Systems and methods involving features of adaptive and/or autonomous traffic control

A system comprising:

• a traffic light controller that implements traffic control using at least one processor;
• non-transitory computer readable media having instructions stored thereon for implementing a neural network array, wherein the computer-readable instructions, upon execution by the at least one processor, cause the at least one processor to perform one or more operations;
• memory storage coupled to one or both of the traffic light controller and/or the neural network subsystem;
• communication input circuitry and/or communication output circuitry coupled to one or both of the traffic light controller and/or the neural network subsystem;
• and
• a plurality of sensor inputs provided to the system;
• wherein the system includes additional headroom to expand and/or upgrade the traffic light controller via addition of additional inputs, traffic object(s) neural network recognition training information and/or subcomponents, and/or control decision training information and/or subcomponents;
• and
• wherein the one or more operations include:
• utilizing higher levels of neural network recognition hierarchy to prioritize passage through the intersection on the basis of one or more of vehicle priority, passenger occupancy, vehicle size, and/or vehicle weight.

A system comprising:

• a traffic light controller that implements traffic control using at least one processor;
• non-transitory computer readable media having instructions stored thereon for implementing a neural array, wherein the computer-readable instructions, upon execution by the at least one processor, cause the at least one processor to:
• receive data regarding travel of traffic objects associated with an intersection;
• use a neural network to recognize traffic types associated with the intersection, the traffic types being composed of one or both of traffic context types and traffic category types;
• use the neural network to recognize traffic states associated with the intersection;
• use the neural network to process traffic flow decisions for the intersection as a function of prior traffic information regarding the intersection;
• and
• use the neural network to determine traffic flow for the intersection via recognition and use of the traffic flow decisions in providing an output that controls a traffic light system associated with the intersection;
• wherein higher levels of neural network recognition hierarchy are used to determine relative traffic object and pedestrian positions and speeds, which is used to select optimal traffic light sequences using a sequence of neural network recognition cycles.

A system comprising:

• at least one computer including at least one processor and non-transitory computer readable media;
• wherein the computer readable media include instructions for implementing a neural network array associated with the at least one computer, wherein the instructions, upon execution by at least one processor, cause the at least one processor to:
• receive data regarding travel of traffic objects associated with an intersection;
• recognize, using the neural network array, traffic types, the traffic types being composed of one or both of traffic context types and traffic category types;
• recognize, using the neural network array, states of the traffic;
• memorize, using the neural network array, prior traffic flow decisions as a function of prior experience;
• recognize and use, via the neural network array, the prior traffic flow decisions in providing an output that controls a traffic light system to achieve efficient traffic flow;
• and
• recognize the types of traffic using an array having one or more sensor inputs beyond inputs from video sensors and physical infrastructure including an infrared (IR) traffic input that provides:
• infrared signatures of detected traffic objects independent from adverse weather or light conditions for recognition of vehicle position and type;
• and
• detection of vehicle passenger occupancy which is used in higher level traffic flow decision making;
• and
• giving priority based on passenger-weighted incoming traffic.