| CPC B60W 60/001 (2020.02) [B60K 35/00 (2013.01); B62D 15/029 (2013.01); B60K 35/22 (2024.01); B60K 35/28 (2024.01); B60K 2360/166 (2024.01); B60W 2552/05 (2020.02); B60W 2554/20 (2020.02); B60W 2554/402 (2020.02); B60W 2554/4029 (2020.02); B60W 2554/4049 (2020.02); B60W 2554/408 (2020.02); B60W 2555/20 (2020.02); B60W 2556/10 (2020.02); E01C 23/01 (2013.01)] | 20 Claims |
|
1. A method performed in a cloud-based digital space, comprising:
receiving data regarding a physical object from a physical space within which a vehicle is operating;
processing the data using machine learning to model road surface conditions, in which respective penalty values are assigned to corresponding road surfaces, a respective penalty value being inversely related to a condition of the corresponding road surface, wherein the penalty values are used to generate a rewards function that categorizes the modeled road surface conditions;
deriving instructions based on the modeled road surface conditions and applying the rewards function to guide actuation of the vehicle along a trajectory; and
transmitting the instructions to the physical space connected to the vehicle to guide actuation of the vehicle.
|
|
14. A cloud-based system effectuating an end-to-end framework, comprising:
a cloud-based platform hosting one or more digital twins corresponding to one or more physical objects from a physical space within which a vehicle is operating, wherein one of the digital twins comprises a data lake and an active road maintenance microservice;
a communications layer communicatively connecting the one or more digital twins to the one or more physical objects, wherein:
the communications layer transmits data regarding the one or more physical objects to at least the one or more corresponding digital twins, and
the communications layer transmits instructions that have been derived from processing of the transmitted data by at least the active road maintenance microservice to the physical space connected to the vehicle; and
wherein the active road maintenance microservice:
processes the data using machine learning to model road surface conditions, in which a rewards function assigns respective rewards values to corresponding road surfaces, a respective rewards value corresponding to a condition of the corresponding road surface, and
derives the instructions based on the modeled road surface conditions and on optimizing the rewards function to guide actuation of the vehicle along a trajectory.
|
|
19. A method performed in a cloud-based system effectuating an end-to-end framework, comprising:
in a digital space:
receiving data regarding a physical object from a physical space within which a vehicle is operating;
processing the data using machine learning to model road surface conditions, including using a rewards function generated by penalty values to assign respective rewards values to corresponding road surfaces that categorizes the modeled road surface conditions, a respective rewards value being related to a condition of the corresponding road surface;
deriving instructions based on applying the rewards function to guide actuation of the vehicle along a trajectory;
transmitting the instructions to the physical space connected to the vehicle to guide actuation of the vehicle;
in the physical space:
receiving the transmitted instructions;
determining whether the vehicle is an autonomous vehicle or a non-autonomous vehicle; and
when the vehicle is an autonomous vehicle navigating the vehicle along the trajectory using the instructions, or when the vehicle is a non-autonomous vehicle prompting a human driver to navigate the vehicle along with trajectory using the instructions.
|