Abstract: Aspects of the present disclosure involve systems, methods, and devices for mitigating Lidar cross-talk. Consistent with some embodiments, a Lidar system is configured to include one or more noise source detectors that detect noise signals that may produce noise in return signals received at the Lidar system. A noise source detector comprises a light sensor to receive a noise signal produced by a noise source and a timing circuit to provide a timing signal indicative of a direction of the noise source relative to an autonomous vehicle on which the Lidar system is mounted. A noise source may be an external Lidar system or a surface in the surrounding environment that is reflecting light signals such as those emitted by an external Lidar system.

Abstract: Aspects of the present disclosure involve systems, methods, and devices for mitigating Lidar cross-talk. Consistent with some embodiments, a method includes detecting a noise signal producing noise in one or more return signals being received by a Lidar unit of an autonomous vehicle (AV) system, and detecting a noise source corresponding to the noise signal. The detecting of the noise source comprises determining a direction of the noise source relative to the AV system and determining a classification of the noise source based on an intensity of the noise signal. The method further includes generating state data to describe the noise source based on the direction of the noise source relative to AV system and the classification of the noise source. The method further includes controlling one or more operations of the AV system based on the state data describing the noise source.

Abstract: Aspects of the present disclosure involve systems, methods, and devices for mitigating Lidar cross-talk. Consistent with some embodiments, a Lidar system is configured to include one or more noise source detectors that detect noise signals that may produce noise in return signals received at the Lidar system. A noise source detector comprises a light sensor to receive a noise signal produced by a noise source and a timing circuit to provide a timing signal indicative of a direction of the noise source relative to an autonomous vehicle on which the Lidar system is mounted. A noise source may be an external Lidar system or a surface in the surrounding environment that is reflecting light signals such as those emitted by an external Lidar system.

Abstract: Aspects of the present disclosure involve systems, methods, and devices for mitigating Lidar cross-talk. Consistent with some embodiments, a Lidar system is configured to include one or more noise source detectors that detect noise signals that may produce noise in return signals received at the Lidar system. A noise source detector comprises a light sensor to receive a noise signal produced by a noise source and a timing circuit to provide a timing signal indicative of a direction of the noise source relative to an autonomous vehicle on which the Lidar system is mounted. A noise source may be an external Lidar system or a surface in the surrounding environment that is reflecting light signals such as those emitted by an external Lidar system.

Abstract: A self-driving semi-truck can include tractor comprising a drive system, a first set of sensors mounted to the tractor, a fifth wheel, and cargo trailer comprising a kingpin coupled to the fifth wheel. The cargo trailer can include a Mansfield bar having a second set of sensors mounted thereto, where the second set of sensors have a rearward field of view from the trailer. The semi-truck can include an autonomous control system that receives sensor data from the first set of sensors and the second set of sensors, and analyzes the live sensor view to autonomously operate the drive system along a current route.

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: An autonomous control system of a self-driving semi-truck can monitor a dynamic orientation of a cargo trailer in relation to a tractor of the semi-trailer truck. Based on the dynamic orientation of the cargo trailer, the control system can dynamically generate a coordinate transform between a first reference frame of a first set of sensors mounted to the tractor, and a second reference frame of a second set of sensors mounted to the cargo trailer, and execute the dynamically generated coordinate transform on sensor data from the second set of sensors to generate a fused sensor view of a surrounding environment of the self-driving semi-truck.

Abstract: Aspects of the present disclosure involve systems, methods, and devices for mitigating Lidar cross-talk. Consistent with some embodiments, a Lidar system is configured to include one or more noise source detectors that detect noise signals that may produce noise in return signals received at the Lidar system. A noise source detector comprises a light sensor to receive a noise signal produced by a noise source and a timing circuit to provide a timing signal indicative of a direction of the noise source relative to an autonomous vehicle on which the Lidar system is mounted. A noise source may be an external Lidar system or a surface in the surrounding environment that is reflecting light signals such as those emitted by an external Lidar system.

Abstract: Aspects of the present disclosure involve systems, methods, and devices for mitigating Lidar cross-talk. Consistent with some embodiments, a method includes detecting a noise signal producing noise in one or more return signals being received by a Lidar unit of an autonomous vehicle (AV) system, and detecting a noise source corresponding to the noise signal. The detecting of the noise source comprises determining a direction of the noise source relative to the AV system and determining a classification of the noise source based on an intensity of the noise signal. The method further includes generating state data to describe the noise source based on the direction of the noise source relative to AV system and the classification of the noise source. The method further includes controlling one or more operations of the AV system based on the state data describing the noise source.

Abstract: A self-driving semi-truck can include tractor comprising a drive system, a first set of sensors mounted to the tractor, a fifth wheel, and cargo trailer comprising a kingpin coupled to the fifth wheel. The cargo trailer can include a Mansfield bar having a second set of sensors mounted thereto, where the second set of sensors have a rearward field of view from the trailer. The semi-truck can include an autonomous control system that receives sensor data from the first set of sensors and the second set of sensors, and analyzes the live sensor view to autonomously operate the drive system along a current route.

Abstract: An autonomous control system of a self-driving semi-truck can monitor a dynamic orientation of a cargo trailer in relation to a tractor of the semi-trailer truck. Based on the dynamic orientation of the cargo trailer, the control system can dynamically generate a coordinate transform between a first reference frame of a first set of sensors mounted to the tractor, and a second reference frame of a second set of sensors mounted to the cargo trailer, and execute the dynamically generated coordinate transform on sensor data from the second set of sensors to generate a fused sensor view of a surrounding environment of the self-driving semi-truck.

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.

Abstract: Systems and methods are directed to determining one or more angles and/or distances between at least first and second portions of a partially or fully autonomous vehicle. In one example, a system includes one or more processors and memory including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include obtaining sensor data. The operations further include determining at least one angle between a first portion and a second portion of an autonomous vehicle based at least in part on the sensor data. The operations further include determining at least one distance between the first portion and the second portion of the autonomous vehicle based at least in part on the sensor data. The operations further include providing the at least one angle and at least one distance for use in controlling operation of the autonomous vehicle.

Abstract: An autonomous semi-truck can include a cabin, a drive system operable to drive the autonomous semi-truck, and a configuration of sensors mounted to the cabin. The configuration of sensors can include at least one high-definition LIDAR sensor having a first field of view that encompasses a region in front of the autonomous semi-truck, and a set of sensors having fields of view that encompass side regions extending laterally from each side of a trailer coupled to the autonomous semi-truck. The autonomous semi-truck can further include a control system that receives sensor data from the at least one HD LIDAR sensor and the set of sensors and autonomously operates the drive system based on the received sensor data.