Edge AI-powered V2X communication enables vehicles to process sensor data locally and exchange real-time information with infrastructure, pedestrians, and other vehicles, advancing collision avoidance, traffic optimization, and autonomous driving capabilities.
02 Feb 2026 | 256 Views | By Nikhil Goel, embedUR
Traffic congestion, unpredictable driving behavior, and preventable accidents have long defined our transportation systems, responding to failure and incidents instead of avoiding them. While we stand on the brink of advanced safety features and connected infrastructure, legacy architectures simply cannot keep up with the demands of a connected world.
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Traffic congestion, unpredictable driving behavior, and preventable accidents have long defined our transportation systems, responding to failure and incidents instead of avoiding them. While we stand on the brink of advanced safety features and connected infrastructure, legacy architectures simply cannot keep up with the demands of a connected world.
That equation is now shifting. With Edge AI powering V2X (Vehicle-to-Everything) communication, vehicles are evolving into proactive decision-makers. They can anticipate hazards, instantly communicate with their surroundings, and ease traffic flow—without relying on distant cloud servers.
This shift in connectivity and intelligence doesn’t just enhance the way we travel—it is redefining mobility itself. We are moving toward predictive accident avoidance, safer roads, and the next era of autonomous driving. V2X and Edge AI are paving the way for roadways that are safer, smarter, and faster.
The Four Pillars of V2X Communication
Vehicle-to-Everything (V2X) is a next-generation wireless communication framework that allows vehicles to exchange real-time data with their surroundings, significantly enhancing safety and efficiency on the road .
At its foundation, V2X relies upon four forms of communication.
Together these four forms of communication, integrated with AI, 5G, and edge computing, comprise a real-time intelligence layer that transcends a vehicle's on-board sensor capability, setting in motion a path to increased levels of safety, awareness, and eventually autonomy in the mobility space.
Edge AI: The Real Power Behind V2X Communication
V2X may be redefining how vehicles communicate, but none of it works without Edge AI. Even the simplest autonomous driving functions depend on it for real-time decision-making. Here’s why:
In short, V2X runs on Edge AI. Without it, the road to autonomy doesn’t move forward.
Inside the Edge AI Systems That Make V2X Possible
1. Hardware components
Edge AI powers V2X by relegating intelligence on-board while weaving vehicles into nearby intelligent infrastructure, like roadside units and 5G towers. At the hardware level, vehicles use specialized AI chips to process high volumes of sensor data in real-time. For instance, the NVIDIA DRIVE Thor provides 2,000 TOPS of performance to fuse LiDAR, radar, and camera inputs, while the Qualcomm Snapdragon Ride, at 5W/TOPS, is an energy-efficient way to deliver the performance needed for autonomous driving–for processes that require both demanding power and energy efficiency.
2. Communication Protocols in V2X
The wireless communication protocols are as important for V2X as the processors and chips that process the data. The two primary approaches to wireless communication for V2X vehicles are: Cellular V2X (C-V2X) and Dedicated Short-Range Communications (DSRC). C-V2X and DSRC can enable direct vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, and vehicle-to-pedestrians (V2P) communication. C-V2X can also be operated in two modes.
The PC5 direct mode utilizes the 5.9 GHz spectrum for low-latency V2V communication, V2I communication and this communication can happen without a cellular tower. The 5G NR-Uu mode supports non-real-time functions, such as AI model updates, fleet diagnostic updates, etc. DSRC uses IEEE 802.11p as its base system while OFDM is used for the channel access. DSRC is highly reliable in clear line of site but, if there is no line of site, or additional lanes of travel between vehicles, there is a question of reliability, which may be better suited for non-dense urban areas.
3. Layered Architecture of C-V2X
C-V2X operates through a layered architecture, with each layer handling a distinct function in the communication process. The top layer is the application layer where safety-critical use cases exist, such as collision avoidance scenarios and traffic coordination. The next layer down is the transport layer, which includes packet segmentation, error correction, and ensuring Quality of Service (QoS) requirements are met.
The network layer manages routing between vehicles and external systems like IoT platforms or cloud services, while the data link layer controls Medium Access Control (MAC) protocols to efficiently schedule LTE/5G transmissions. At the foundation, the physical layer modulates and demodulates signals over the 5.9 GHz spectrum, ensuring robust wireless communication. Together, these layers enable seamless, secure, and low-latency V2X interactions.
4. Privacy and Performance: Why Edge AI Leads the Way
To balance privacy with efficiency, Edge AI also utilizes advanced techniques to keep sensitive data secure without compromising powerful model training. Federated learning is a method where cars can collaboratively learn a AI system without ever transmitting the raw data. This is the approach used in Tesla’s 4D auto-labeling system. In fact, there are many ways Edge AI can leverage federated learning as well as homomorphic encryption, as in the case of securely storing customers’ data by only providing encrypted data which would comply with certain regulations (like GDPR).
Why Edge AI is Outpacing Cloud AI for Smart Vehicles
Edge AI outperforms Cloud AI in smart mobility primarily due to its ultra-low latency (1–10 ms), lower bandwidth requirements, and ability to operate autonomously. In contrast, Cloud AI typically experiences higher latency (50–500 ms), requires high bandwidth, and depends heavily on continuous wireless connectivity.
Because Edge AI processes information locally, within the vehicle, it is also less exposed to cyber threats, making it inherently safer. While Cloud AI remains valuable for fleet-level analysis, long-term diagnostics, and aggregated insights, Edge AI is becoming the preferred choice for safety-critical, real-time decisions such as traffic optimization, collision avoidance, and other rapid response functions essential for efficient smart mobility.
Conclusion: Driving Towards an Intelligent Future
V2X powered by Edge AI is more than a technology happening in real time; it is becoming the network of intelligent transportation. By enabling vehicles to talk in real time, process large amounts of sensors data in the clouds or edge, and operate securely with ultra-low latency, the ecosystem outlined in this document is paving the way for safer roads, improved traffic flow, and embraced mobility of the future (drone deliveries, autonomous vehicles and ridesharing). In the decade ahead, innovation will be enabled by frontier technologies including 6G, quantum computing, and much more, allowing paradigms of precision, connectivity, security, and predictable intelligence to expand exponentially.
However, the unlocking of connected mobility is not only an innovative opportunity; it will also require a collaborative effort where automakers, governments, and technology leaders align to build infrastructure, develop common standards, and be security and privacy conscious. The decisions made today will determine how we move in the future!
And the future of mobility is not even a question of 'if'... it is a question of 'are we ready?' The possibilities are limitless. Now, the question is: are we prepared to lead the change?
Nikhil Goel is the Senior Director of Engineering at embedUR. Views expressed are the authors’ personal.