However, the forefront of intelligent driving believes that vehicle intelligence does not mean a lack of connectivity. On the contrary, the ultimate goal of vehicle intelligence will be to achieve the Internet of Vehicles (IoV). A large number of vehicles of the same brand or with the same intelligent driving system will be connected to the cloud platform. Through the aggregation of massive amounts of data and collective intelligent analysis, continuous algorithm iteration and safety warnings will be achieved. Unlike vehicle-road cooperation, which focuses on ultra-low latency direct connection between vehicles and roadside facilities and leverages edge computing and C-V2X/DSRC technologies to provide redundant environmental perception and real-time command issuance for autonomous driving, the Internet of Vehicles will place a greater test on the capabilities of autonomous driving systems.
What is the Internet of Vehicles (IoV)?
Vehicle-to-everything (V2X) is a technology system that connects vehicles with the cloud and big data through cellular networks (3G/4G/5G). In this model, each vehicle uploads GPS trajectory, sensor data (cameras, radar, lidar, etc.), and driving behavior information to the cloud platform in real time. Relying on the powerful computing power and deep learning models of the cloud, optimal route planning, group safety warnings, and real-time traffic analysis are quickly generated. Updates and instructions are then sent to vehicles via OTA (Over-the-Air), realizing a closed-loop service of "cloud-vehicle-cloud".
The core value of the Internet of Vehicles (IoV) lies in leveraging "collective intelligence." When a large number of vehicles using the same system share data, the cloud can aggregate and analyze this data to accurately predict road congestion trends and potential risks, and proactively push acceleration/deceleration or detour suggestions to following vehicles, thereby significantly reducing accident rates and travel time fluctuations. Have you ever encountered a warning when using navigation software that a vehicle ahead is about to brake or turn left? This is actually a manifestation of "collective intelligence." The reason this warning appears is because the vehicle ahead is also using the same navigation software. If multiple cars on the road share the same intelligent driving system, the function of "collective intelligence" will also be realized.
Furthermore, the Internet of Vehicles (IoV) provides a convenient channel for the continuous iteration of vehicle software. Manufacturers utilize cloud platforms for large-scale model training and online simulation, and quickly push updates to every connected vehicle via OTA (Over-The-Air) updates, enabling continuous optimization of driving strategies and perception algorithms. This "online upgrade" capability not only shortens the development cycle but also improves the vehicle's adaptability and safety in complex road environments.
What is Vehicle-to-Infrastructure (V2I) cooperation?
Vehicle-to-Infrastructure (V2I) emphasizes direct communication and collaborative operation between vehicles and road infrastructure such as traffic lights, roadside units (RSUs), roadside radar, and cameras. Through wireless communication technologies such as Dedicated Short-Range Communications (DSR) or Cellular Vehicle-to-Everything (C-V2X), vehicles can acquire critical data such as real-time roadside traffic signals, construction warnings, and pedestrian dynamics within milliseconds and react rapidly.
To further reduce latency and improve reliability, vehicle-road cooperative systems often introduce Mobile Edge Computing (MEC) nodes to push data processing and decision-making down to the roadside, ensuring that the end-to-end latency from data acquisition to early warning broadcast is controlled within 10 ms, meeting the emergency braking requirements of highway conditions and complex intersections.
The value of vehicle-road cooperation lies in providing redundant environmental perception and real-time command issuance for autonomous driving, compensating for the shortcomings of single-vehicle perception in blind spots and extreme weather conditions, and giving the entire vehicle system a "second line of defense" in critical moments. At the same time, it is also an important component of intelligent transportation, providing real-time and refined data support for urban traffic management and planning through three-way collaboration between the roadside, cloud, and vehicle. However, vehicle-road cooperation is difficult to implement due to its high cost and complexity (related reading: Why did vehicle-road cooperation "cool down" in 2025?).
What are the differences between vehicle-to-everything (V2X) and vehicle-road cooperation?
While both connect vehicles to the network, vehicle-to-everything (V2X) and vehicle-to-infrastructure (V2I) communication differ significantly in essence. V2X emphasizes "cloud convergence and group collaboration," with vehicles maintaining a continuous connection to a remote cloud platform via cellular networks, highlighting cloud capabilities such as "high bandwidth + massive storage + complex computing." V2I, on the other hand, focuses on "local direct connection and edge processing," establishing dedicated links between vehicles and roadside infrastructure through C-V2X/DSRC, and completing critical decisions at edge nodes, emphasizing local response capabilities of "ultra-low latency + high reliability." This difference directly determines their respective roles in application scenarios: V2X is suitable for large-scale road condition analysis, group risk warnings, and long-cycle algorithm iterations; V2I excels at emergency warnings and collaborative control of vehicles traveling at high speeds or in complex intersections.
When millions of vehicles from the same system are connected to the same cloud platform, the Internet of Vehicles (IoV) possesses the inherent advantage of "swarm intelligence." The cloud continuously aggregates the speed, acceleration, braking status, and obstacle detection results of each vehicle, and uses machine learning algorithms to identify potential congestion areas or accident hotspots. This model not only reduces blind spots caused by individual vehicle perception errors but also achieves "preemptive" collective defense. Compared to traditional reliance on local detection by onboard sensors, swarm intelligence can intervene in the early stages of an incident, minimizing the possibility of rear-end collisions and chain-reaction collisions. Simultaneously, through continuous over-the-air (OTA) updates, the cloud optimizes algorithm models based on real-time data, continuously improving the accuracy of alerts and the timeliness of decision-making.
Vehicle-to-everything (V2X) systems excel at completing data collection and command issuance at the "millionth of a second" level, constructing a local, second-level protection network for autonomous driving. Roadside sensors and RSUs are deployed at key nodes, broadcasting traffic light phases, construction warnings, and pedestrian dynamics via C-V2X or DSRC, directly reaching the vehicle control unit without going through a remote cloud. Thanks to edge computing, data is processed locally in real time, with end-to-end latency as low as 1–10ms, sufficient to handle emergency braking demands during high-speed lane changes on highways or collision warnings at complex intersections. This "local, second-level protection" mechanism not only improves the reliability of autonomous driving in extreme scenarios but also provides the system with backup perception and decision-making capabilities in the event of a network outage.
Typical application scenarios of vehicle-to-everything (V2X) and vehicle-road cooperation
In daily use, vehicle-to-everything (V2X) and vehicle-road cooperation each have unique application scenarios and often work together. The most intuitive scenario of V2X is "crowd navigation": when thousands of vehicles connected to the same platform are traveling on the same road segment, the cloud can calculate the average speed and braking frequency of the road segment in real time, and push the optimal speed and route suggestions to the fleet in batches, thereby reducing congestion and fuel consumption.
In the "green wave" traffic system on urban main roads, the advantages of vehicle-road cooperation are even more obvious. The roadside traffic lights broadcast the phase sequence of the current and several future signal cycles to the approaching vehicles via C-V2X. Vehicles only need to maintain a predetermined speed to pass through a series of green lights continuously, reducing energy consumption and emissions caused by stopping, waiting, and sudden acceleration and braking.
In emergency avoidance scenarios, the two can also provide hybrid protection if they work together. When the cloud detects a chain accident trend on the road ahead, it first broadcasts a warning instruction to the convoy; at the same time, the roadside RSU is also monitoring local sensors. Once it detects that an individual vehicle is skidding or a pedestrian is crossing the line, it immediately pushes an emergency braking request through C-V2X to ensure that dual safety intervention for the vehicles is completed in the shortest possible time.
What are the differences in needs between vehicle-to-everything (V2X) and vehicle-road cooperation?
The construction of the connected vehicle ecosystem requires deep cooperation among automakers, telecommunications operators, cloud service providers, and software providers. Automakers are responsible for integrating high-bandwidth communication modules and OTA update frameworks into vehicles; operators provide cellular networks covering urban and rural areas and edge computing infrastructure; cloud service providers build big data platforms and AI models; and software vendors develop navigation, risk warning, and fleet management applications. Based on this, business models mainly include diversified services such as data subscriptions, paid OTA upgrades, targeted advertising, driving behavior analysis, and insurance risk control.
Vehicle-road cooperation relies more on infrastructure investment from governments and local traffic management departments. The deployment, operation, and maintenance of roadside units (RSUs) and edge computing nodes are typically undertaken through public-private partnerships, with business models including "green wave" subscriptions, roadside data services, and revenue sharing with traffic management platforms. Automakers and mobility platforms can collaborate with governments to provide users with value-added experiences based on roadside services, such as priority passage, emergency avoidance guarantees, and toll discounts.
In the future, with the commercialization of 6G and higher spectrum, communication bandwidth will be further improved and latency will be further reduced, and the integrated cloud-edge-device architecture will become more mature. AI and big data will play a greater role in group behavior prediction and roadside global optimization, enabling more precise traffic flow control and personalized driving services. At the same time, based on open platforms and standardized interfaces, cross-brand and cross-system interconnection will become the norm, laying the foundation for the global popularization and international deployment of autonomous driving.
