Cars Communicating Badly Is an Already-Solved Problem

The history of networking is full of tools that repurposed solutions to very different kinds of problems first. Wi-Fi’s origins trace back in part to a team of Australian radio astronomers trying to detect signals from evaporating black holes. But the data processing tools they’d developed also proved capable at extracting clean messages from any chaotic, echoing signal environment . Echoes are echoes, after all, whether from distant star systems or from the far corner of the house. I research vehicle communications networks , connecting cars to cars and to transportation infrastructure like traffic lights—for tomorrow’s vehicle-to-everthing (V2X) networks . V2X research has long relied on models that assume “perfect” or “ideal” network conditions. Which is a simplifying assumption that makes the math tractable. But this assumption doesn’t reflect how real wireless signals behave in a moving, obstructed, high-density environment. That gap is exactly the kind of real-world unpredictability that open radio access networks (a.k.a. O-RAN )—an open, programmable architecture behind some 4G and 5G cellular networks—were built to manage. So why has the O-RAN standard—which is open and available to be applied well beyond 5G telecom—never been used for vehicle communications? Solutions to the vehicle-to-everything (V2X) problem have to date relied on new networking protocols built from scratch—only to discover chicken-and-egg problems , thorny standards wars , and real signal congestion challenges at scale . By contrast, O-RAN allows V2X engineers to reuse the networking protocols already developed for cellular communications. O-RAN was developed assuming cellphone towers are generally fixed in place. But, as can be seen below, O-RAN accommodates mobile “towers”—cars and trucks, in this case—with little additional effort . Imagining a New Way to Connect Vehicles Self-driving vehicle technology has largely been an each-car-for-itself endeavor. Tesla’s approach , for instance, relies heavily on powerful on-board banks of computers and suites of sensors spread around the car. However, as an alternative to the “data center on wheels” model, this new O-RAN approach to V2X that relies on each cars’ nearby neighbors, wherever they are on the road. Each O-RAN connected vehicle can then use a diversity of cars’ sensors and viewing angles for better group coordination and decision-making. There is, to be clear, no O-RAN V2X test network operating in the world. Not yet. It was just ten years ago that the Third-Generation Partnership Project ( 3GPP ) released its initial cellular V2X standard . The 3GPP have refined V2X over three major releases since. In the U.S. and the E.U., the FCC and related European agencies have put forward other standards for short-range wireless V2X communication protocols . However, no consensus standard has yet emerged. So lacking any clear, unambiguous guidance on the future of V2X networks, autonomous-car makers—like Waymo , Tesla , Zoox , and Cruise —have leaned more on self-reliance, bulking up each vehicle with as many sensors and GPUs as possible. Here, though, is where O-RAN might be able to help. A little like APIs (a.k.a. application program interfaces ) connect one app to another on your smartphone, O-RAN serves as an API for the network itself. And because of O-RAN’s open standards, a wireless network becomes programmable, vendor-neutral, and open to custom applications called xApps . To test our proof-of-concept framework, I have been part of a team simulating five-minutes of O-RAN V2X network traffic over one square kilometer of urban area, using real buildings and real-world road layouts from OpenStreetMap and traffic patterns generated by the modeling package SUMO . The simluations assumed a traffic density of 50-70 vehicles per km—not rush hour but not light traffic either. In our simulation, we assumed vehicles communicated via a millimeter-wave frequency of 28 gigahertz and that each component of our O-RAN V2X system had its own dedicated xApp. Taken together, these inputs—real geometry, real traffic, and each vehicle’s live GPS position—constitute what network researchers call a digital twin of the urban environment. That’s a virtual replica detailed enough for the network to reason about the physical world in real time. This virtual world gave us a real result, too. The simulations, published recently in IEEE Network , revealed that existing V2X standards—in which cars uncoordinatedly spit out messages into the network—result in signals “talking” over each other some 80-100 percent of the time. However, using O-RAN signal coordination, the message “collision” rate dropped to near zero. And that matters because a seized-up V2X network doesn’t just fail quietly. It can fail in ways that might make a road turn treacherous. How O-RAN Can Coordinate V2X Traffic High-frequency data links between cars are already difficult to maintain, even on a clear day with no buildings or city infrastructure getting in the way. Yet, in this situation, existing V2X networks leave a car to conduct blind searches for each dropped signal beam. Traveling at highway speeds, that search takes long enough for the surrounding world to change completely. An O-RAN network continuously tracks signal conditions across the network , and in O-RAN V2X simulations, we also gave the network access to a detailed map of the urban environment—building positions, road geometry, intersection layouts—combined with each vehicle’s GPS trajectory. Together, these parameters let the network’s control layer predict where and when a signal link is about to fail and instruct each car’s antenna to adjust before the connection drops. Signal pointing is one failure mode. Losing the connection entirely—because no direct path exists at all—is another. Consider, for instance, a crossroads of two busy streets, with a few alleys and parking lots adding to the list of potential dangers. If a signal from car A cannot reach car B directly, or if the path length is too far for an individual beam to travel, the signal must find an intermediary car or stationary sensor nearby that can pass along the message. And existing V2X standards are slow and reactive—polling potential relay vehicles one-by-one: Are you available? Can you redirect this message? By contrast, O-RAN keeps a running graph of optimized message routes, accounting for a range of real-world constraints. So when an O-RAN link fails (whether that link is direct from sender to receiver—or indirect), the system already has a reroute mapped out. This is partly why we included “ multi-hop routing ” in the O-RAN V2X simulations. Multi-hop V2X O-RAN routing complicated three separate elements of the simulation: for each signal’s middleman (some cars may be ideally positioned to relay a signal from car A to car B, but we made the simulation neglect any cars that were also overwhelmed with their own signals and signal-processing needs); for each signal’s strength (we required that every intermediate link be able to maintain a stable network connection, factoring in distance and traffic conditions); and for each signal’s latency (we required a realistic accounting for added signal latency time for each additional hop in a multi-hop routing). And with each added complication, O-RAN V2X multi-hop routing continued to extend the network’s capacity from 25 percent of nearby cars connected (without multi-hop) to nearly 100 percent (with multi-hop). These complications, at least at the simulation level, did not slow the V2X network down. How Could O-RAN Ever Be Scaled Up for the Real World? We are in touch with potential collaborators and institutions to develop testbeds, prototype hardware, and tester vehicles for potential proving grounds. The Institute of Science Tokyo , for instance, has already expressed interest in working on some of these early-stage problems. To date, our published research on O-RAN V2X has centered around a computer simulation only. Real-world hardware will undoubtedly surface challenges our simulation could not. So questions of network latency and the computational overhead needed for O-RAN V2X signaling remain as yet unresolved. Plus, concerns about full interoperability and realistic security will each demand their own investigations. After all, no one will trust a V2X network to do anything if that network’s cyber vulnerabilities haven’t been anticipated and patched in advance. Realizing the O-RAN V2X vision will require progress on multiple fronts simultaneously. On the standards side, O-RAN’s vehicular extensions—the interfaces that allow vehicles to participate in the network as managed elements rather than passive users—would ultimately need to be formally adopted by the O-RAN Alliance and recognized by 3GPP’s V2X specifications . That process takes years. On the industry side, there is a more immediate problem that our architecture is already positioned to solve: interoperability. Today, a car made by one manufacturer cannot necessarily parse V2X sensor data sent from a car made by another. Firmware is proprietary; data formats differ. But an O-RAN control layer would act as a universal translator—normalizing each vehicle’s data into a common format and accelerating a push toward true multi-platform vehicle-to-vehicle communications. A more widespread and truly universal standard would, by itself, represent a substantial step forward for V2X.