Speaking the language of traffic

Vehicle on the intelligent traffic grid speak a language.  Let's list of language descriptors for the current generatio of navigation dvices:

The Relational Data Format of the traffic grid by Navteq, the various XML map definitions used on  the web, and the Wave protocol for traffic messaging between traffic devices.  What are the essential ingredients?  What is the object, where is it, and where is it going.  The Robocar knowledge of truth is a list of objects in its final map, the list describing the objects, where they are  and where they are going.  The entire robocar traffic network , including traffic signs follow this pattern.  A stop sign sits where it is and isn;t going anywhere, but a car may proceed through the intersection; the description format applies in both cases, even the traffic rules for the DMV use that format. 

The overall langague of traffic is simple, because the only applicatipon the language really supports is computing the next configuration of objects.  Autopbots are simple, they take the known array of objects, compute their own next position, go there and repeat, objects in traffic simply rearranging themselves. 

What is the math behind it all?
Well it is a predictive fileter, taking the object map at time t and computing an expected map at time t+1, including the objects own expected position.  Moving to a new position in the map requires taling the approopriat rule section of the rule book and overlaying that on the map to compute exclusion and inclusion zones.  But the zones themselves are staionary objects with positon, nothing more.  The math behind the predictive filter carrys both the predicted position of an object and an error term defining how much error is in that position.  The predicted position and the error term being updated whenever a measuremnt on the object arrives.   Generalized measurments of objects follow the same format.  These come from smart cameras, laser radar, GPS, communications, or digital mars. 

The outcome of all this real time mapping are the control signals to steering, brake and acceleator, as well as continual communication with other traffic objects.  So, other than a simple dashboard display, there is no real human involved.The key to this industry is understanding how the autbots can simply arrive a common conclusions without human interaction. 

• A common definition of traffic rules.  
• A shared description of vehicle and object types.  
• Common messaging between smart sensors and autobot navigators.

Now we can apply this analysis to today's cars with lane guidance and collicion avoidance; and make a HOT lane that support speeds of 95 MPH. At least all the components are there.  This alone would likely boost actual GDP growth up  1%, yet still requires a driver behind the wheel.  Even the novice driver can get to a safe stop from 95 MPH as long as the autobot safe emergency braking works.  With a driver behind the weheel, still, we can extend our HOT lanes with large BRT and cargo carriers, at high speeds.  We still have not really gone into any singularity; large cofiguration BRT already exist, and large configuration cargo has been proven.   So, with a simple tweak of the rules,  and little infrastruture, our HOT/BRT lanes would give us another 1% GDP boost.  That puts us up to 3.5% GDP, and likely keeps our nominal oil imports at 2002 levels.

What does it take to get this going?    It is happening,and fast.  Vehicle designers beware, control by wire steering, braking, and acceleration are a must have.  The rest can be added later with a black box upgrade. Ray LaHood has no choice but to support higher vehicle speeds and larger vehicle configurations on HOT/BRT lanes with road pricing;  using robocar technology.