V2X (Vehicle-to-Everything) technology, as a complex system engineering solution, aims to break down the "information silo" of vehicles. It achieves a holistic view beyond single-vehicle perception by enabling high-frequency interactions between roadside infrastructure and in-vehicle terminals.

From a technical architecture standpoint, V2X relies heavily on the seamless integration of key components, including onboard units, roadside units, communication networks, and cloud control platforms. This integration significantly enhances traffic efficiency and reduces accident rates.

However, in practical applications, V2X technology is predominantly deployed in restricted areas such as ports, mining sites, and specific logistics parks. Its widespread adoption on complex urban public roads still faces numerous challenges.

Determinism and Technical Closed Loop in Restricted Areas

Restricted areas like ports are ideal pioneers for V2X technology due to their highly controllable environments. Within a closed port, vehicle routes are typically predetermined, and operational processes are standardized.

Unlike urban streets, where vehicles may drive against traffic, pedestrians may suddenly cross the road, and unpredictable small animals may appear, the primary traffic participants in ports are standardized container trucks or automated guided vehicles.

This simplified physical environment significantly reduces the difficulty of system perception. Roadside units can be densely deployed at key nodes, and high-definition cameras and LiDAR can fully cover local areas.

This "God's-eye view" effectively addresses common line-of-sight obstruction issues in container yards. Through "blind spot filling" by roadside sensors, vehicles can obtain real-time information around corners in advance, enabling smoother acceleration and deceleration control.

In fact, V2X technology in restricted areas like ports has now advanced to collaborative decision-making and even collaborative control stages. Raw data collected by roadside equipment can be processed in real-time by edge computing platforms, directly generating specific driving recommendations that are issued to vehicles.

Given the relatively low vehicle speeds within ports (typically maintained at 20 to 30 kilometers per hour), the system has sufficient time to compute and react. Meanwhile, port operational vehicles usually adopt unified wire-controlled chassis and electronic architectures, allowing control instructions issued by the roadside to be precisely executed by the vehicle's actuators.

In this mode, roadside facilities not only extend perception capabilities but also become an integral part of the vehicle's decision-making logic, forming a complete closed loop from perception to decision-making to control.

The reason V2X can be applied in restricted areas like ports is that these areas have a clear commercial closed-loop logic. Operators of restricted areas like ports serve as both investors and direct beneficiaries of technological applications. By achieving unmanned loading, unloading, and horizontal transportation through V2X, they can significantly reduce labor costs, improve 24/7 operational capabilities, and mitigate economic losses caused by safety incidents.

This highly aligned structure of rights, responsibilities, and benefits motivates the main entities in restricted areas to invest substantial construction costs to improve infrastructure.

Additionally, the communication environment in these areas is relatively pure, allowing for the use of privately deployed proprietary networks. This avoids data packet loss or latency fluctuations caused by signal congestion in public base stations, ensuring stable data interaction within the millisecond range.

The Long-Tail Dilemma and Perception Gap on Public Roads

When V2X technology attempts to move beyond enclosed areas and enter complex urban public roads, the technical challenges it faces grow exponentially.

The most notable feature of public roads is the "long-tail effect," meaning that while the vast majority of driving scenarios are routine, it is the extremely diverse and low-probability extreme conditions that determine the safety of autonomous driving.

Although roadside facilities can provide additional perception data, processing this massive amount of information at high speeds and fusing it in real-time with data from onboard sensors is an extremely challenging technical problem.

At urban intersections, roadside units may simultaneously monitor hundreds of dynamic targets. The system must determine within milliseconds which targets pose potential threats to the vehicle and filter out background noise.

In terms of technical implementation, public roads impose stringent requirements on the accuracy and synchronicity of data interaction. According to existing technical specifications, data interaction between roadside facilities and cloud control platforms must meet specific frequency and accuracy requirements.

For example, data reporting for roadside perception objects typically requires a fixed frequency of no less than 10Hz, while trajectory position accuracy often needs to be controlled within 0.2 meters. However, on public roads, due to complex geographical environments, GPS signals may produce multipath effects between buildings, leading to subtle deviations in coordinate system alignment between roadside equipment and onboard terminals.

If the spatial positioning of the two cannot achieve a high degree of synchronization, the system may produce "ghost images" or misjudge obstacle positions. This uncertainty can interfere with the vehicle's normal driving decisions.

Communication latency is even more critical in high-speed scenarios. Although 5G technology provides high bandwidth, channel latency and jitter remain difficult to avoid during base station handovers or network peak periods. At a speed of 100 kilometers per hour, a 100-millisecond delay means the vehicle has traveled nearly 3 meters.

The original intention of V2X is to allow the road to inform the vehicle of "invisible risks," but if there is a slight deviation in the timing of roadside instructions, the vehicle cannot react at the optimal moment.

Furthermore, environmental interference on public roads is significant. Complex electromagnetic environments and adverse weather conditions (such as heavy rain or dense fog) can reduce the reliability of roadside sensors, rendering the system ineffective when auxiliary support is most needed.

This perceptual limitation is also reflected in the uniformity of infrastructure coverage. While restricted areas can achieve comprehensive monitoring coverage, achieving seamless intelligent coverage in urban traffic networks and highway networks requires substantial financial investment.

During the stage when roadside facilities are not yet widespread, vehicles cannot rely on collaborative signals on all road segments. This "intermittent" collaborative perception requires vehicles to possess extremely strong standalone intelligence as a fallback. Since standalone intelligence is already sufficiently powerful, many developers view V2X as merely "icing on the cake" rather than "a necessity in times of crisis." This awkward technological positioning further limits its implementation speed on public roads.

Balancing Legal Liability and Social Ethics

Beyond technical barriers, the definition of legal liability is another major obstacle to the large-scale application of V2X on public roads. In restricted areas, the responsible party for accidents is usually relatively clear and can be attributed to vehicle hardware failures or system software defects, with losses shared internally among operators through agreements.

However, on public roads, the entities involved in V2X include automobile manufacturers, system developers, communication operators, roadside facility maintenance parties, and human drivers. This multi-entity participation makes the causal chain after accidents extremely complex.

The current traffic legal framework is built on the premise that "the human driver is the primary responsible party." If a collision occurs involving an autonomous vehicle in V2X mode, the process of determining fault becomes exceptionally difficult.

Suppose the accident was caused by a roadside unit providing an incorrect "no vehicle ahead" signal, leading the vehicle to accelerate at an intersection and collide with a violating vehicle. Should the law punish the vehicle manufacturer or the operator of the roadside facility?

In current judicial practice, joint liability arising from technical failures often lacks clear legal grounds. If it is stipulated that autonomous vehicles can obtain roadside data free of charge and the data provider is exempt from accuracy liability, this would discourage automakers from using roadside signals as a core control basis, reducing V2X to a mere warning system.

The absence of an insurance system also prevents the application of V2X from forming a closed loop. Existing vehicle insurance claim logic cannot cover losses caused by "cloud errors" or "roadside failures." If insurance companies cannot underwrite the technical risks of roadside facilities with actuarial pricing, large-scale promotion of V2X would entail significant uncertain legal risks.

Additionally, data privacy and cybersecurity are also focal points of concern for the widespread adoption of V2X. V2X requires real-time collection of massive amounts of traffic flow information, which may include sensitive data such as pedestrian characteristics and vehicle trajectories.

On public roads, there is no consensus yet on how to ensure compliance in the collection, transmission, and storage of this data or how to prevent system hacking that could lead to large-scale traffic paralysis.

The complexity of responsibility division also extends to the technical difficulty of accident investigation. To fairly assign responsibility, the system needs to record massive amounts of sensor data, known as the "black box" mechanism.

However, the amount of data on public roads is tens of millions of times greater than in restricted areas. How to reconstruct the true logic of an accident from massive bitstreams and prove which link experienced microsecond-level logical failures requires the establishment of an extremely professional and neutral technical arbitration system.

Before such a system is established, all participating parties will tend to conduct small-scale pilots in restricted areas out of risk aversion rather than applying them on public roads.

Economic Leverage Constraints and Industry Collaboration Bottlenecks

From the perspective of economic cost-benefit analysis, the widespread adoption of V2X on public roads faces significant cost expenditures. Construction investments in specific areas such as ports can recover costs within a few years by improving operational efficiency and reducing labor costs, with a limited and controllable coverage area.

However, for urban transportation, the cost of intelligent infrastructure transformation is extremely high. A smart expressway with high-level V2X capabilities requires the deployment of fiber-optic networks, millimeter-wave radars, high-definition cameras, and high-density RSUs, with construction costs per kilometer potentially reaching millions or even higher. Achieving this level of coverage nationwide would impose enormous pressure on financial investment and operational maintenance.

In addition to infrastructure construction costs, the insufficient penetration rate of onboard terminals is also a serious constraint. This creates a "chicken-and-egg" problem: if roadside facilities are incomplete, vehicle owners have no incentive to purchase expensive vehicle networking modules; conversely, if there are insufficient intelligent vehicles on the road, expensive roadside equipment will remain idle and unable to achieve scale effects.

Although the penetration rate of intelligent driving passenger vehicles in China is rapidly increasing, the proportion of vehicles truly equipped with comprehensive V2X communication capabilities remains low. This mismatch in hardware and software deployment results in the overall social benefits of V2X being insignificant at this stage.

Cross-industry standardized collaboration is also a long-standing bottleneck. The implementation of V2X requires deep cross-border cooperation among multiple industries, including automotive, communications, traffic management, and surveying and mapping. Each industry has its own standard system and interest demands.

Traffic authorities focus on road safety and smoothness, while automakers focus on vehicle driving experience and system uniqueness. Regarding data-sharing mechanisms, how to break down industry barriers, establish unified cloud control platform interface specifications, and achieve cross-regional and cross-brand compatibility and interoperability is still in its early stages.

If the roadside system constructed in a city cannot serve intelligent vehicles of all brands, the social value of such infrastructure will be significantly diminished.

Final Thoughts

V2X technology, widely discussed since the early days of autonomous driving, seems to have lost some of its practicality in the era of widespread standalone intelligence. However, in restricted areas, V2X still has its unique applications. Zhineng Jia Zui Qian Yan believes that V2X may explore more possibilities in restricted scenarios in the future, but the ultimate technological path for autonomous driving will likely be standalone intelligence.

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