Why Waze shows you the past

As usual, when driving back home during rush hour, I turned on Waze to get insights on the road conditions.  After a few minutes Waze diverted me to a side road to avoid a traffic congestion.  However as I looked at the highway, traffic was flowing freely while I was stuck in a traffic light on the diversion.  It then dawned on me that the success of Waze in directing people to the diversion, means that Waze is blind to the changing conditions on the highway.

Various solutions to this problem are possible.  One example is A/B testing, i.e. having some users diverted while others are not diverted.  In this manner Waze can keep up with traffic conditions, but at the expense of its users and it risks loosing its users trust if they are used as experimental pawns.  Another example is reducing the persistence time of events, i.e. in the absence of active reports or driving speeds Waze reverts back to normal conditions.

While the consequences of this in Waze are mainly loss of time, a future with autonomous cars may be more complicated.  Autonomous vehicles will pass through three phases

  • a minority of autonomous cars and limited functionality
  • a majority of autonomous cars and car to car and car to device communications
  • only autonomous cars allowed on roads

Its reasonable to assume that there will be at most a handful of car operating systems and probably all will meet some basic criteria.  This means that the diversity of driving patterns we see on the roads today will disappear. So we have a situation of similar objects with many degrees of freedom and interacting in a non linear way.  In physics such system are invariably chaotic.  A simple way to understand the complexity of the system consider a single road.  When the distance between cars is large and there is no interaction the traffic flows.  However as more cars populate the road they start to interact.   When one car brakes so do all the others.  We see this even in human driving, as highways become congested the traffic is erratic even without visible obstructions.  For autonomous cars this will be accentuated since all cars have the same underlying logic, so certain situations are prone to trouble.  Since this is a road specific and multi car scenario, it requires extensive and accurate simulations to create safeguards in the car operating system which identify and change the interaction rules to delay the onset of chaos. These situations will be exasperated in mixed driving conditions when humans try to take advantage of the predefined rules of the autonomous vehicles.  For example, if an autonomous car always keeps it distance, there will always be a human driver cutting in, which will push the autonomous car further back in line.  The mixed driving period will be challenging in every aspect as even innocent behavior can have far reaching consequences raising questions of liability and fault.





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