At this stage all you need to install is the machine learning algorithm so it can watch and learn what real drivers do when faced with the conditions it's sensing.
Absolutely brilliant!
Absolutely brilliant!
It may contain some low-level classifiers for things like people and possibly other cars, but this is not strictly required and car manufacturers generally try to avoid it, because it is difficult to reason about. Traffic sign classification is another candidate, but again, at least the Google car probably pulls this from their precomputed map.
Finally, none of these classifiers are trained online, in the car, but always offline, likely on carefully selected data on some GPUs for many days or weeks.
The nightly reporting-home of Tesla's fleet shows how interconnectedness is introducing network effects into unexpected places. Sort of like how map quality is now driven–in part–by the user base.
"ALVINN is a forward-looking, vision based driving system that uses a neural network to learn the mapping between road images and appropriate vehicle turn radius. By watching a person drive for about 5 minutes, it can learn the relevant features required for driving[3]. It has successfully driven our testbed vehicles on unlined paved paths, jeep trails, lined city streets and interstate highways."
http://www.cs.cmu.edu/afs/cs/usr/tjochem/www/nhaa/navlab5_de...
The problem obviously is ... Privacy and more importantly - upload speeds to google servers. US internet infrastructure wouldn't cope with thousands of cars uploading crisp HD videos. Probably google infrastructure wouldnt cope either.
Elon and Tesla believe that their array of ultrasound sensors, GPS, and front facing camera will be sufficient without the addition of an expensive LIDAR roof mounted scanner. Google has under 100 self-driving vehicles in the field collecting data; Tesla is now making 2500 vehicles/week that will be collecting data.
My comment stands. The sensor networks are the same except for the Velodyne LIDAR device. Google has Street View data, Tesla will have a substantial fleet of vehicles collecting data towards improving their autopilot system.
Vast amount of mapping data do you no good if you have to test your scenarios in simulated environments (as Google requested they be allowed to do to the California DMV).
Reliably driving like the average driver is perfectly acceptable.
(On a side note, nothing is preventing the machine learning from learning when a driver's response results in accident, a system can learn from negative experiences as well)