Sensor based roadway traffic control system
Roadway intersection signaling solution is provided having systems and methods that can include a processor to identify a setting for roadway signaling. The setting can include predetermined phases to apply according to time intervals to control traffic objects at the intersection. The processor can identify data from a sensor on an object and an environmental condition at the roadway intersection. The processor can determine, based on the object and the environmental condition input into a machine learning (ML) model, a timing for the object to pass the roadway intersection. The processor can adjust, based on the timing for the object to pass the roadway intersection, the setting for signaling at the roadway intersection and generate an instruction to a signaling device at the roadway intersection based on the adjusted setting.
1 . A system comprising,
one or more processors, coupled with memory, to:
identify, from one or more sensors of a roadway intersection, sensor data corresponding to one or more objects moving within an area comprising the roadway intersection;
identify, based on the sensor data input into one or more machine learning (ML) models trained on a plurality of objects moving in a plurality of directions and according to a plurality of velocities within one or more areas comprising one or more roadway intersections, an object to be assisted in traversing the roadway intersection;
determine, from the sensor data, a predicted trajectory of the object and a predicted time duration for the object to traverse the roadway intersection;
determine, using a priority matrix mapping the predicted trajectory and the predicted time duration to one or more variations of control of one or more signals for one or more signaling devices of the roadway intersection, one or more signals to apply to the one or more signaling devices, wherein the one or more signals are determined based on a priority associated with a type of the object of a plurality of types of objects within the area;
generate one or more instructions for controlling the one or more signaling devices according to the one or more signals to assist the object in traversing the roadway intersection;
initiate, via a controller and responsive to the one or more instructions, actuation of one or more phases of the one or more signaling devices of the roadway intersection according to the time duration for the object to traverse the roadway intersection; and
verify, upon initiation of the actuation of the one or more phases and based on sensor data from the one or more sensors, whether the object has traversed the roadway intersection within the predicted time duration.
2 . The system of claim 1 , wherein the one or more processors are configured to:
determine, based on the sensor data, that the object is present within a zone of approach to the roadway intersection; and
determine, responsive to the object being present within the zone of approach, the one or more signals of the one or more signaling devices.
3 . The system of claim 1 , wherein the priority matrix is stored in the memory and is updated dynamically based on traffic density and frequency of a type of the object detected by the one or more sensors.
4 . The system of claim 1 , wherein the priority matrix stored in the memory comprises one or more weighted parameters for at least one of the object, the predicted trajectory, or the predicted time duration, and wherein the one or more processors are configured to:
determine the one or more weighted parameters based on data from the one or more sensors that indicates at least one of a type of the object, a velocity of the object, or an occupancy of objects within the area; and
determine, using the priority matrix and the one or more weighted parameters, the one or more signals to apply to the one or more signaling devices.
5 . The system of claim 1 , wherein the priority matrix further maps the predicted trajectory and the predicted time duration of the object with a second predicted trajectory and a second predicted time duration of a second object of the one or more objects, and wherein the one or more processors are configured to determine, based on the mapping of the predicted trajectory and the predicted time duration of the object with the second predicted trajectory and the second predicted time duration of the second object, the one or more signals to apply to the one or more signaling devices to assist the object and the second object in traversing the roadway intersection.
6 . The system of claim 1 , wherein the type of the object corresponds to at least one of: (i) a pedestrian, (ii) a bicycle, (iii) a motor vehicle, (iv) a bus, (v) a truck, (vi) a motorcycle, (vii) a scooter, (viii) a wheelchair, (ix) a stroller, (x) an animal, (xi) an emergency vehicle, (xii) an autonomous vehicle, (xiii) a trailer, or (xiv) a train.
7 . The system of claim 1 , wherein the one or more processors are configured to determine the one or more signals based at least in part on sensor data indicating one or more environmental conditions at the roadway intersection, the environmental conditions comprising at least one of weather conditions, road-surface conditions, visibility level, or traffic density.
8 . The system of claim 1 , wherein, upon determining that the object has not traversed the roadway intersection within the predicted time duration, the one or more processors are further configured to generate updated one or more instructions to adjust one or more phases of the one or more signaling devices to provide additional time for the object to traverse the roadway intersection.
9 . The system of claim 1 , wherein, upon determining that the object has not traversed the roadway intersection within the predicted time duration, the one or more processors are further configured to generate an instruction to initiate one of an all-red light operation or a flashing light operation of the one or more signaling devices.
10 . The system of claim 1 , wherein the one or more processors are configured to generate the one or more signals to include at least one of:
(i) signaling a head-start interval for the object when the object comprises a pedestrian or a bicycle;
(ii) signaling a head-start interval for a second object detected by the one or more sensors, wherein the second object comprises a motor vehicle, to traverse the roadway intersection prior to arrival of the object comprising the pedestrian or the bicycle;
(iii) signaling a no-turn movement on a first signaling device of the one or more signaling devices while signaling a straight-through movement on a second signaling device of the one or more signaling devices;
(iv) displaying, via a dynamic signage device associated with the roadway intersection, a message indicating no additional vehicles are permitted to enter the roadway intersection;
(v) transmitting, via a communication interface, a vehicle-to-everything (V2X) message identifying the one or more signals for the one or more signaling devices; or
(vi) adjusting targeted street light illumination or sign contrast upon detecting low visibility or occlusion of the object based on the sensor data.
11 . The system of claim 1 , wherein the one or more processors are configured to:
analyze the sensor data to determine a velocity and a trajectory of the object to be assisted; and
generate the one or more instructions for the one or more signaling devices based on the velocity and the trajectory.
12 . The system of claim 1 , wherein the one or more processors are configured to communicate, via a network, with a system for managing roadway traffic, data from the one or more sensors regarding identified objects and the one or more signals to facilitate coordinated traffic control, by the system, across a plurality of roadway intersections comprising the roadway intersection.
13 . The system of claim 1 , wherein the one or more processors are configured to generate an alert for a second object of the one or more objects, the alert to notify the second object to adjust movement across the roadway intersection responsive to the object traversing the roadway intersection.
14 . A method, comprising,
identifying, by one or more processors coupled with memory, from one or more sensors of a roadway intersection, sensor data corresponding to one or more objects moving within an area comprising the roadway intersection;
identifying, by the one or more processors, based on the sensor data input into one or more machine learning (ML) models trained on a plurality of objects moving in a plurality of directions and according to a plurality of velocities within one or more areas comprising one or more roadway intersections, an object to be assisted in traversing the roadway intersection;
determining, by the one or more processors, from the sensor data, a predicted trajectory of the object and a predicted time duration for the object to traverse the roadway intersection;
determining, by the one or more processors, using a priority matrix mapping the predicted trajectory and the predicted time duration to one or more variations of control of one or more signals for one or more signaling devices of the roadway intersection, one or more signals to apply to the one or more signaling devices, wherein the one or more signals are determined based on a priority associated with a type of the object of a plurality of types of objects within the area;
generating, by the one or more processors, one or more instructions for controlling the one or more signaling devices according to the one or more signals to assist the object in traversing the roadway intersection;
initiating, by the one or more processors, via a controller and responsive to the one or more instructions, actuation of one or more phases of the one or more signaling devices of the roadway intersection according to the time duration for the object to traverse the roadway intersection; and
verifying, by the one or more processors, upon initiation of the actuation of the one or more phases and based on sensor data from the one or more sensors, whether the object has traversed the roadway intersection within the predicted time duration.
15 . The method of claim 14 , comprising:
determining, by the one or more processors, based on the sensor data, that the object is present within a zone of approach to the roadway intersection; and
determining, by the one or more processors, responsive to the object being present within the zone of approach, the one or more signals of the one or more signaling devices.
16 . The method of claim 14 , wherein the priority matrix is stored in the memory and is updated dynamically based on traffic density and frequency of a type of the object detected by the one or more sensors.
17 . The method of claim 14 , wherein the priority matrix stored in the memory comprises one or more weighted parameters for at least one of the object, the predicted trajectory, or the predicted time duration, the method comprising:
determining, by the one or more processors, the one or more weighted parameters based on data from the one or more sensors that indicates at least one of a type of the object, a velocity of the object, or an occupancy of objects within the area; and
determining, by the one or more processors, using the priority matrix and the one or more weighted parameters, the one or more signals to apply to the one or more signaling devices.
18 . The method of claim 14 , wherein the priority matrix further maps the predicted trajectory and the predicted time duration of the object with a second predicted trajectory and a second predicted time duration of a second object of the one or more objects, the method comprising:
determining, by the one or more processors, based on the mapping of the predicted trajectory and the predicted time duration of the object with the second predicted trajectory and the second predicted time duration of the second object; and
applying, by the one or more processors, the one or more signals to the one or more signaling devices to assist the object and the second object in traversing the roadway intersection.
19 . The method of claim 14 , wherein the type of the object corresponds to at least one of: (i) a pedestrian, (ii) a bicycle, (iii) a motor vehicle, (iv) a bus, (v) a truck, (vi) a motorcycle, (vii) a scooter, (viii) a wheelchair, (ix) a stroller, (x) an animal, (xi) an emergency vehicle, (xii) an autonomous vehicle, (xiii) a trailer, or (xiv) a train.
20 . A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to:
identify, from one or more sensors of a roadway intersection, sensor data corresponding to one or more objects moving within an area comprising the roadway intersection;
identify, based on the sensor data input into one or more machine learning (ML) models trained on a plurality of objects moving in a plurality of directions and according to a plurality of velocities within one or more areas comprising one or more roadway intersections, an object to be assisted in traversing the roadway intersection;
determine, from the sensor data, a predicted trajectory of the object and a predicted time duration for the object to traverse the roadway intersection;
determine, using a priority matrix mapping the predicted trajectory and the predicted time duration to one or more variations of control of one or more signals for one or more signaling devices of the roadway intersection, one or more signals to apply to the one or more signaling devices, wherein the one or more signals are determined based on a priority associated with a type of the object of a plurality of types of objects within the area;
generate one or more instructions for controlling the one or more signaling devices according to the one or more signals to assist the object in traversing the roadway intersection;
initiate, via a controller and responsive to the one or more instructions, actuation of one or more phases of the one or more signaling devices of the roadway intersection according to the time duration for the object to traverse the roadway intersection; and
verify, upon initiation of the actuation of the one or more phases and based on sensor data from the one or more sensors, whether the object has traversed the roadway intersection within the predicted time duration.