Ambarella’s 2018 CV2 automotive chip was designed to combine two ways of seeing the road: monocular cameras could classify familiar objects at a distance, while stereo cameras could infer depth and identify obstacles by their shape—even when the object was unfamiliar. “Double vision” described that complementary perception, not two images shown to a driver.
What Ambarella meant by “double vision”
Announced in 2018, the Ambarella CV2 was an automotive system-on-chip (SoC) intended for advanced driver-assistance systems (ADAS) and autonomous vehicles. It brought computer vision, image processing, stereovision and 4Kp60 video encoding together in one automotive-qualified platform. Ambarella said its deep-neural-network performance was up to 20 times that of its earlier CV1 chip; that was a company claim, not a measure of complete vehicle capability. EE Times reported the announcement on March 28, 2018.
The two vision modes had different strengths. A monocular system processes images from a single camera view and can use a trained neural network to classify objects. Stereo vision compares images from a pair of cameras to estimate depth and reconstruct 3D shape. CV2 was designed to support both approaches in the same platform.
How monocular and stereo perception differ
| Question | Monocular vision | Stereo vision |
|---|---|---|
| What does it contribute? | Learned classification of objects in a single camera view. | Depth and 3D shape inferred by comparing paired camera views. |
| What was reported about range? | VisLab founder Alberto Broggi described classification of distant objects up to 180 meters. This is a statement reported in 2018, not a universal operating range. | Ambarella reported more than 150 meters of stereo obstacle-detection range on its EVA demonstration vehicle in 2018. |
| What about an unfamiliar object? | A classifier’s recognition depends on what it has learned to recognize. | Geometry can reveal an obstacle even if its specific type or shape is not recognized by a classifier. |
| What is the trade-off? | Uses one camera view for this perception mode; Ambarella’s 2018 announcement does not quantify its hardware cost relative to stereo. | Requires paired cameras and their calibration; Ambarella’s 2018 announcement does not quantify the added cost or complexity. |
Broggi summarized the monocular strength this way: “Monocular vision detects and classifies objects further in the distance — up to 180 meters away.” For stereo, he said: “Even when the cameras see an object with an unknown shape … stereo will get that.” Both statements were reported by EE Times in 2018 and should be understood as descriptions of the system’s intended capabilities, not guarantees for every road, weather condition or vehicle installation.
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Why combine the two approaches?
The point was complementarity, not a claim that either method was universally superior. A monocular classifier could identify a known object far ahead; stereo geometry could supply depth and flag an obstacle that the classifier did not recognize. Ambarella presented the combination as a form of redundancy: a failure to classify an object would not necessarily mean the vision system missed its physical presence.
That is useful context for comparing stereo with a monocular camera: stereo adds geometric depth information, while monocular processing can deliver learned classification at distance. The 2018 announcement does not establish that the combination alone made a vehicle autonomous or safe. It describes perception functions in an automotive computing platform, not a complete driving system.
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What the CV2 chip was designed to handle
Ambarella said CV2 could support four stereo cameras and four monocular cameras. It also supported 4Kp60 AVC/HEVC video encoding. The chip was reported to use Samsung’s 10-nanometer manufacturing process and consume approximately 4–5 watts. These are specifications reported in the 2018 announcement, not measurements of a finished vehicle’s total camera or computing-system power.
CV2 followed CV1 and was described as software-compatible with it. Ambarella planned to begin sampling CV2 in the second quarter of 2018, a historical schedule rather than an indication of present-day availability. The announcement targeted automotive manufacturers and Tier 1 suppliers integrating the chip into vehicles; CV2 was not presented as a consumer retail product.
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How Ambarella demonstrated the system in EVA
Ambarella’s EVA demonstration vehicle was a Lincoln MKZ fitted mostly with vision sensors, alongside Bosch front radar. Its camera arrangement used separate long- and short-range stereo groups:
- Long range: Two 4K (8-megapixel) sensors, separated by a 30-centimeter stereo baseline, with a 75-degree horizontal field of view.
- Short range: Four stereo cameras with 2-megapixel sensors, 10-centimeter baselines and fisheye lenses.
Ambarella reported more than 150 meters of stereo obstacle-detection range on EVA. Broggi also cited approximately 800–900 million 3D points per second from the long-range stereoscopic camera, compared with about 2 million points per second for lidar. That point-rate comparison is a 2018 statement attributed to Broggi; by itself, it does not establish that stereo perception was more accurate, reliable or useful than lidar.
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What “double vision” does—and does not—tell you
CV2’s central idea was to put neural-network perception and stereo geometry on one automotive chip, so each could contribute information the other might not provide. The figures and system details here describe Ambarella’s 2018 announcement. They do not establish whether CV2 is still commercially available, what later products may have replaced it, or whether any specific vehicle entered production with this configuration.
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