This simple demonstration shows how three analog sensors can easily be integrated into a cognitive model that can be deployed in a simple device. In this case, we are accumulating three risk factors and controlling the length of time that the yellow light will remain on. The assumption is that collisions at an intersection can be reduced if the yellow light remains on longer giving the drivers longer time to react. The objective is also to keep traffic moving through the intersection without any extra delays if the risk factors are low.
This demonstration assumes that there are three analog sensors: One that detects the speed of the traffic, One that detects the volume of traffic, and one that measures weather conditions.
The speed detector measures average speed of the traffic going through the intersection. For example, suppose that the sensor detects the average speed for the last x number of vehicles between 0 and 100 MPH.
The traffic volume sensor counts vehicles per minute and keeps a running average. Assume it counts from 0 to 200 vehicles per minute.
The weather sensor detects weather conditions and it measures 0 (best conditions) for perfectly clear sunny day to 100 for snow, ice, 0 visibility (worst conditions).
In this demonstration we have set some thresholds such that if there is high volume, but the speed is almost 0, then there will be no increased risk and we won't increase the length of time for the yellow light.
Also, if there is almost no traffic (potentially at an off hour), and one car speeds through at 100 MPH, then we will also not increase the length of time for the yellow light.
In this system we are defining a model where the impact of speed in the overall risk is non-linear. A curve defines the increasing risk associated with speed. We are also defining the maximum impact of speed in the overall integration of speed, traffic volume and weather.
With traffic volume, we are defining a linear relationship as volume increases.
And also with the weather sensor, we will assume that as the weather degrades from 0 (best weather) to 50 (cloudy) that this will have no impact on the risk and therefore no impact on the length of time for the yellow light. When it degrades past 50 towards 100 (worst), then the risk will rise according to the S curve shown above.
In this demonstration we are showing two configuration parameters that might be set with DIP switches or potentiometers in a productized design: Base Time and Max Time. The Base Time value would be the time that the yellow light stayed on with no added risk. The Max Time would be the maximum time that the yellow light remained on with maximum risk detected.
In this demonstration, the maximum impact of speed and traffic volume has been set at design time. The same is true for the shape of the curves for the three sensors. All of these could be controlled with other configuration parameters.
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