Once a self-driving car has a breakout success, everyone will quickly know what kind of sensor package it has, what kinds of compute hardware, and general features of the machine learning approach and tradeoffs like mapping vs on-the-fly interpretation. Knowing these things will make it much quicker to catch up compared to when everyone is still exploring for a solution.
The hardware/software demands are well known. Best practices have been established. The secret sauce is in getting the whole orchestra to harmonize, and in optimizing to get through the insane workload.
The biggest hurdles are at the start, but any company that can demo an autonomous vehicle that can, on average, drive a few miles in urban traffic without getting hung up is off and running. After those initial big hurdles, it's small hurdles stretching off further than the eye can see.
Uber had never really learned the first big hurdles. They scaled too big, too fast, and the whole operation was a clusterfuck. The rumour is that Uber was having trouble getting simulation working for them. So early on with Uber, driving around Philly, they were disengaging every block or two, and 2 years later they weren't doing much better.
In the wake of their accident they've had some time to reevaluate everything, and we'll see if they've actually managed to sort out their problems.
Hi Fricken, I'm a reporter doing some research on self-driving cars, in particular some of the lingering technical and safety challenges to commercial deployment. I've enjoyed many of your posts on the topic. I was hoping to talk with you on-background, and I'd really appreciate your help. Happy to explain more if you wouldn't mind connecting. heather.somerville@thomsonreuters.com. Thank you.
Seems to me that the data collected in the miles driven is an essential component in addition to the sensor package and other hardware. Creating a hardware package is the easy thing, collecting the data doesn't seem like it'd be easier than starting from scratch.
