From an article in TechTalks by Ben Dickson.
Tesla has been a vocal champion for the pure vision-based approach to autonomous driving, and in this year’s Conference on Computer Vision and Pattern Recognition (CVPR), its chief AI scientist Andrej Karpathy explained why.
Speaking at CVPR 2021 Workshop on Autonomous Driving, Karpathy, who has been leading Tesla’s self-driving efforts in the past years, detailed how the company is developing deep learning systems that only need video input to make sense of the car’s surroundings. He also explained why Tesla is in the best position to make vision-based self-driving cars a reality.
A General Computer Vision System
Deep neural networks are one of the main components of the self-driving technology stack. Neural networks analyze on-car camera feeds for roads, signs, cars, obstacles, and people.
But deep learning can also make mistakes in detecting objects in images. This is why most self-driving car companies, including Alphabet subsidiary Waymo, use lidars, a device that creates 3D maps of the car’s surrounding by emitting laser beams in all directions. Lidars provided added information that can fill the gaps of the neural networks.
However, adding lidars to the self-driving stack comes with its own complications. “You have to pre-map the environment with the lidar, and then you have to create a high-definition map, and you have to insert all the lanes and how they connect and all the traffic lights,” Karpathy said. “And at test time, you are simply localizing to that map to drive around.”
It is extremely difficult to create a precise mapping of every location the self-driving car will be traveling. “It’s unscalable to collect, build, and maintain these high-definition lidar maps,” Karpathy said. “It would be extremely difficult to keep this infrastructure up to date.”
Tesla does not use lidars and high-definition maps in its self-driving stack. “Everything that happens, happens for the first time, in the car, based on the videos from the eight cameras that surround the car,” Karpathy said.
The self-driving technology must figure out where the lanes are, where the traffic lights are, what is their status, and which ones are relevant to the vehicle. And it must do all of this without having any predefined information about the roads it is navigating.
Karpathy acknowledged that vision-based autonomous driving is technically more difficult because it requires neural networks that function incredibly well based on the video feeds only. “But once you actually get it to work, it’s a general vision system, and can principally be deployed anywhere on earth,” he said.
For the complete article on why Tesla does not use lidar CLICK HERE.
