How Tesla's Autopilot works

Recently, Andrej Karpathy, Head of Artificial Intelligence (AI) on Tesla’s Autopilot Team, offered several interesting insights on how Autopilot works in a video. First, he detailed how Tesla trains Autopilot with its customer-collected data feed using long tail examples like stop signs painted on buildings or those occluded by tree branches. To improve Autopilot’s reaction to these “corner cases”, Tesla sends a software detector to its 800,000+ vehicle fleet to identify images of, say, occluded stop signs. In contrast, with fleets of hundreds instead of hundreds of thousands driving in several cities instead of nationwide, GM’s Cruise Automation and Waymo have limited access to data on corner cases in training their vehicles.

Karpathy also explained how Tesla is bridging the gap between cameras and LiDAR. We previously heard about an Autopilot update involving 3D video labeling as opposed to 2D image labeling, enabling faster, more accurate image detection and path planning - Tesla’s solution to the LiDAR gap. LiDAR recognizes images and assesses direct depth more accurately than do cameras. In a two-step indirect process, cameras take shots of images and software gauges depth by analyzing the pixels. Mistakes involving a few pixels can translate into meters or yards of inaccuracy. By labeling 3D videos of driving scenes, Tesla is compensating for the camera’s weakness as the primary image sensor in its vehicles.

Karpathy also discussed Tesla’s local mapping which includes much less detail than the high definition maps its competitors use. While a Waymo vehicle drives with preloaded information about the exact location of a stop sign, within centimeters of accuracy, a Tesla would detect only the presence of a stop sign somewhere in the vicinity. In addition to vague local maps and its camera-based approach, 3D video labeling separates Tesla from its competitors, enabling the recognition of corner cases in solving for full autonomy.

We believe Tesla’s approach is highly differentiated and will be almost impossible for a competitor to replicate. While autonomous driving is an extremely complex problem to solve, Tesla could enjoy a near-monopoly in autonomous ride hailing if it is successful.

 

Catherine Wood, CEO and CIO of ARK Invest pitched Tesla at the 2019 Sohn Hearts & Minds Conference.

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