Abstract: Vehicles according to at least some embodiments of the disclosure include a sensor, and a computing device comprising at least one hardware processing unit. The computing device is programmed to perform operations comprising capturing an image with the sensor, identifying an object in the image, and in response to an accuracy of the identification meeting a first criterion, pre-loading a braking system of the autonomous vehicle. In some aspects, the computing device may predict that an object not currently within a path of the vehicle has a probability of entering the path of the vehicle that meets a second criterion. When the probability of entering the path meets the second criterion, some of the disclosed embodiments may pre-load the braking system.

Abstract: Various examples are directed to systems and methods for operating a vehicle comprising a tractor and a trailer attached for pulling behind the tractor. A center-of-mass system may determine a mass of the trailer and a tractor understeer. The center-of-mass system may determine the tractor understeer using steering input data describing a steering angle of the tractor and yaw data describing a yaw of the tractor. The center-of-mass system may determine a load center of mass using the tractor understeer and a mass of the trailer. The center-of-mass system may further determine that the load center of mass transgresses a center-of-mass threshold and send an alert message indicating that the load transgresses the load center-of-mass threshold.

Abstract: Vehicles according to at least some embodiments of the disclosure include a sensor, and a computing device comprising at least one hardware processing unit. The computing device is programmed to perform operations comprising capturing an image with the sensor, identifying an object in the image, and in response to an accuracy of the identification meeting a first criterion, pre-loading a braking system of the autonomous vehicle. In some aspects, the computing device may predict that an object not currently within a path of the vehicle has a probability of entering the path of the vehicle that meets a second criterion. When the probability of entering the path meets the second criterion, some of the disclosed embodiments may pre-load the braking system.

Abstract: Vehicles according to at least some embodiments of the disclosure include a sensor, and a computing device comprising at least one hardware processing unit. The computing device is programmed to perform operations comprising capturing an image with the sensor, identifying an object in the image, and in response to an accuracy of the identification meeting a first criterion, pre-loading a braking system of the autonomous vehicle. In some aspects, the computing device may predict that an obj ect not currently within a path of the vehicle has a probability of entering the path of the vehicle that meets a second criterion. When the probability of entering the path meets the second criterion, some of the disclosed embodiments may pre-load the braking system.

Abstract: Vehicles according to at least some embodiments of the disclosure include a sensor, and a computing device comprising at least one hardware processing unit. The computing device is programmed to perform operations comprising capturing an image with the sensor, identifying an object in the image, and in response to an accuracy of the identification meeting a first criterion, pre-loading a braking system of the autonomous vehicle. In some aspects, the computing device may predict that an object not currently within a path of the vehicle has a probability of entering the path of the vehicle that meets a second criterion. When the probability of entering the path meets the second criterion, some of the disclosed embodiments may pre-load the braking system.

Abstract: Various examples are directed to systems and methods for operating a vehicle comprising a tractor and a trailer attached for pulling behind the tractor. A center-of-mass system may determine a mass of the trailer and a tractor understeer. The center-of-mass system may determine the tractor understeer using steering input data describing a steering angle of the tractor and yaw data describing a yaw of the tractor. The center-of-mass system may determine a load center of mass using the tractor understeer and a mass of the trailer. The center-of-mass system may further determine that the load center of mass transgresses a center-of-mass threshold and send an alert message indicating that the load transgresses the load center-of-mass threshold.

Abstract: Various examples are directed to systems and methods for operating a vehicle comprising a tractor and a trailer attached for pulling behind the tractor. A center-of-mass system may determine a mass of the trailer and a tractor understeer. The center-of-mass system may determine the tractor understeer using steering input data describing a steering angle of the tractor and yaw data describing a yaw of the tractor. The center-of-mass system may determine a load center of mass using the tractor understeer and a mass of the trailer. The center-of-mass system may further determine that the load center of mass transgresses a center-of-mass threshold and send an alert message indicating that the load transgresses the load center-of-mass threshold.

Abstract: Systems and methods for compensating for acceleration-related sensor mismatch of an autonomous vehicle are provided. A computing system for compensating for autonomous vehicle acceleration-related sensor mismatch can include one or more processors and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the computing system to perform operations. The operations can include obtaining data indicative of an acceleration mismatch between a first portion and a second portion of an autonomous vehicle. The operations can further include determining a sensor compensation action based at least in part on the data indicative of the acceleration mismatch. The operations can further include implementing the sensor compensation action for the autonomous vehicle.

Abstract: Disclosed are systems, methods, and computer-readable storage media to control a vehicle. In one aspect, a method includes capturing point-cloud data representative of a surrounding of an autonomous vehicle with one or more LIDAR sensors, identifying a point in the point cloud data as a non-matching point in response to the point having no corresponding point in a map used to determine a position of the autonomous vehicle, determining whether the non-matching point is to be used in a determination of an overlap score based on one or more comparisons of the point cloud data and the map, determining the overlap score in response to the determining whether the non-matching point is to be used in the determination of the overlap score, determining a position of the autonomous vehicle based on the overlap score and the map, and controlling the autonomous vehicle based on the position.

Abstract: Vehicles according to at least some embodiments of the disclosure include a sensor, and a computing device comprising at least one hardware processing unit. The computing device is programmed to perform operations comprising capturing an image with the sensor, identifying an object in the image, and in response to an accuracy of the identification meeting a first criterion, pre-loading a braking system of the autonomous vehicle. In some aspects, the computing device may predict that an object not currently within a path of the vehicle has a probability of entering the path of the vehicle that meets a second criterion. When the probability of entering the path meets the second criterion, some of the disclosed embodiments may pre-load the braking system.

Abstract: Various examples are directed to systems and methods of monitoring a vehicle. At least one processor unit may access first slope data indicative of a first slope referenced to a cab of the vehicle and access second slope data indicative of a second slope independent of the cab of the vehicle. The at least one processor unit may generate cab tilt data indicative of a cab tilt of the cab using the first slope data and the second slope data.

Abstract: Various examples are directed to systems and methods for at least partially controlling a vehicle. A vehicle system may access motion plan data for the vehicle. The vehicle system may generate drivetrain bias data representative of drivetrain effects of the vehicle. The vehicle system may generate a biased throttle command using the motion plan data and the drivetrain bias data and apply the biased throttle command to a propulsion system of the vehicle.

Abstract: Various examples are directed to systems and methods for operating a vehicle comprising a tractor and a trailer attached for pulling behind the tractor. A center-of-mass system may determine a mass of the trailer and a tractor understeer. The center-of-mass system may determine the tractor understeer using steering input data describing a steering angle of the tractor and yaw data describing a yaw of the tractor. The center-of-mass system may determine a load center of mass using the tractor understeer and a mass of the trailer. The center-of-mass system may further determine that the load center of mass transgresses a center-of-mass threshold and send an alert message indicating that the load transgresses the load center-of-mass threshold.

Abstract: Systems and methods for compensating for acceleration-related sensor mismatch of an autonomous vehicle are provided. A computing system for compensating for autonomous vehicle acceleration-related sensor mismatch can include one or more processors and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the computing system to perform operations. The operations can include obtaining data indicative of an acceleration mismatch between a first portion and a second portion of an autonomous vehicle. The operations can further include determining a sensor compensation action based at least in part on the data indicative of the acceleration mismatch. The operations can further include implementing the sensor compensation action for the autonomous vehicle.

Abstract: Systems and methods for compensating for displacement-related sensor mismatch of an autonomous vehicle are provided. An autonomous vehicle can define a pitch axis and a roll axis. The pitch axis can be perpendicular to the roll axis. A system can include one or more processors, and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the computing system to perform operations. The operations can include obtaining data indicative of a displacement of a first portion of the autonomous vehicle relative to a second portion of the autonomous vehicle. The operations can further include determining an orientation of the first portion relative to the second portion about at least one of the pitch axis or the roll axis based at least in part on the data indicative of the displacement.