Different self-driving strategies: Google and Mobileye

Google (Waymo) and Mobileye both are doing self-driving cars and they have different strategies,

	Google	Mobileye
Level	L4	L2 -> L3 -> L4
Strategy	Sensing surroundings Driving plan based on map and ML of good driving and the surrounding image of his car	Sensing surrounding objects Driving plan based on ML on map, position/movement of the objects in the surrounding and mostly-used tracks on the roads
Years of development	8 years (from approx. 2009)	18 years (from approx. 1999)
Interesting facts	2,777,585 km driven in autonomous mode (till June 2016) Test based on pre-programmed route data Total 14 collisions: 1 program caused, others cause by human error Not be able to spot potholes or a police officer signaling the car to stop	Mobileye has provide more 90% OEMs with its products The Tesla AutoPilot crash in 2016 was caused by Mobileye’s camera which, against a bright spring sky, failed to distinguish a large white 18-wheel truck and trailer crossing the highway
Year to reach L4	2020	2021

Autonomous Level 4 is high driving automation at which a driver is still in the car and he can overtake the autonomous system.

Waymo also detects objects but focuses more on its deep learning to mimic a good driver. However, a good driver can handle “new” and not-experienced situations safely, which is the rare cases in ML database. Without enough such rare cases, ML will never learn well. To collect such cases, it faces two problems,

ML resource will exponentially increase
It will never achieve zero-fatality rate with limited cases for ML because there are always cases AI driver does not see/learn

On the other hand, Mobileye sets the goal of zero-fatality rate for its system. The fatality rate for 100 million Km (in USA in 2015) is 0.7 . It is questionable if it is a realistic goal to have zero-fatality rate for autonomous drive.

Furthermore, Mobileye claims that its approach is different from Waymo’s. I think they have big overlaps.