An Unintuitive Take on Data Augmentation for Self-Driving Cars
Forgive me hijackjng a self-driving ML discussion to ask a novice question on self-driving — “stateful” vs “stateless”, and how can state be used safely?<p>Specifically I mean pre-determined saved&#x2F;downloaded knowledge about the current route being driven, or your specific instantaneous location. Anything from annotated maps, to LIDAR scans.<p>It seems like the paradox is that stateful algorithms can dramatically improve performance by factoring in data that real drivers benefit from tremendously, namely, familiarity with the road.<p>However detecting when known state is violated due to some temporal change (road work, accident, natural disaster), and being able to shift back into “general flight” rules, seems in some ways to be even trickier than not using historical state in the first place.<p>So you need an algorithm that can learn to drive better than a human, without having basically any contextual knowledge of the road it’s driving on. In other words the algorithm needs to be a better driver the first time it ever goes down a road than a human who has driven the road for years.<p>As a human, familiarity with the road makes a tremendous difference for how I drive it. I generally drive the same roads 80% of the time (I.e. commuting) and knowing what to expect around each bend absolutely changes how I drive the road, even down to where the speed traps will be.<p>Any ML driving solution that depends on super-fancy stateful pre-scans of the environment seem fundamentally flawed. If you can’t drive a road safely that isn’t pre-scanned in hidef LIDAR for instance, I don’t suppose you can safely drive that road on an arbitrary Monday. Maybe solutions like this were never even attempted, but certainly some amount of statefulness is inherent in some of the commercial solutions out there (Supercruise?)<p>So what kind of state can you use safely? First thought was basics like speed limit, number of lanes, type of road surface, and your algorithm may have predictions of upcoming changes being constantly weighted against the current assessment of the present state.<p>So maybe the fundamental rule is no hard-coded state that can’t be reliably detected in real-time to the point where at some point a real-time signal is able to over-ride the programmed state?<p>But then it seems that inevitably you get to the point where your real-time classifiers are basically running the show anyway so are you back to - what good is a Map anyway?<p>Would love interested but accessible readings on the subject, or maybe it’s so off base that it’s not really part of the discussion?