AIDL Weekly Issue #5 - Special Issue on Self-Driving Cars

Editorial

Intel/Mobileye big deal, more Waymo/Uber drama, etc. - yet another big week for self-driving cars! It's not hyperbole to say that self-driving cars represent one of largest market-size application for A.I. The jockeying for positions had been happening for a while and won't abate anytime soon. Intel largely missed the boat on mobile and is determined not to miss it on A.I. and autonomous vehicles. There's a subsystem race going on in the h/w and s/w space to solve all the myriad problems.

At the highest level, a successful architecture would need to at least understand:

  • Where am I (car) and where am I going? Need maps, GPS, odometry data.
  • What's around me based on my sensors? Need car sensors - LIDAR, camera, ultrasound, audio, infrared, etc. Need low-level intelligence / classifiers on each of those signals to identify and make sense of road signs, humans, pets, random objects on the street
  • What's around me based on external telemetry data? Need other car-related positioning and odometry data, weather data, traffic pattern data
  • How do I make sense of what's around me, what other objects are doing and whether I'm doing the right actions? A brain that takes internal sensor data and external telemetry data, makes sense of them and outputs an action. This is an oversimplification and is inherently a really tough challenge. There are so many corner and non-corner cases to account for. No company wants to own the first self-driving car that kills a pedestrian. How does the algorithm weigh navigation decisions in an unavoidable accident scenario where you could hit one group of pedestrians or another?
  • How do I train car to be smarter over time? Need phone home feature to a remote human operator if car can't decide what to do, generating training data
  • Etc.

This isn't meant to be exhaustive, but as you can see, the moment we start thinking about all the things a human driver does in navigation and in response to other moving blobs on the street, it becomes incredibly hard to create a driving machine replica. We suspect there will be multiple waves of innovation here over time, along the dimensions of better sensors, more types of telemetry data, better cost curve, and better brain.

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