In daily traffic operations, traffic control authorities may temporarily set up mobile traffic lights at intersections. This can be due to power system maintenance, occasional power supply failures, or traffic control measures necessitated by road construction. These mobile traffic lights differ significantly in physical appearance from the fixed traffic light towers we are used to, which are typically mounted high in the air. Mobile traffic lights usually consist of a base, battery pack, support pole, and signal lamps, with an overall height much lower than standard signal lights. Positioned directly on the ground, the height of their light-emitting units may only reach around a person's waist level or slightly above the engine hood of an ordinary car. In such situations, human drivers can rely on their experience to identify these lights. However, for intelligent driving systems that heavily rely on preset rules, geometric models, and the line-of-sight perception radius, this poses a significant challenge.

Obstruction Issues at the Physical Perception Layer and Sensor Fusion Mechanisms

The primary impact of mobile traffic lights on intelligent driving systems is their "atypical" spatial positioning. According to mainstream specifications in the traffic equipment market, mobile solar signal lights offer a wide range of adjustable installation heights. For instance, common TSU series devices can be adjusted from a collapsed height of about one meter to an extended height of over three meters. However, during emergency deployments triggered by power failures, the equipment is generally installed at a lower physical height for efficiency. When an autonomous vehicle follows a large bus or van into an intersection, the visual camera may easily lose continuous visual tracking of these low-lying signal lights due to the limited top-down angle and physical obstruction by the preceding vehicle.

As the core sensor for identifying the color characteristics of traffic lights, the camera's detection effectiveness is highly dependent on "line-of-sight accessibility." If the preceding vehicle blocks the key light-emitting area of the signal light, even if this obstruction covers only about 30% of the light-emitting surface, the autonomous driving system may determine a recognition failure due to the inability to extract a complete circular or arrow outline. In complex urban scenarios, visual distortion caused by large vehicle obstructions is not simply a matter of "not seeing." When the outline of a large vehicle fills the sensor's field of view, the autonomous driving system struggles to distinguish whether the object ahead is a static traffic facility or a moving trailer. To address this issue, companies like Didi, Huawei, and Tesla have proposed solutions that generally incorporate a weight allocation logic for multi-sensor fusion.

When the confidence level of the visual sensor declines due to obstruction, the autonomous driving system significantly increases the data weight of LiDAR and occupancy networks. Although LiDAR cannot directly "see" the color of traffic lights, it can emit laser pulses millions of times per second to construct a three-dimensional point cloud model of the intersection ahead. Even if the traffic light is partially obstructed, LiDAR can still perceive the presence of a physical entity with specific geometric characteristics at the center or edge of the intersection. The existence of this physical entity triggers the occupancy network algorithm at the bottom layer of the intelligent driving system. The occupancy network divides three-dimensional space into numerous tiny "voxel" cubes and directly determines whether these cubes are occupied by physical objects. This logic bypasses the pitfalls of "semantic recognition," meaning the system does not need to immediately identify what the low-lying object is; it only needs to know that there is an "impassable" obstacle, thereby establishing the first line of defense against collisions at the perception level.

When dealing with perception gaps caused by obstructions from preceding vehicles, some solutions also utilize "dynamic data augmentation" techniques. These techniques adjust the camera's light-sensing parameters in real-time through algorithms, attempting to extract weak color characteristic signals from complex backlight or high-glare backgrounds. For example, when a backlit environment occupies a large area of the lens's field of view, the system performs stepwise compensation for brightness and contrast in the local area suspected of containing traffic lights, ensuring that the color saturation of the light-emitting unit is sufficient to trigger the threshold of the classification neural network.

Handling Semantic Conflicts Between Map Prior Information and Real-Time Perception

For autonomous driving systems, the second core challenge posed by mobile traffic lights is the logical judgment of "real and false signals." In most vehicles with navigation-assisted driving functions enabled, the navigation map not only provides route guidance but also stores a large amount of static infrastructure information, known as "map priors." When a vehicle approaches an intersection, the system has already been informed in advance of the presence of a set of fixed light poles at that location based on map data. However, after a power outage, these fixed light poles go dark, and a temporary traffic light not recorded on the map appears on the ground.

In this situation, a rigorous "logical debate" occurs within the autonomous driving system. Traditional intelligent driving solutions may overlook real-time ground signals due to excessive reliance on map information, causing the vehicle to accelerate at a temporary red light. However, current autonomous driving systems, especially those adopting "light map" or "mapless" architectures, adhere to the core principle of "real-time perception as the leader, map as auxiliary reference." When the system detects that the signal lights marked on the map are invisible or extinguished, while the perception module captures a temporary traffic light on the ground, it automatically reduces the trust weight of map information.

This conflict resolution logic relies on Road Topology Reasoning (RCR) technology. The RCR network can, like humans, infer legal driving paths in real-time based on the geometric characteristics of the intersection, the intersection of lane markings, and the position of the intersection stop line. Even if the position of the mobile traffic light on the ground deviates from the standard coordinates on the map, the RCR network can recognize its regulatory role in the current spatiotemporal context. For example, if the temporary traffic light is placed at the center circle of the intersection, the system will determine the semantic signal of the object through a logical model trained by deep reinforcement learning, indicating that the current traffic flow is under temporary regulation.

To enhance the stability of this reasoning, autonomous driving systems employ a "crowdsourced updating" dynamic mechanism. If the first vehicle passing through the intersection detects a discrepancy between the map information and the actual road conditions (i.e., the fixed lights are off, and there is a temporary light on the ground), it uploads this discrepancy feature to a cloud server. The cloud-based large model rapidly processes these data from real traffic flows, generates a temporary "live layer," and distributes it to subsequent vehicles approaching the intersection. This mechanism enables subsequent vehicles to obtain information about the presence of mobile traffic lights at the intersection in advance through the temporary patch downloaded from the cloud, even if their vision is completely obstructed.

The determination of temporary traffic lights by autonomous driving systems undergoes several weighing stages. The first stage is perception consistency checking. The system compares whether the luminous signal captured by the camera coincides with the physical entity detected by LiDAR. If the camera sees a red light, but LiDAR indicates that the location is empty (possibly due to road reflections), the system filters out this false signal. The second stage is spatial logic verification. The intelligent driving system calculates whether the height of the detected signal light falls within a reasonable threshold. For ground-based mobile traffic lights, although their height is below the normal range, once identified as a traffic control facility, their priority is elevated to the highest level.

This shift toward "perception-led" indicates that autonomous driving systems are becoming increasingly similar to experienced drivers, no longer mechanically executing map instructions but possessing the foundation to adapt flexibly to on-site environments. This flexibility is crucial for ensuring driving safety at chaotic intersections after power outages.

Spatiotemporal Sequence Memory and Socialized Intention Reasoning

Of course, in scenarios with mobile traffic lights, there is an extreme case of object obstruction where the mobile traffic light is completely blocked. In such cases, the intelligent driving system does not immediately enter a "blind" state but instead introduces a memory mechanism based on spatiotemporal sequences. Simply put, the autonomous driving system does not rely solely on the current frame but also retrospectively examines perception results from the past few seconds or even tens of seconds.

If the vehicle has seen the signal state of the mobile traffic light when it was 50 meters away from the intersection, then even if the view disappears when approaching 20 meters from the intersection, the recurrent neural network (RNN) or Transformer model within the autonomous driving system will still retain the "hidden state" of the object. This state memory includes not only the object's position but also its last observed color. As long as the time span falls within the safety range set by the algorithm, the autonomous driving system will maintain its existing cognition of the traffic rules at the intersection and continuously update the relative coordinates of the traffic light in virtual space based on the vehicle's driving distance and speed.

Another solution, "socialized intention reasoning," can address this issue. In complex traffic flows, the signal state of traffic lights is reflected in the behavioral patterns of surrounding vehicles. When the autonomous driving system cannot see the traffic light, it begins to frequently observe the movements of preceding vehicles and the "collective choices" of surrounding traffic. For instance, the autonomous driving system monitors whether the brake lights of preceding vehicles frequently illuminate and whether they accelerate decisively or hesitate and move slowly when crossing the stop line.

Through this monitoring of other vehicles' behaviors, the autonomous driving system is essentially performing indirect signal inference. If all lanes ahead are queuing and lateral traffic flows begin to move, even if the vehicle cannot see the temporary traffic light placed on the ground at all, the system can infer that the current state must be a red light. This decision-making logic based on "social evidence" prompts the autonomous driving system to calculate an "environmental consistency probability." When observing preceding vehicles decelerating and the map indicating an intersection, the system adopts a conservative defensive driving strategy even in the absence of visual perception.

This socialized intention reasoning algorithm path includes two core steps. The first is trajectory prediction analysis. The autonomous driving system not only tracks the current position of preceding vehicles but also predicts their future 3 to 5-second motion trajectories. If the prediction shows that the preceding vehicle will come to a stop before the stop line, the system initiates a deceleration decision in advance. The second is multi-vehicle behavior aggregation. The system simultaneously analyzes the vehicle states in the left, middle, and right lanes. If vehicles in all three lanes exhibit signs of stopping, the certainty of the environmental signal significantly increases.

To achieve this goal, autonomous vehicles must possess powerful computational resources to process vast amounts of data in parallel. In Tesla's FSD system, the neural network not only learns how to recognize objects but also how to predict the content of the next frame of images. If the mobile traffic light that should have appeared in the next frame is blocked by a large vehicle, the model generates a predictive copy internally through existing spatiotemporal weights. Although this "mental supplementation" ability carries some risk of error, it is more reliable than simple signal interruption when dealing with short-term obstructions.

Final Words

The negative impact of mobile temporary traffic lights on autonomous driving systems is gradually being mitigated through collaborative evolution at the perception, map, and decision-making layers. Although these low-lying, easily obstructed devices are indeed a "weak spot" for autonomous driving at the current stage, with the maturation of occupancy networks, spatiotemporal sequence memory, and socialized reasoning technologies, intelligent driving vehicles will also acquire the ability to navigate safely in imperfect environments.

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