Autonomous driving perception technology has undergone significant changes over the past few years, evolving from initial 2D image detection to bird's-eye view projections and now to the highly discussed occupancy networks. These advancements in perception technology have progressively strengthened the capabilities of autonomous driving.

The core logic of occupancy networks involves dividing the 3D space around a vehicle into countless tiny voxels and predicting whether each voxel is occupied by an object or remains free. This method breaks away from the reliance on traditional perception algorithms that depend on "bounding boxes" and instead restores the true appearance of the physical world through detailed geometric descriptions. However, as this technology enters the stage of large-scale industrial deployment, a series of underlying issues have surfaced.

The Heavy Burden of Hardware Computational Power and Memory Consumption

In its pursuit of environmental representation precision, occupancy networks first face the problem of explosive growth in computational resources. Traditional perception tasks only output the coordinates and attributes of a small number of targets, whereas occupancy networks require dense inference predictions across the entire 3D grid.

This dense voxel representation inherently possesses cubic-level complexity. If the system attempts to double the perception range or enhance the perception precision by a factor of two, the required computational load and memory usage will surge exponentially.

Current in-vehicle computing platforms struggle to support the operation of full-scale, dense occupancy networks in terms of computational power reserves. To achieve real-time perception output with limited chip resources, many technical solutions are forced to compromise on resolution.

However, lower resolutions can lead to blurred object edges and even the loss of critical information about small obstacles. Although techniques such as Tri-Perspective View (TPV) or Sparse Occupancy Networks (SparseOcc) have been proposed to reduce hardware burdens through projection compression or processing only non-empty regions, these simplified models still suffer from information loss or inference delays when dealing with highly complex urban traffic intersections.

In practical testing, many dense occupancy network models can only maintain extremely low frame rates on high-performance computing platforms, far from meeting the response speed required for safe driving.

The limitation of memory bandwidth is equally an invisible killer hindering the technology's deployment. The frequent transfer of 3D feature maps between different layers of neural networks places extremely high demands on the throughput of the in-vehicle bus.

When a vehicle is traveling quickly in complex urban environments, the perception system must process massive amounts of data from multiple cameras and sensors within milliseconds. Any minor delay caused by computational resource scheduling can lead to the failure of final decision-making.

This extreme reliance on computational power and bandwidth means that, at this stage, occupancy networks are still more inclined to appear in high-end vehicle models equipped with top-tier computational chips, making them difficult to popularize in ordinary mass-produced vehicles.

Scarcity and Precision Deviation of Ground Truth Annotations

The training of occupancy networks heavily relies on high-quality ground truth labels, meaning each 3D voxel must be accurately annotated with semantic categories. However, manually annotating such vast and fragmented data is virtually impossible.

The current industry standard is to use "4D automatic annotation" technology, which involves using data collection vehicles equipped with high-precision LiDAR to generate a set of ground truth data through the stacking of multiple frames of point clouds and offline algorithm optimization.

However, this ground truth data generated through automatic annotation is far from perfect.

LiDAR inherently has physical sampling limitations, with its point cloud density rapidly decreasing with distance. This means that, in distant regions, the ground truth voxels generated through automatic annotation are often very sparse and discontinuous, failing to provide sufficiently clear guidance for model training.

Additionally, during the process of multi-frame stacking, moving objects in the environment (such as driving cars or running pedestrians) can leave severe "motion blur" or "artifacts." Although technical solutions attempt to eliminate these interferences through time synchronization and motion compensation algorithms, in complex dynamic traffic flows, such annotation errors still cannot be completely eradicated, leading the model to learn incorrect geometric features.

The issue of semantic confusion during automatic annotation is also prominent.

In some irregular scenarios, LiDAR point clouds struggle to distinguish material properties. For example, dense roadside vegetation may be geometrically similar to brick walls, or low curbs may be confused with ground reflection signals.

If the ground truth data contains errors in these subtle differences, the model will develop severe judgment biases during inference. For an autonomous driving system, mistaking a cluster of traversable weeds for a solid wall may reduce driving efficiency, but mistaking a wall for weeds can pose a safety risk. This systemic bias originating from the annotation process remains a major obstacle for occupancy networks to achieve higher reliability.

Perception Instability Caused by Lack of Spatiotemporal Consistency

In real driving environments, perception results must be continuous and stable. However, current occupancy networks exhibit severe flickering when processing consecutive visual frames, a phenomenon known in academia as "spatiotemporal inconsistency."

The same obstacle may be predicted as occupied at one moment but suddenly disappear and then reappear in the next moment. This unstable output greatly confuses downstream planning and control systems, potentially causing illogical sudden braking or sharp steering maneuvers in the vehicle.

The root cause of spatiotemporal inconsistency lies in the model's insufficiently robust mechanism for fusing historical information. Although many algorithms attempt to smooth perception results by introducing time-series features, when the vehicle is moving quickly, the camera is shaking, or the lighting environment is changing dramatically, it becomes difficult to achieve precise spatial alignment between historical frame voxel features and the current frame. Minor coordinate transformation errors are amplified in the 3D grid, leading to misalignments or double images in the prediction map.

This phenomenon is particularly evident when processing dynamic objects, where the model often struggles to capture the precise boundaries of rapidly moving objects in real-time, causing the predicted "occupancy flow" to lag behind the actual object displacement.

This instability is also reflected in the handling of occlusion scenarios.

When an object is temporarily occluded by roadside vehicles or trees, the occupancy network should possess a certain "associative" ability to judge that the space remains occupied.

However, due to the lack of strong physical reasoning capabilities and long-term memory, many models immediately classify the occluded space as "free" or "unknown." This perceptual "fragmentation" not only threatens driving safety but also exposes the current shortcomings of deep learning models in understanding the continuity of the physical world.

Perception Blind Spots in Extreme Scenarios and Small Targets

Although occupancy networks are highly anticipated for solving "long-tail scenario" problems, they still exhibit significant vulnerabilities under certain physical extremes.

For instance, they may fail to capture thin, elongated objects such as streetlight poles, guardrail wires, and thin tree branches. Because the resolution of the voxel grid is preset and fixed, these small objects often occupy such a small volume proportion during voxelization that they are filtered out as background noise by the model or judged as discontinuous isolated points.

If a high-speed autonomous vehicle fails to identify a row of thin isolation guardrails from a distance, the consequences could be dire.

Another issue is the perception of "special materials," particularly transparent and highly reflective objects, such as glass walls, transparent guardrails, and mirrored surfaces, which pose significant challenges for virtually all visual perception algorithms.

Occupancy networks rely on multi-view feature matching to estimate depth and geometric structure, but the transparent nature of glass causes light to pass through directly, leading the model to mistakenly perceive the area ahead as passable empty space.

Even in systems equipped with LiDAR, laser beams may penetrate or reflect specularly, failing to obtain accurate distance data. This makes occupancy networks highly prone to severe perceptual illusions when facing modern glass curtain wall buildings or transparent sound barriers.

There is also an inherent conflict between the effective perception distance and accuracy.

As distance increases, the resolution of objects in camera images decreases, and depth estimation errors grow exponentially. In occupancy networks, voxel predictions at long distances often become very blurry and susceptible to interference from clutter in the sky or horizon, generating inexplicable "floating voxels."

Although these distant false obstacles may not immediately cause collisions, they can severely disrupt the vehicle's long-distance path planning, leading the system to frequently produce unnecessary deceleration.

Solving these deep geometric perception problems requires not only deeper networks but also more profound modeling and integration of optical and geometric physics principles.

Final Remarks

Although occupancy networks theoretically provide a more comprehensive and physically plausible means of environmental representation for autonomous driving, they still face significant technical challenges in terms of computational overhead, ground truth acquisition, spatiotemporal stability, and extreme geometric perception.

The existence of these problems requires that, in future research and development, we not only pursue more powerful model architectures but also pay greater attention to the depth of sensor fusion, the quality of automatic annotation, and the establishment of tighter physical constraints between perception and planning and control. Only by gradually overcoming these limitations can occupancy networks truly become a solid foundation for autonomous driving systems to safely navigate through large-scale, complex physical worlds.

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