In the realm of autonomous driving, the pure vision approach has long garnered significant recognition. Binocular cameras, which emulate the functioning of human eyes, are capable of reconstructing 3D information through parallax calculations. This gives them a distinct edge in terms of distance judgment and spatial perception. Consequently, they find widespread application in pure vision systems.

However, during actual deployment in the real world, certain manufacturers have transcended binocular systems and opted for trinocular camera solutions. This raises the question: Why opt for trinocular cameras when binocular systems are already in use?

How Do Binocular Cameras Perceive Depth?

While binocular cameras can theoretically reconstruct 3D scenes and determine depth, trinocular designs aim to overcome the shortcomings of binocular systems by offering enhanced flexibility.

Before delving into trinocular systems, it's essential to first grasp how binocular cameras operate. Onboard binocular cameras mimic human visual mechanisms by employing two cameras placed slightly apart to simultaneously capture the same scene. Depth information is then derived by comparing the disparities between the two images.

Unlike monocular cameras, which can only discern shapes and colors or rely on learned patterns to estimate distances, binocular systems can directly quantify the distance of objects from the vehicle. This capability is of paramount importance for tasks such as collision risk assessment, precise parking, and obstacle avoidance.

While monocular vision provides a mere 2D representation, binocular systems enable the system to perceive depth in the real world. This is why binocular cameras are favored over monocular ones in numerous autonomous driving and driver-assistance scenarios.

Binocular systems depend on image features, such as texture, edges, and contrast, to match corresponding points between the left and right images. If the scene lacks diversity, has a uniform texture, or is poorly lit, matching can fail, rendering depth estimation unreliable.

Depth calculation in binocular systems is also influenced by parallax size. If the target is situated far from the camera, the parallax becomes exceedingly small—potentially less than a pixel—leading to substantial errors in depth estimation.

Moreover, binocular systems necessitate precise 'baseline' calibration, which refers to the relative distance and alignment between the two cameras. This makes calibration and synchronization a challenging task.

Hence, while binocular systems are more robust and direct than monocular ones, they still exhibit deficiencies in certain scenarios, particularly in poor lighting conditions, mixed distances, or when dealing with occlusions and indistinct textures.

Why Are Trinocular/Multi-Camera Systems Gaining Popularity?

With an increasing number of automakers adopting trinocular cameras, how are they designed, and what problems do they address? For autonomous driving, the 'multi-vision + multi-camera' design is geared towards enhancing the system's adaptability to complex environments. Visual perception systems can be broadly classified into three main approaches: monocular, binocular, and multi-camera (including trinocular, quad-camera, and surround-view multi-camera systems).

Trinocular cameras are not merely about adding an extra camera. They typically comprise a wide-angle camera (for close-range and peripheral monitoring), a medium-view camera (covering conventional fields of view at medium distances), and a telephoto camera (with a narrow field of view for long-distance observation and detecting distant targets).

This configuration enables trinocular cameras to maintain robust perception across 'near-mid-far' distances. They can detect sudden obstacles or pedestrians up close while simultaneously monitoring vehicles or road conditions far ahead, ensuring safety and swift response times during high-speed or long-distance driving.

Multi-camera systems, such as trinocular ones, also capitalize on information correlation across different perspectives. The system can cross-verify the existence and position of targets through multi-view consistency. If one perspective is obstructed, reflects light poorly, or has an unfavorable angle, another camera can provide clearer observations. This occlusion compensation capability, based on multiple perspectives, reduces reliance on a single path or viewpoint, minimizing missed detections and making overall perception more stable and reliable.

Furthermore, employing multiple cameras with synchronized data collection provides a more comprehensive and stable understanding of the environment, offering higher coverage of lanes, obstacles, pedestrians, surrounding vehicles, and nearby and distant objects. For autonomous driving (especially mid- to high-level systems), such redundancy and coverage are of utmost importance.

Key Advantages of Trinocular Over Binocular Systems

1) More Comprehensive Distance and Field-of-View Coverage

Binocular systems typically feature a fixed baseline and focal length/field of view, making them suitable for medium-distance depth estimation. However, relying solely on binocular systems makes it challenging to cover all distances and fields of view in dynamic scenarios like 'high-speed long-distance + urban close-range + peripheral blind spots.'

Trinocular systems, with their 'wide-angle + medium-focus + telephoto' combination, can clearly perceive pedestrians and crossing vehicles up close, monitor general traffic conditions at medium distances, and detect distant vehicles, objects, and road conditions far ahead. This combination renders the perception system more flexible and better suited for real-world driving scenarios where 'near and far conditions mix and changes can occur at any time.'

2) Enhanced Robustness

In real traffic environments, roads may be devoid of vehicles, vehicles may be situated far away, or objects may be occluded. Relying solely on binocular stereo matching can lead to issues in low-texture, long-distance, poor lighting, or occluded scenarios.

Trinocular systems, with multiple perspectives/focal lengths, are better equipped to handle these challenging situations. If one lens fails to capture a clear view, another can step in. If one angle is occluded, medium-focus or telephoto lenses or wide-angle lenses can provide supplementary information. This redundancy across multiple visual paths/perspectives makes the system more stable overall.

3) Greater Flexibility in Depth Estimation Range and Accuracy

In binocular ranging, setting a wide baseline allows for measuring longer distances but compromises accuracy at close range. Conversely, a narrow baseline provides good close-range accuracy but struggles with long-distance measurements.

Trinocular cameras, with multiple camera groups, can utilize appropriate lenses/baselines/focal lengths for different distances, simultaneously achieving high close-range accuracy and long-distance detection. This flexibility is difficult for binocular systems to match.

4) Better Suited for Complex Scenarios

Autonomous driving demands not only clear perception but also sufficient redundancy, fail-safes, and error tolerance. Complex weather, lighting, occlusions, and potential hazards can cause failures in single-view systems. With only binocular cameras, if they fail (due to uniform texture, long distance, occlusion, glare, etc.), the system may miss obstacles.

Trinocular/multi-camera systems enhance overall stability and reliability through perspective redundancy, lens diversity, and algorithm fusion—critical for safety. This is especially advantageous for advanced autonomous driving (such as Level 3/4 vehicles).

What Challenges Do Trinocular Cameras Present?

Trinocular cameras necessitate precise control over the relative positions (baseline, angles), time synchronization, and lens distortion correction among the three cameras. Poor calibration or synchronization can introduce depth errors or alignment issues, which could be catastrophic in autonomous driving.

Multiple cameras also mean an increased volume of image data, necessitating multi-view fusion, depth calculation, and decision-making integration. This demands more powerful hardware, more complex algorithms, and real-time performance capabilities.

The design of trinocular cameras, with multiple lenses and varying focal lengths/fields of view, imposes stricter requirements on camera modules, lens arrangement, occlusion protection, and calibration, making them more expensive. For some low-cost vehicles, the cost may be prohibitive.

Therefore, while 'trinocular' systems offer advantages, 'more lenses do not necessarily equate to better performance.' A comprehensive evaluation based on required functions (driver assistance or advanced autonomous driving), cost, computational power, and reliability is essential.

Conclusion

Returning to the original question: Why opt for trinocular cameras when binocular systems are already in place?

The answer lies in the fact that while binocular systems can measure depth and provide stereo vision, they have blind spots and limitations, often performing inconsistently due to environmental, distance, texture, or calibration issues. Trinocular systems, with additional cameras and varying focal lengths/perspectives, fill these gaps, making the entire visual system more reliable and comprehensive in complex, real-world environments.

Trinocular systems are not merely about adding another camera; they represent a balanced compromise based on needs, scenarios, and cost/performance trade-offs.

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