smart edge computing devices and applications, the volume of data produced is growing exponentially. Connected, and “intelligent,” devices are predicted to grow to 200 billion by 2020, generating enormous amounts of data every single day. The business potential created by this data comes with huge expectations. Edge devices, edge intelligence, high bandwidth connectivity, high performance computing, machine learning and other technologies are essential to enabling opportunities in markets such as Mobility and Industrial IoT.
This article will focus on Edge Computing, AI moving closer to the endpoint device (at the network edge or actually embedded in the end point device/thing), and vehicle to vehicle/everything communications.
While there were many presentations and panels on security, that is beyond the scope of the IEEE ComSoc Techblog. However, we share Intel’s opinion, expressed during a lunch panel session, that standards for Over The Air (OTA) security software/firmware updates are necessary for almost all smart/intelligent devices that are part of the IoT.
Architectural Implications of Edge Computing, Yogesh Bhatt VP of Products- ML, DL and Cognitive Tech Ericsson – Silicon Valley:
Several emerging application (data flow) patterns are moving intelligence from the cloud to local/metro area to on premises and ultimately to the endpoint devices. These applications include: cloud native apps like content delivery; AI enabled apps like sensing, thinking and acting; immersive apps like media processing/augmentation/distribution.
AI enabled Industrial apps are increasing. They were defined as: The ability to collect and deliver the right data/video/images, at the right velocity and in the right quantities to wide set of well-orchestrated ML-models and provide insights at all levels in the operation. Connectivity and compute are being packaged together and offered as “a service.” One example given was 4K video over (pre-standard) “5G” wireless access at the 2018 U.S. Open. That was intended to be a case study of whether 5G could replace miles of fiber to broadcast live, high definition sports events.
Yogesh Bhatt VP of Products- ML, DL and Cognitive Tech Ericsson – Silicon Valley
Image courtesy of GSA Global
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Required Architecture for Emerging App Patterns: Application Cloud, Management & Monetization Network slices, Mobile Fixed Cloud infrastructure, Distributed Cloud and Transport. The flow of emerging apps requires computing capability to be distributed based on the application pattern and flow. That in turn mandates cross-domain orchestration and automation of services.
Key take-aways:
- Emerging Application patterns will require significant compute capabilities close to the data sources and sinks (end points)
- Current Device-to-Cloud Architecture need to expand to encompass hosting points that provides such processing capabilities
- The processing capabilities at these Edge locations would be anything but like the centralized Cloud Data Centers (DCs)
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Heterogeneous Integration for the Edge, Yin Chang Sr. VP, Sales & Marketing ASE Group:
ASE sees the “Empowered Edge” as a key 2019 strategic trend. Edge computing drivers include: latency/determinism, cost of bandwidth, better privacy and security, and higher reliability/availability (connections go down, limited autonomy).
- At the edge (undefined where that is -see my comment below) we might see the following: Collect/Process data, Imaging Device, Image processing, Biometric Sensor, Microphone, Sensors with embedded MCUs, Environmental Sensor.
- At the core (assumed to be somewhere in the cloud/Internet): Compute/Intelligent processing, AI & Machine Learning, Networks/Server Processors, High Bandwidth Memory (HBM), Neuro-engine (future), Quantum computing (future).
Compute capabilities are moving to the edge and endpoints:
- Edge Infrastructure and IoT/Endpoint Systems are growing in compute power per system.
- As the number of IoT/Endpoint systems outgrows other categories, TOTAL Compute will be at the Endpoint.
Challenges at the Edge will require a cost effective integration solution which will need to deal with:
- Cloud connectivity – latency and bandwidth limitations
- Mixed device functionality – sense, compute, connect, power
- Multiple communication protocols
- Form factor constraints
- Battery life
- Security
- Cost High density
ASE advocates Heterogeneous Integration at the Edge— by material, component type, circuit type (IP), node and bonding/ interconnect method. The company has partnered with Cadence to realize System in Package (SiP) intelligent design with “advanced functional integration.” That partnership addresses the design/verification challenges of complex layout of advanced packages, including ultra-complex SiP, Fan-Out and 2.5D packages.
One such SiP design for wireless communications is antenna integration:
- Antenna on/in Package for SiP module integration
- Selective EMI Shielding for non-limited module level FCC certification
- Selective EMI Shielding – partial metal coating process by sputter for FCC EMI certification
- Small Size Antenna Integration – Chip antenna, Printed circuit antenna (under development)
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Democratizing AI at the Endpoint, Brian Faith, CEO of QuickLogic:
QuickLogic was described as “a platform company that enables our customers to quickly and easily create intelligent ultra-low power endpoints to build a smarter, more connected world.” The company was founded in 1989, IPO in 1999, and now has a worldwide presence. Brian said they were focused on AI for growth markets including:
▪ Hearable/Wearable
▪ Consumer & Industrial IoT
▪ Smartphone/Tablet
▪ Consumer Electronics
AI and edge computing are coming together such that data analytics is moving from the cloud to the edge to the IoT endpoint (eventually). However, there are trade-offs for where computing should be located which are based on the application type. Some considerations include:
▪Applications latency & power consumption (battery life) requirements
▪Data security can be a factor
▪Local insights are trivial and non-actionable
▪Smart Sensors => rich data => actionable if real-time
▪Network sends insightful data (less bandwidth needed)
▪Cloud focuses on aggregate data insights and actions
AI Adoption Challenges:
1. Resource-Constrained Hardware:
▪ Can’t just run TensorFlow
▪ Limited SRAM, MIPS, FPU / GPU
▪ Mobile or wireless battery/power requirements
2. Resource-Constrained Development Teams:
▪ Embedded coding more complex & fragmented than cloud PaaS
▪ Scarcity of data scientists, DSP, FPGA and firmware engineers
▪ Limited bandwidth to explore new tools / methods
3. Lack of AI Automated Tools:
• Typical process: MATLAB modeling followed by hand coded C/C++
• Available AI tools focus on algorithms, not end-to-end workflows
• Per product algorithm cost: $500k, 6-9 months; often far greater
For Machine Learning (ML) good training is vital as is the data:
• Addresses anticipated sources of variance
• Leverages application domain expertise
• Includes all potentially relevant metadata
• Seeks optimal size for the problem at hand
ML Algorithms should fit within Embedded Computing Constraints:
Endpoint Inference Models:
• Starts with model appropriate to the problem
• Fits within available computing resources with headroom
• Utilizes least expensive features that deliver desired accuracy
SensiML Toolkit:
• Provides numerous different ML and AI algorithms and automates the selection process
• Leverages target hardware capabilities and builds models within its memory and computing limits
• Traverses library of over 80 features to optimize selection to best features to fit the problem
A Predictive Maintenance for a Motor Use Case was cited as an example of AI/ML:
Challenges:
▪Unique model doesn’t scale across similar motors (due to concrete, rubber, loading)
▪ Endpoint AI decreases system bandwidth, latency, power
Monitoring States:
▪ Bearing / shaft faults
▪ Pump cavitation / flow inefficiency
▪ Rotating machinery faults
▪ Seismic / structural health monitoring
▪ Factory predictive maintenance
QuickLogic aims to democratize AI-enabled SoC Design using SiFi templates and a cloud based SoC platform with a goal of a custom SoC in 12 weeks! In 2020 the company plans to have: an AI Software Platform, SoC Architecture, and eFPGA IP Cores. Very impressive indeed, if all that can be realized.
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Empowering the Edge Panel Session:
Mike Noonen of Mixed-Com chaired a panel discussion on Empowering the Edge. Two key points made was the edge computing is MORE SECURE than cloud computing (smaller attack surface) and that as intelligence (AI/ML/data processing) moves to the edge, connections will be richer and richer. However, no speaker or panelist or moderator defined where the edge actually is located? Is it on premises, the first network element in the access network, the mobile packet core (for a cellular connection), LPWAN or ISP point of presence? Or any of the above?
Mike Noonen of Mixed-Com leads Panel Discussion
Photo courtesy of GSA Global
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After the conference, Mike emailed this to me:
“One of the many aspects of the GSA Silicon Summit that I appreciate is the topic/theme (such as edge computing). The speakers and panelists addressing the chosen theme offer a 360 degree perspective ranging from technical, commercial and even social aspects of a technology. I always learn something and gain new insights when this broad perspective is presented.”
I couldn’t agree more with Mike!
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V2X –Vehicle to Everything connectivity, Paul Sakamoto, COO of Savari:
V2X connectivity technology today is based on two competing standards: DSRC: Dedicated Short Range Communications (based on IEEE 802.11p WiFi) and C-V2X: Cellular Vehicle to Everything (based on LTE). Software can run on either, but the V2X connectivity hardware is based on one of the above standards.
DSRC: Dedicated Short Range Communications:
- Legacy Tech – 20 years of work, Low Latency Performance Range and reliability
- No carrier fees; minimize fixed cost
- Infrastructure needs; how to pay?
- EU Delegate Act win, but 5GAA is contesting
C-V2X: Cellular Vehicle to Everything:
- Developed from LTE-Big Money Backing
- Cellular communications history; good range and reliability
- Carrier fees required; subsidy for fixed costs
- Mix in with base stations to amortize costs
- China has chosen it as part of the government’s 5G plan
V2X Challenge: Navigate the Next 10 Years:
For mobile use, the main purpose is safety and awareness:
• Tight message security
• Low latency (<1ms)
• Needs client saturation
• Short range
For infrastructure, the main purpose is efficiency and planning:
• Tight message security
• Moderate latency (~100ms)
• Needed where needed
• Longer range
In closing, Paul said V2X is going to be a long raise with many twists and turns. Savari’s strategy is to be ”radio agnostic,” use scalable computing and scalable security elements, have a 7-10 year business plan with a 2-3 year product development cycle, and be ready to pounce at any inflection point (which may mean parallel developments).
May 20, 2020 Update:
ITU-R WP 5D will produce a draft new Report ITU-R M.[IMT.C-V2X] on “Application of the Terrestrial Component of IMT for Cellular-V2X.”
3GPP intends to contribute to the draft new Report and plans to submit relevant material at WP 5D meeting #36. 3GPP looks forward to the continuous collaboration with ITU-R WP 5D for the finalization of Report ITU-R M.[IMT.C-V2X].
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