Generation and transmission of control commands for connected vehicles based on predicted future flow, average velocity, or future density of traffic
A server accesses vehicle data from each connected vehicle (CV) of a subset of a plurality of CVs on a roadway portion, the vehicle data comprising at least one of: a position, a velocity, or a headway. The server generates, based on the accessed vehicle data, a long-term shared world model. The server generates, using the long-term shared world model, a data structure representing predicted future velocities on the roadway portion by position and time by applying a traffic flow model to the long-term shared world model. The server transmits, to a connected autonomous vehicle (CAV), a control signal for controlling operation of the CAV based on the generated data structure.
1. A method comprising:
receiving, at a control server and from a connected vehicle (CV), velocity data representing velocities of the CV and additional vehicles proximate to the CV;
computing a velocity correction factor based on the velocity data;
receiving, at the control server and from the CV, headway data representing a headway of the CV and the additional vehicles proximate to the CV;
computing a headway correction factor based on the headway data;
determining, using a prediction engine at the control server, a future flow, a future average velocity, and a future density for a segment of a roadway;
adjusting the determined future flow based on the velocity correction factor and the headway correction factor;
adjusting the determined future average velocity based on the velocity correction factor;
adjusting the determined future density based on the headway correction factor;
generating, at the control server, a control signal for a connected automated vehicle (CAV) based on at least one of: the determined future flow, the determined future average velocity, or the determined future density; and
transmitting the generated control signal to the CAV.
2. The method of claim 1 , wherein the additional vehicles proximate to the CV comprise at least one of: a vehicle in front of the CV in a lane adjacent to the CV or a vehicle behind the CV in a lane adjacent to the CV.
3. The method of claim 1 , wherein the control server receives the velocity data and the headway data from multiple CVs, including the CV, wherein the velocity correction factor is computed based on the velocity data from the multiple CVs, wherein the headway correction factor is computed based on the headway data from the multiple CVs.
4. The method of claim 1 , wherein the prediction engine leverages a long-term shared world model storing data received from multiple CVs, including the CV, and multiple roadway sensors associated with at least one roadway used by the multiple CVs.
5. The method of claim 1 , wherein adjusting the determined future flow based on the velocity correction factor and the headway correction factor comprises: multiplying the determined future flow by a quotient of the velocity correction factor and the headway correction factor.
6. The method of claim 1 , wherein adjusting the determined future average velocity based on the velocity correction factor comprises: multiplying the determined future average velocity by the velocity correction factor.
7. The method of claim 1 , wherein adjusting the determined future density based on the headway correction factor comprises: dividing the determined future density by the headway correction factor.
8. An apparatus comprising:
a memory storing instructions; and
a processor to execute the stored instructions to:
receive, at a control server and from a connected vehicle (CV), velocity data representing velocities of the CV and additional vehicles proximate to the CV;
compute a velocity correction factor based on the velocity data;
receive, at the control server and from the CV, headway data representing a headway of the CV and the additional vehicles proximate to the CV;
compute a headway correction factor based on the headway data;
determine, using a prediction engine at the control server, a future flow, a future average velocity, and a future density for a segment of a roadway;
adjust the determined future flow based on the velocity correction factor and the headway correction factor;
adjust the determined future average velocity based on the velocity correction factor;
adjust the determined future density based on the headway correction factor;
generate, at the control server, a control signal for a connected automated vehicle (CAV) based on at least one of: the determined future flow, the determined future average velocity, or the determined future density; and
transmit the generated control signal to the CAV.
9. The apparatus of claim 8 , wherein the additional vehicles proximate to the CV comprise at least one of: a vehicle in front of the CV in a lane adjacent to the CV or a vehicle behind the CV in a lane adjacent to the CV.
10. The apparatus of claim 8 , wherein the control server receives the velocity data and the headway data from multiple CVs, including the CV, wherein the velocity correction factor is computed based on the velocity data from the multiple CVs, wherein the headway correction factor is computed based on the headway data from the multiple CVs.
11. The apparatus of claim 8 , wherein the prediction engine leverages a long-term shared world model storing data received from multiple CVs, including the CV, and multiple roadway sensors associated with at least one roadway used by the multiple CVs.
12. The apparatus of claim 8 , wherein adjusting the determined future flow based on the velocity correction factor and the headway correction factor comprises: multiplying the determined future flow by a quotient of the velocity correction factor and the headway correction factor.
13. The apparatus of claim 8 , wherein adjusting the determined future average velocity based on the velocity correction factor comprises: multiplying the determined future average velocity by the velocity correction factor.
14. The apparatus of claim 8 , wherein adjusting the determined future density based on the headway correction factor comprises: dividing the determined future density by the headway correction factor.
15. A non-transitory computer-readable medium storing instructions which, when executed by a processor, cause the processor to perform operations comprising:
receiving, at a control server and from a connected vehicle (CV), velocity data representing velocities of the CV and additional vehicles proximate to the CV;
computing a velocity correction factor based on the velocity data;
receiving, at the control server and from the CV, headway data representing a headway of the CV and the additional vehicles proximate to the CV;
computing a headway correction factor based on the headway data;
determining, using a prediction engine at the control server, a future flow, a future average velocity, and a future density for a segment of a roadway;
adjusting the determined future flow based on the velocity correction factor and the headway correction factor;
adjusting the determined future average velocity based on the velocity correction factor;
adjusting the determined future density based on the headway correction factor;
generating, at the control server, a control signal for a connected automated vehicle (CAV) based on at least one of: the determined future flow, the determined future average velocity, or the determined future density; and
transmitting the generated control signal to the CAV.
16. The computer-readable medium of claim 15 , wherein the additional vehicles proximate to the CV comprise at least one of: a vehicle in front of the CV in a lane adjacent to the CV or a vehicle behind the CV in a lane adjacent to the CV.
17. The computer-readable medium of claim 15 , wherein the control server receives the velocity data and the headway data from multiple CVs, including the CV, wherein the velocity correction factor is computed based on the velocity data from the multiple CVs, wherein the headway correction factor is computed based on the headway data from the multiple CVs.
18. The computer-readable medium of claim 15 , wherein the prediction engine leverages a long-term shared world model storing data received from multiple CVs, including the CV, and multiple roadway sensors associated with at least one roadway used by the multiple CVs.
19. The computer-readable medium of claim 15 , wherein adjusting the determined future flow based on the velocity correction factor and the headway correction factor comprises: multiplying the determined future flow by a quotient of the velocity correction factor and the headway correction factor.
20. The computer-readable medium of claim 15 , wherein adjusting the determined future average velocity based on the velocity correction factor comprises: multiplying the determined future average velocity by the velocity correction factor.