According to an aspect of an embodiment, operations may comprise receiving, from a LIDAR mounted on a vehicle, a first 3D point cloud comprising points of a region around the vehicle as observed by the LIDAR. The operations may also comprise accessing an HD map comprising a second 3D point cloud comprising points of the region around the vehicle. The operations may also comprise segmenting LIDAR ground points from LIDAR non-ground points in the first 3D point cloud. The operations may also comprise segmenting map ground points from map non-ground points in the second 3D point cloud. The operations may also comprise determining a pose of the vehicle by matching the LIDAR ground points to the map ground points and by matching the LIDAR non-ground points to the map non-ground points.

A utility vehicle includes: a travel structure including a front wheel, a rear wheel, a steering structure mounted to the front wheel, and a drive source that drives the front wheel and/or the rear wheel; circuitry that controls the travel structure to effect autonomous travel without manned operation in a given travel area; and a vehicle location detector that detects a location of the utility vehicle. During the autonomous travel, the circuitry determines, based on road condition data where the travel area is divided into regions with different predetermined road condition levels, to which of the road condition levels a road condition of a region ahead of the location of the utility vehicle in a travel direction belongs, and the circuitry controls the travel structure such that a given travel parameter is within a corresponding one of permissible ranges predetermined respectively in association with the road condition levels.

A vehicle and associated method for calculating tire saturation is provided. The method may include the stability control computer calculating slip ratio and longitudinal force for the tire, calculating tire longitudinal stiffness by dividing longitudinal force by slip ratio, calculating tire saturation from tire longitudinal stiffness, and the stability control computer altering dynamic control of the vehicle based on calculated tire saturation. The stability control computer may calculate tire saturation from a tire saturation membership function which includes a first tire longitudinal stiffness value representing an unsaturated tire, a second tire longitudinal stiffness value representing a saturated tire, and a function line connecting the first tire longitudinal stiffness value to the second tire longitudinal stiffness value.

An apparatus including an interface and a processor. The interface may be configured to receive area data and sensor data from a plurality of vehicle sensors. The processor may be configured to extract road characteristics for a location from the area data, predict expected sensor readings at the location for the plurality of sensors based on the road characteristics, calculate dynamic limits for the sensor data in response to the expected sensor readings and determine a plausibility of the sensor data received from the interface when the vehicle reaches the location. The sensor data may be plausible if the sensor data is within the dynamic limits. A confidence level of the sensor data may be adjusted in response to the plausibility of the sensor data.

A method and system for estimating surface roughness of a ground for an off-road vehicle to control an implement comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A first sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A second sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle.

A method and system for estimating surface roughness of a ground for an off-road vehicle to control steering of a vehicle, an implement, or both, comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A first sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A second sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle.

A powered balancing mobility device that can provide the user the ability to safely navigate expected environments of daily living including the ability to maneuver in confined spaces and to climb curbs, stairs, and other obstacles, and to travel safely and comfortably in vehicles. The mobility device can provide elevated, balanced travel.

A method and system for estimating surface roughness of a ground for an off-road vehicle to control ground speed comprises detecting motion data of an off-road vehicle traversing a field or work site during a sampling interval. A pitch sensor is adapted to detect pitch data of the off-road vehicle for the sampling interval to obtain a pitch acceleration. A roll sensor is adapted to detect roll data of the off-road vehicle for the sampling interval to obtain a roll acceleration. An electronic data processor or surface roughness index module determines or estimates a surface roughness index based on the detected motion data, pitch data and roll data for the sampling interval. The surface roughness index can be displayed on the graphical display to a user or operator of the vehicle, or applied to control or execute a ground speed setting of the vehicle.

A system to reduce VRU unreferenced heading drift error is disclosed. The VRU comprises an IMU and a processor, which hosts first and second modules for bias cancelation. The first module reads inertial data from the IMU when the VRU is powered on; determines whether the VRU is static for a time period; if the VRU is static, corrects gyroscope bias by subtracting an initial bias value from a previous bias value; sets a predefined initial yaw value. The second module reads inertial data from the IMU during in-run operation of the VRU; updates roll, pitch and yaw data, based on input data from a sensor fusion algorithm; outputs updated roll, pitch and yaw data; determines whether the VRU is static for a time period; and if the VRU is static, corrects the bias by subtracting a current bias value, multiplied by a predefined parameter, from a previous bias value.

A system and corresponding method for autonomous driving of a vehicle are provided. The system comprises at least one neural network (NN) that generates at least one output for controlling the autonomous driving. The system further comprises a main data path that routes bulk sensor data to the at least one NN and a low-latency data path with reduced latency relative to the main data path. The low-latency data path routes limited sensor data to the at least one NN which, in turn, employs the limited sensor data to improve performance of the at least one NN's processing of the bulk sensor data for generating the at least one output. Improving performance of the at least one NN's processing of the bulk sensor data enables the system to, for example, identify a safety hazard sooner, enabling the autonomous driving to divert the vehicle and avoid contact with the safety hazard.