Abstract: The present disclosure relates generally to systems for facilitating the use of autonomous vehicles (AVs), and more particularly to automated artificial intelligence (AI)-based techniques for determining an insurance premium for an AV ride based upon various factors including the evaluation of risk associated with the AV ride. An automated AI-based infrastructure is provided that uses automated machine-learning (ML) based techniques for evaluating a level of risk for any particular AV ride and then determining an insurance premium for the AV ride based on the level of risk. The insurance premium determination incorporates Usage Based Insurance Pricing (UBIP) that has been customized for autonomous driving, whereby the level of risk is predicted based on information associated with the expected usage of the AV during the ride. Thus, the insurance premium is customized for each ride and can be determined as part of calculating upfront the total price of the ride.

Abstract: The present disclosure relates generally to autonomous machines (AMs) and more particularly to techniques for intelligently planning, managing and performing various tasks using AMs. A control system (referred to as a fleet management system or FMS) is disclosed for managing a set of resources at a site, which may include AMs. The FMS is configured to control and manage the AMs at the site such that tasks are performed autonomously by the AMs. An AM may directly communicate with another AM located on the site to complete a task without requiring to be in constant communication with the FMS during the performance of the task. The FMS is configured to use various optimization techniques to allocate resources (e.g., AMs) for performing tasks at the site. The resource allocation is performed so as to maximize the use of available AMs while ensuring that the tasks get performed in a timely manner.

Abstract: The present disclosure relates generally to techniques for the kinematic estimation and dynamic behavior estimation of autonomous heavy equipment or vehicles to improve navigation, digging and material carrying tasks at various industrial work sites. Particularly, aspects of the present disclosure are directed to obtaining a set of sensor data providing a representation of operation of an autonomous vehicle in a worksite environment, estimating, by a trained model comprising a Gaussian process, a set of output data based on the set of sensor data, controlling an operation of the autonomous vehicle in the worksite environment using input data derived from the set of sensor data and the set of output data, obtaining actual output data from the operation of the autonomous vehicle in the worksite environment, and updating the trained model with the input data and the actual output data.

Abstract: The present disclosure relates generally to techniques for the kinematic estimation and dynamic behavior estimation of autonomous heavy equipment or vehicles to improve navigation, digging and material carrying tasks at various industrial work sites. Particularly, aspects of the present disclosure are directed to obtaining a set of sensor data providing a representation of operation of an autonomous vehicle in a worksite environment, estimating, by a trained model comprising a Gaussian process, a set of output data based on the set of sensor data, controlling an operation of the autonomous vehicle in the worksite environment using input data derived from the set of sensor data and the set of output data, obtaining actual output data from the operation of the autonomous vehicle in the worksite environment, and updating the trained model with the input data and the actual output data.

Abstract: The present disclosure relates generally to autonomous machines (AMs) and more particularly to techniques for intelligently planning, managing and performing various tasks using AMs. A control system (referred to as a fleet management system or FMS) is disclosed for managing a set of resources at a site, which may include AMs. The FMS is configured to control and manage the AMs at the site such that tasks are performed autonomously by the AMs. An AM may directly communicate with another AM located on the site to complete a task without requiring to be in constant communication with the FMS during the performance of the task. The FMS is configured to use various optimization techniques to allocate resources (e.g., AMs) for performing tasks at the site. The resource allocation is performed so as to maximize the use of available AMs while ensuring that the tasks get performed in a timely manner.

Abstract: The present disclosure relates generally to systems for facilitating the use of autonomous vehicles (AVs), and more particularly to automated artificial intelligence (AI)-based techniques for determining an insurance premium for an AV ride based upon various factors including the evaluation of risk associated with the AV ride. An automated AI-based infrastructure is provided that uses automated machine-learning (ML) based techniques for evaluating a level of risk for any particular AV ride and then determining an insurance premium for the AV ride based on the level of risk. The insurance premium determination incorporates Usage Based Insurance Pricing (UBIP) that has been customized for autonomous driving, whereby the level of risk is predicted based on information associated with the expected usage of the AV during the ride. Thus, the insurance premium is customized for each ride and can be determined as part of calculating upfront the total price of the ride.