Autonomous DeLorean drives sideways to move forward

Post by petschge » Sun, Dec 22, 2019, 10:22 AM UTC

OnlineGladiator wrote: post #62

tempestn wrote: post #53

Earlier quoted context omitted.

Another exception of a sort, and I believe the one this research is meant to address, is when the drift is initiated unintentionally. Say you hit a patch of black ice mid-corner, for example. This kind of work can expand the envelop of the control system to encompass these scenarios.

I don't think this is true. If you hit black ice and the tire skids, the problem is you just have no friction. Modern traction control systems already account for this as best they can - I don't see how this model would help any more except for trying to go faster through patches of black ice. And even then, drifting inherently requires traction in order to modulate the throttle and control the vehicle. If you can't…

Post by OnlineGladiator » Sun, Dec 22, 2019, 10:46 AM UTC

LoSboccacc wrote: post #72

OnlineGladiator wrote: post #70

Earlier quoted context omitted.

You're assuming Autopilot can't currently account for losing traction (this is probably true). But unexpectedly hitting a patch of black ice won't favor one model over the other - in either case you'll use the same methods to slow down. The only difference would be if you also had to avoid an object some distance ahead, and the drift model helped you maneuver around it by applying the throttle. It's extremely difficu…

> contrive a scenario pretty easy actually. car ahead swerve to avoid an obstacle. autopilot don't see the obstacle until the car ahead swerve. autopilot has to change lane to avoid obstacle. if it has recently snow some lane will have different traction than others. this is what happens on most of these pileup videos you see on youtube, so while not common (there are a handful a year after all) they aren't just hypo…

Post by lecarore » Sun, Dec 22, 2019, 10:51 AM UTC

Post by wongarsu » Sun, Dec 22, 2019, 10:57 AM UTC

charliepark wrote: post #39

Animats wrote: post #6

They did most of that back in 2015.[1] This is version 2. It's interesting what's happening as the control theory people get into machine learning. The controls people don't typically run a neural net as a controller. They use the trained net as a tool for building a controller with known continuity properties. The trouble with pure neural net controllers is that they sometimes do something totally bogus for some dat…

> "The trouble with pure neural net controllers is that they sometimes do something totally bogus for some data point within the normal input space." A friend's theory on Google Maps is that it sometimes intentionally sends you down a non-optimal path in order to gather data on alternate routes. This friend isn't in the ML space, so this is just their anecdotal observation. Are they right, then?

Post by LoSboccacc » Sun, Dec 22, 2019, 11:18 AM UTC

OnlineGladiator wrote: post #73

LoSboccacc wrote: post #72

Earlier quoted context omitted.

> contrive a scenario pretty easy actually. car ahead swerve to avoid an obstacle. autopilot don't see the obstacle until the car ahead swerve. autopilot has to change lane to avoid obstacle. if it has recently snow some lane will have different traction than others. this is what happens on most of these pileup videos you see on youtube, so while not common (there are a handful a year after all) they aren't just hypo…

You're right - I guess it's not that hard to imagine that scenario. But how would the car know which lanes have traction? If the car can reliably detect black ice it can avoid it without ever having to slip/skid.