Model adaptation for autonomous trucking in right of way

1. A system comprising:

a central server;

an interface for receiving data from a railroad system that manages a railroad running parallel to a dedicated roadway;

a plurality of sensors positioned in a fixed location relative to the dedicated roadway, wherein each sensor in the plurality of sensors:

detects one or more autonomous vehicles in a first field of view on the dedicated roadway, and for each detected autonomous vehicle:

generates sensor data for the detected autonomous vehicle based on the detected autonomous vehicle on the dedicated roadway and the data received at the interface from the railroad system;

generates observational data based on the generated sensor data;

instructs the detected autonomous vehicle to switch to an enhanced processing mode based on the generated observational data;

determines an action for the detected autonomous vehicle based on the generated observational data, the action indicative of an action the autonomous vehicle should take when traversing the dedicated roadway; and

instructs the detected autonomous vehicle to traverse the dedicated roadway based on the determined action;

acquires additional sensor data of the autonomous vehicles traversing the dedicated roadway;

detects an identity for each of the autonomous vehicles from the additional sensor data;

determines a location for at least some of the autonomous vehicles on the dedicated roadway; and

from the identity for each of the autonomous vehicles, determines that the at least some of the autonomous vehicles are proximate to the end of the dedicated roadway, and

in response, transmits an indication to the at least some of the autonomous vehicles to switch to a normal processing mode.

2. The system of claim 1 , wherein the interface displays data related to the railroad that traverses in parallel to the dedicated roadway and one or more trains traverse the railroad, the data comprising a number of the one or more trains, a direction of the one or more trains traveling on the railroad, and a number of railroads.

3. The system of claim 1 , wherein the autonomous vehicles that traverse the dedicated roadway comprise autonomous trucks.

4. The system of claim 1 , wherein:

the plurality of sensors:

acquires first sensor data of the autonomous vehicles traversing the dedicated roadway;

detects an identity for each of the autonomous vehicles from the first sensor data;

from the identity for each of the autonomous vehicles, determines that each of the autonomous vehicles have entered the dedicated roadway; and

in response, transmits an indication to each of the autonomous vehicles to switch to the enhanced processing mode.

5. The system of claim 4 , wherein the enhanced processing mode comprises (i) a first setting for operating the detected autonomous vehicle using sensor data from the plurality of sensors and the sensor data onboard the detected autonomous vehicle or (ii) a second setting in which the detected autonomous vehicle utilizes an enhanced trained machine-learning model for producing actions for traversing the dedicated roadway.

6. The system of claim 1 , wherein:

the plurality of sensors:

acquires second sensor data of the autonomous vehicles traversing the dedicated roadway;

acquires third sensor data of one or more trains traversing the railroad that traverses in parallel to the dedicated roadway; and

transmits the acquired second and third sensor data to the central server;

wherein the central server:

receives the second and third sensor data from each sensor of the plurality of sensors:

determines from the received second and third sensor data:

prevailing speeds of the autonomous vehicles traversing the dedicated roadway;

vehicle dynamics of the autonomous vehicles traversing the dedicated roadway;

objects currently identified on the dedicated roadway; and

characteristics of the one or more trains traversing the railroad; and

in response, determines one or more actions for each of the autonomous vehicles for traversing the dedicated roadway based on the prevailing speeds, the vehicle dynamics, the objects currently identified, and the characteristics of the one or more trains traversing the railroad.

7. The system of claim 1 , wherein:

the plurality of sensors:

acquires fourth sensor data of the autonomous vehicles traversing the dedicated roadway;

acquires fifth sensor data indicative of a train that has derailed off the railroad, the railroad traversing in parallel to the dedicated the roadway;

transmits the acquired fourth and fifth sensor data to the central server;

wherein the central server:

receives the acquired fourth and fifth sensor data from each sensor of the plurality of sensors:

determines from the received fourth and fifth sensor data:

a first indication that the train has derailed off the railroad;

a second indication that at least some of the autonomous vehicles traversing the dedicated roadway are on a path to collide with the derailed train; and

in response, transmits an instruction to the at least some of the autonomous vehicles to (i) reroute traffic on the dedicated roadway to avoid the derailed train, (ii) decelerate the autonomous vehicles, (iii) stop the autonomous vehicles from colliding with the derailed train, or (iv) a combination of (i)-(iii).

8. The system of claim 1 , wherein the normal processing mode comprises a setting for operating an autonomous vehicle with an onboard trained machine-learning model used (i) prior to entrance of the autonomous vehicle to the dedicated roadway and (ii) after the autonomous vehicle exits the dedicated roadway.

9. A computer-implemented method comprising:

receiving, at an interface, data from a railroad system that manages a railroad running parallel to a dedicated roadway;

detecting, by each sensor in a plurality of sensors positioned in a fixed location relative to the dedicated roadway, one or more autonomous vehicles in a first field of view on the dedicated roadway, and for each detected autonomous vehicle:

generates sensor data for the detected autonomous vehicle based on the detected autonomous vehicle on the dedicated roadway and the data received at the interface from the railroad system;

generates observational data based on the generated sensor data;

instructs the detected autonomous vehicle to switch to an enhanced processing mode based on the generated observational data;

determines an action for the detected autonomous vehicle based on the generated observational data, the action indicative of an action the autonomous vehicle should take when traversing the dedicated roadway; and

instructs the detected autonomous vehicle to traverse the dedicated roadway based on the determined action;

acquiring additional sensor data of the autonomous vehicles traversing the dedicated roadway;

detecting an identity for each of the autonomous vehicles from the additional sensor data;

determining a location for at least some of the autonomous vehicles on the dedicated roadway; and

from the identity for each of the autonomous vehicles, determines that the at least some of the autonomous vehicles are proximate to the end of the dedicated roadway, and in response, transmitting an indication to the at least some of the autonomous vehicles to switch to a normal processing mode.

10. The computer-implemented method of claim 9 , further comprising:

displaying, at the interface, data related to the railroad that traverses in parallel to the dedicated roadway and one or more trains traverse the railroad, the data comprising a number of the one or more trains, a direction of the one or more trains traveling on the railroad, and a number of railroads.

11. The computer-implemented method of claim 9 , wherein the autonomous vehicles that traverse the dedicated roadway comprise autonomous trucks.

12. The computer-implemented method of claim 9 , further comprising:

acquiring, by the plurality of sensors, first sensor data of the autonomous vehicles traversing the dedicated roadway;

detecting, by the plurality of sensors, an identity for each of the autonomous vehicles from the first sensor data;

from the identity for each of the autonomous vehicles, determining, by the plurality of sensors, that each of the autonomous vehicles have entered the dedicated roadway; and

in response, transmitting, by the plurality of sensors, an indication to each of the autonomous vehicles to switch to the enhanced processing mode.

13. The computer-implemented method of claim 12 , wherein the enhanced processing mode comprises (i) a first setting for operating the detected vehicle using sensor data from the plurality of sensors and the sensor data onboard the autonomous vehicle or (ii) a second setting in which the detected autonomous vehicle utilizes an enhanced trained machine-learning model for producing actions for traversing the dedicated roadway.

14. The computer-implemented method of claim 9 , further comprising:

acquiring, by the plurality of sensors, second sensor data of the autonomous vehicles traversing the dedicated roadway;

acquiring, by the plurality of sensors, third sensor data of one or more trains traversing the railroad that traverses in parallel to the dedicated roadway;

transmitting, the plurality of sensors, the acquired second and third sensor data to a central server;

receiving, by the central server, the second and third sensor data from each sensor of the plurality of sensors:

determining, by the central server, from the received second and third sensor data:

prevailing speeds of the autonomous vehicles traversing the dedicated roadway;

vehicle dynamics of the autonomous vehicles traversing the dedicated roadway;

objects currently identified on the dedicated roadway; and

characteristics of the one or more trains traversing the railroad; and

in response, determining, by the central server, one or more actions for each of the autonomous vehicles for traversing the dedicated roadway based on the prevailing speeds, the vehicle dynamics, the objects currently identified, and the characteristics of the one or more trains traversing the railroad.

15. The computer-implemented method of claim 9 , further comprising:

acquiring, by the plurality of sensors, fourth sensor data of the autonomous vehicles traversing the dedicated roadway;

acquiring, by the plurality of sensors, fifth sensor data indicative of a train that has derailed off the railroad, the railroad traversing in parallel to the dedicated roadway;

transmitting, by the plurality of sensors, the acquired fourth and fifth sensor data to a central server;

receiving, by the central server, the acquired fourth and fifth sensor data from each sensor of the plurality of sensors:

determining, by the central server, from the received fourth and fifth sensor data:

a first indication that the train has derailed off the railroad;

a second indication that at least some of the autonomous vehicles traversing the dedicated roadway are on a path to collide with the derailed train; and

in response, transmitting, by the central server, an instruction to the at least some of the autonomous vehicles to (i) reroute traffic on the dedicated roadway to avoid the derailed train, (ii) decelerate the autonomous vehicles, (iii) stop the autonomous vehicles from colliding with the derailed train, or (iv) a combination of (i)-(iii).

16. The computer-implemented method of claim 9 , wherein the normal processing mode comprises a setting for operating an autonomous vehicle with an onboard trained machine-learning model used (i) prior to entrance of the autonomous vehicle to the dedicated roadway and (ii) after the autonomous vehicle exits the dedicated roadway.

17. One or more non-transitory machine-readable media storing instructions that, when executed by one or more processing devices, cause the one or more processing devices to perform operations comprising:

receiving, at an interface, data from a railroad system that manages a railroad running parallel to a dedicated roadway;

detecting, by each sensor in a plurality of sensors positioned in a fixed location relative to the dedicated roadway, one or more autonomous vehicles in a first field of view on the dedicated roadway, and for each detected autonomous vehicle:

generates sensor data for the detected autonomous vehicle based on the detected autonomous vehicle on the dedicated roadway and the data received at the interface from the railroad system;

generates observational data based on the generated sensor data;

instructs the detected autonomous vehicle to switch to an enhanced processing mode based on the generated observational data;

determines an action for the detected autonomous vehicle based on the generated observational data, the action indicative of an action the autonomous vehicle should take when traversing the dedicated roadway; and

instructs the detected autonomous vehicle to traverse the dedicated roadway based on the determined action;

acquiring additional sensor data of the autonomous vehicles traversing the dedicated roadway;

detecting an identity for each of the autonomous vehicles from the additional sensor data;

determining a location for at least some of the autonomous vehicles on the dedicated roadway; and

from the identity for each of the autonomous vehicles, determines that the at least some of the autonomous vehicles are proximate to the end of the dedicated roadway, and in response, transmitting an indication to the at least some of the autonomous vehicles to switch to a normal processing mode.

18. The one or more non-transitory machine-readable media of claim 17 , further comprising:

displaying, at the interface, data related to the railroad that traverses in parallel to the dedicated roadway and one or more trains traverse the railroad, the data comprising a number of the one or more trains, a direction of the one or more trains traveling on the railroad, and a number of railroads.