Revolutionizing Autonomous Transportation: Verizon and Cisco Collaborate on Virtualized Network for Deploying Delivery Vehicles and Robotaxis

Revolutionizing Autonomous Transportation: Verizon and Cisco Collaborate on Virtualized Network for Deploying Delivery Vehicles and Robotaxis

Home » News » Revolutionizing Autonomous Transportation: Verizon and Cisco Collaborate on Virtualized Network for Deploying Delivery Vehicles and Robotaxis
Table of Contents

Introduction

Cities can utilize cellular and mobile edge compute technology to implement delivery bots and robotaxis instead of relying on roadside units for network power, as demonstrated by recent research from Verizon and Cisco. A proof-of-concept project in Las Vegas tested this concept.

By leveraging existing cellular networks with a public MEC infrastructure, cities can accelerate the deployment of autonomous vehicles. However, concerns about service limitations have been raised by some government agencies.

A joint test by Cisco and Verizon confirmed that combining Verizon’s LTE network, public 5G Edge with AWS Wavelength, and Cisco Catalyst IR1101 routers meets the latency requirements for autonomous driving applications.

According to Krishna Iyer, director of systems architecture at Verizon, this project showcases the efficacy of mobile edge compute platforms for innovative connected transportation solutions with a more streamlined architecture.

Mark Knellinger, lead transportation solutions architect at Cisco, emphasized the cost-saving benefits for roadway operators by avoiding the significant expenses associated with deploying and managing a dedicated vehicle-to-everything environment.

Components of a vehicle-to-everything infrastructure

Autoweek defines “vehicle-to-everything” as the comprehensive term for a vehicle’s connected communications that assist in navigation and information sharing with the network. This includes real-time traffic data processing, response to changing road conditions, and recognition of road signs and alerts.

Additional communication aspects encompass:

  • Vehicle-to-vehicle: Wireless data exchange with other vehicles.
  • Vehicle-to-infrastructure: Information sharing with infrastructure components like cameras, streetlights, and signs.
  • Vehicle-to-pedestrian: Communication with cyclists and pedestrians for enhanced safety.
  • Vehicle-to-network: Data connection and sharing with data centers, road infrastructure, and other vehicles.

Getting multiple stakeholders to agree

An analysis of vehicle-to-infrastructure construction costs highlights the intricate nature of these projects. The report identifies various stakeholders involved in a V2I deployment:

  • Road operators
  • Mobile network operators
  • Suppliers/technology providers
  • Vehicle OEMs
  • Service provider
  • End-users (drivers and fleet operators)
  • Academic researchers

Establishing a unified communication architecture is a crucial aspect of these initiatives. The study evaluates the costs of a cellular network-based communication system, encompassing 2G, 3G, 4G, and/or 5G, as well as two direct communication approaches utilizing dedicated spectrum, 802.11p and PC5.

While a mobile communications network-based infrastructure is cost-effective and attractive, concerns persist regarding its ability to support safety-critical services. The report emphasizes that road operators often adopt a risk-averse stance, as direct communication systems are currently the only means to guarantee performance for such services. The limited scope of these services raises doubts about maintaining consistent service levels across the entire road network.

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roosho Senior Engineer (Technical Services)
I am Rakib Raihan RooSho, Jack of all IT Trades. You got it right. Good for nothing. I try a lot of things and fail more than that. That's how I learn. Whenever I succeed, I note that in my cookbook. Eventually, that became my blog. 
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<p><strong>original Article:</strong><br />
<br />
<strong>heading: Understanding the Impact of Artificial Intelligence on Cybersecurity</strong><br />
<br />
artificial Intelligence (ai) is Revolutionizing the Field of Cybersecurity by Enhancing Threat Detection and Response Capabilities. This Article Explores the Various Ways Ai is Being Utilized in Cybersecurity and the Implications for the Future of Digital Security.<br />
<br />
<strong>heading: Ai-powered Threat Detection</strong><br />
<br /></p>
<ol>
<li>ai Algorithms Can Analyze Vast Amounts of Data to Identify Patterns and Anomalies That May Indicate a Security Threat.<br /></li>
<li>machine Learning Models Can Be Trained to Recognize New and Evolving Threats Without Human Intervention.<br /></li>
<li>ai Can Automate the Process of Detecting and Responding to Cyber Attacks in Real Time, Improving Overall Security Posture.<br />
<br />
<strong>heading: Implications for Digital Security</strong><br />
<br /></li>
<li>ai Has the Potential to Significantly Reduce Response Times to Cyber Threats, Minimizing the Impact of Attacks.<br /></li>
<li>by Continuously Learning from New Data, Ai Systems Can Adapt to Emerging Threats and Strengthen Defense Mechanisms.<br /></li>
<li>the Use of Ai in Cybersecurity Can Free Up Human Analysts to Focus on More Strategic Tasks, Improving Overall Efficiency.<br />
<br />
<strong>heading: Conclusion</strong><br />
<br />
in Conclusion, Artificial Intelligence is a Powerful Tool in the Fight Against Cyber Threats. by Leveraging Ai-powered Technologies, Organizations Can Stay One Step Ahead of Cybercriminals and Protect Their Digital Assets More Effectively. Embracing Ai in Cybersecurity is Essential for Safeguarding Sensitive Information in Today's Increasingly Digital World.</li>
</ol>,
0 8 2

Original Article:

Heading: Understanding the Impact of Artificial Intelligence on Cybersecurity

Artificial Intelligence (AI) is revolutionizing the field of cybersecurity by enhancing threat detection and response capabilities. This article explores the various ways AI is being utilized in cybersecurity and the implications for the future of digital security.

Heading: AI-Powered Threat Detection

  1. AI algorithms can analyze vast amounts of data to identify patterns and anomalies that may indicate a security threat.
  2. Machine learning models can be trained to recognize new and evolving threats without human intervention.
  3. AI can automate the process of detecting and responding to cyber attacks in real time, improving overall security posture.

    Heading: Implications for Digital Security

  4. AI has the potential to significantly reduce response times to cyber threats, minimizing the impact of attacks.
  5. By continuously learning from new data, AI systems can adapt to emerging threats and strengthen defense mechanisms.
  6. The use of AI in cybersecurity can free up human analysts to focus on more strategic tasks, improving overall efficiency.

    Heading: Conclusion

    In conclusion, Artificial Intelligence is a powerful tool in the fight against cyber threats. By leveraging AI-powered technologies, organizations can stay one step ahead of cybercriminals and protect their digital assets more effectively. Embracing AI in cybersecurity is essential for safeguarding sensitive information in today’s increasingly digital world.
,

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