V2X (Vehicle-to-Everything) is a sophisticated systems engineering approach that breaks down the 'information silos' of vehicles. It achieves a comprehensive perspective beyond individual vehicle perception through high-frequency interactions between roadside equipment and in-vehicle terminals.

From a technical architecture standpoint, V2X primarily relies on the deep integration of key components such as 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 is currently predominantly deployed in restricted areas like ports, mining zones, or specific logistics parks. Its widespread adoption on complex urban public roads still faces significant challenges.

Deterministic Nature and Technical Closed-Loop in Restricted Areas

Ports and other restricted areas are ideal pioneering zones for V2X due to their highly controllable environments. Within these enclosed areas, vehicle routes are generally predefined, and operational processes are highly standardized.

Unlike urban streets, where vehicles may suddenly travel in the wrong direction, pedestrians may abruptly cross the road, and small animals may appear unpredictably, traffic participants in ports are primarily standardized container trucks or automated guided vehicles.

This simplified physical environment greatly reduces the difficulty of system perception. Roadside units can be deployed at high densities at key nodes, utilizing high-definition cameras and LiDAR to provide comprehensive coverage of local areas.

This 'bird's-eye view' effectively addresses common line-of-sight obstructions in container yards. Through 'blind spot filling' by roadside sensors, vehicles can proactively obtain real-time conditions around corners, enabling smoother acceleration and deceleration control.

In fact, V2X in ports and other restricted areas has now advanced to stages of collaborative decision-making and even collaborative control. In these regions, raw data collected by roadside equipment undergoes real-time processing via edge computing platforms, directly generating specific driving recommendations that are issued to vehicles.

Given that vehicle speeds within ports are relatively low, typically maintained at 20 to 30 kilometers per hour, this provides ample time for system computation and response. Simultaneously, port operational vehicles generally adopt unified wire-controlled chassis and electronic-electrical architectures, enabling precise execution of control instructions issued by roadside equipment by vehicle actuators.

In this model, roadside facilities not only extend perception capabilities but also become part of vehicle 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 regions possess a clear commercial closed-loop logic. Operators of ports and other restricted areas, as the primary investors, are also the direct beneficiaries of technological applications. By implementing unmanned loading/unloading and horizontal transportation through V2X, significant reductions in labor costs can be achieved, along with enhanced 24/7 operational capabilities and decreased economic losses from safety incidents.

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

Additionally, the communication environment in these regions is relatively pure, allowing for the use of privately deployed dedicated networks. This avoids data packet loss or latency fluctuations caused by signal congestion in public base stations, ensuring millisecond-level stability in data interactions.

Long-Tail Dilemmas and Perception Gaps on Public Roads

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

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

Although roadside facilities can provide additional perceptual data, processing this vast amount of information during high-speed travel and fusing it in real-time with data from onboard sensors presents a highly 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 host vehicle and filter out background noise.

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

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

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

Communication delays are even more critical in high-speed scenarios. Although 5G technology provides high bandwidth, channel latency and jitter remain difficult to avoid during cross-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 intent of V2X is to enable roads to inform vehicles of 'invisible risks,' but if there is a slight deviation in the timing of roadside instructions, vehicles 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 transportation 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, V2X is seen by many developers as merely 'icing on the cake' rather than 'providing essential support when most needed.' 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 presents another significant obstacle to the large-scale adoption 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 internal agreements within the operating entity facilitating loss-sharing.

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 exceptionally 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 an accident occurs because a roadside unit provides an incorrect 'no vehicle ahead' signal, causing the vehicle to accelerate at an intersection and collide with a violating vehicle. Should the law penalize the vehicle manufacturer or the operating entity of the roadside facility?

In current judicial practice, there is often a lack of clear legal basis for joint liability arising from technical failures. 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, thereby 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 for actuarial pricing, then large-scale promotion of V2X implies 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 substantial 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 investigations. To fairly assign responsibility, the system needs to record vast amounts of sensor data, akin to a 'black box' mechanism.

However, the volume of data on public roads is tens of millions of times greater than that in restricted areas. How to reconstruct the true logic of an accident from this vast bitstream 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, out of risk aversion, to conduct small-scale pilots in restricted areas 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 roads, expensive roadside equipment remains idle and cannot 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/mapping. Each industry has its own standard system and interest demands.

Traffic authorities are concerned with road safety and smooth traffic flow, while automakers focus on driving experience and system uniqueness. Regarding data-sharing mechanisms, breaking down industry barriers, establishing unified cloud control platform interface specifications, and achieving cross-regional and cross-brand compatibility remain in their infancy.

If the roadside systems constructed in one city cannot serve intelligent vehicles of all brands, the social value of such infrastructure is significantly diminished.

Final Thoughts

V2X, a direction widely discussed since the early days of autonomous driving and seemingly losing practicality with the popularization of standalone intelligence, still has its place in restricted areas. Zhijia Zui Qianyan believes that V2X may explore more possibilities in restricted scenarios in the future, but the ultimate technological path for autonomous driving will ultimately be standalone intelligence.

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