Re: [Algorithms] Network prediction algorithms specific to racing?
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Re: [Algorithms] Network prediction algorithms specific to racing?
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From: Jamie F. <ja...@qu...> - 2010-04-16 09:38:42
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Jason Hughes wrote: [snip] > It does occur to me that it may not be possible to mathematically map > uniquely into track space easily, but an artist could very well use a > good UV layout tool to create a giant flowing UV shell that could at > least create a fairly good coordinate space for the whole track. > Positive in U means heading straight along the track, V would be the > lateral axis. There would definitely be discontinuities when branches > met, or when the player left the track (sometimes), but it could be done > with existing tools in human-aided fashion. The very same mapping could > also use that for helping AI drive, come to think of it. It's > effectively an artist-generated influence map at that point. We used this on PS1 for a racing game (which wasn't released) back in '98; although the hint was per track quad rather than in a texture. The track was divided into strips of quads, and each quad had a vector associated with it that the AI used as a hint for what it should be doing. Worked fine in the end, but testing and debugging was a pain, as you'd have to first find a problem (test and test until a car hits something that misbehaves), work out which vector(s) gave bad hints, then tweak them... then test and test and test to see if the problem had gone away. The plus side: it was fast; we had 30 racing cars on that little old PS1. Jamie > JH > > On 4/15/2010 6:47 PM, Blair Holloway wrote: >> Shawn Hargreaves wrote on his blog about having a track-space >> coordinate system in MotoGP: >> >> blogs.msdn.com/shawnhar/archive/2009/12/30/motogp-ai-coordinate-systems.aspx >> <http://blogs.msdn.com/shawnhar/archive/2009/12/30/motogp-ai-coordinate-systems.aspx> >> >> If you were to have the player's position and velocity transmitted in >> track space coordinates instead of world space, you'd be able to do >> dead reckoning in that space entirely - i.e. reckon forward based on >> the player's local track velocity rather than their world space >> velocity. As long as the player is on the track and not, say, driving >> perpendicular to it, the player would always interpolate along the >> track, even on corners. >> >> Corners are likely to be, quite literally, one of your corner cases, >> due to players needing to slow down to corner correctly - something >> the reckoning/interpolation would need to take into account. >> Additionally, if the player is driving fast diagonally across the >> track, there may be cases where he would be reckoned outside of the >> track, in which case you might need to detect those cases and switch >> to a world space prediction method. >> >> - Blair >> >> On Fri, Apr 16, 2010 at 7:50 AM, Megan Fox <sha...@gm... >> <mailto:sha...@gm...>> wrote: >> >> At present, we have a fairly terrible scheme that amounts to no real >> rectification or prediction AND a dead-space that allows out-of-sync >> conditions to get around how jumpy it would be without it. Not so >> good. I was pulled off my other tasks and re-assigned to make it not >> suck / take it to polished instead of prototype, basically. >> >> My suspicion is that dead reckoning (ie. like you say, simple >> extrapolation from last known pos/vel) will be "ok", but that it will >> tend to not produce the smooth arcs I'd like to see in a cart racer. >> I'm also not convinced that a second or third degree prediction from >> sampling the prior positions would be anywhere near usable if the >> player ever deviated from smooth arc driving. So, just thought I'd >> see if there were any particular known-better-case solutions, given >> that it's a well-trodden domain. >> >> On Thu, Apr 15, 2010 at 3:25 PM, Jason Hughes >> <jh...@st... <mailto:jh...@st...>> >> wrote: >> > I guess there are two aspects of this: prediction and visual >> rectification. >> > >> > We've been doing a networked basketball game recently, where one >> team is >> > entirely controlled remotely. Even with fairly high latency, slow >> > moving objects (like players) will look okay using a smooth >> > rectification algorithm, but they're a little delayed from >> real-time. >> > High velocity objects (ie. bullets or ballistic objects) are pretty >> > easy to sync as well, provided they are not controlled or >> intelligent. >> > The main issues we've had thus far involve rectifying the ball >> physical >> > state after a collision with a player, since that's often >> incorrect and >> > delayed. Luckily, most collisions in basketball games result in the >> > ball being grabbed, so this isn't always an issue. For >> reference, our >> > simple rectification code is X = Lerp(X, X', f), where f could vary >> > depending on the latency and current error. Spline >> rectification would >> > be nicer, and we might implement that, though it may be overkill >> for us. >> > >> > I haven't tried prediction beyond simple linear extrapolation >> based on >> > the last single sample (position, velocity, orientation). 1st order >> > prediction would likely close most of the gaps for generally smooth >> > functions like position and orientation, but for velocity, I'm >> not sure >> > it would help in our case, since the velocity is basically joystick >> > angle for us and is generally discontinuous. >> > >> > Sorry I can't be more help with racing specifically. Out of >> curiosity, >> > what have you tried and how did it work? >> > >> > Thanks, >> > JH >> > >> > Jason Hughes >> > President >> > Steel Penny Games, Inc. >> > Austin, TX >> > >> > >> > On 4/15/2010 3:34 PM, Megan Fox wrote: >> >> I'm currently fiddling with dead reckoning for a racing game I'm >> >> working on, but this seems like a specific enough domain that there >> >> ought to be specific-to-racing prediction approaches. We have a >> >> fairly good idea of what the player is doing (trying to stay on the >> >> track), and that would seem to give more data that isn't really >> being >> >> exploited with standard approaches - but googling isn't turning up >> >> much. >> >> >> >> Do such algorithms exist? Or, in practice, are the same approaches >> >> used in FPSes et al still the ones to use here? >> >> >> >> >> > >> > >> ------------------------------------------------------------------------------ >> > Download Intel® Parallel Studio Eval >> > Try the new software tools for yourself. Speed compiling, find bugs >> > proactively, and fine-tune applications for parallel performance. >> > See why Intel Parallel Studio got high marks during beta. >> > http://p.sf.net/sfu/intel-sw-dev >> > _______________________________________________ >> > GDAlgorithms-list mailing list >> > GDA...@li... >> <mailto:GDA...@li...> >> > https://lists.sourceforge.net/lists/listinfo/gdalgorithms-list >> > Archives: >> > >> http://sourceforge.net/mailarchive/forum.php?forum_name=gdalgorithms-list >> > >> >> >> >> -- >> Megan Fox >> http://www.shalinor.com/ |
