An agricultural machine includes one or more tires, a detector to detect a low pressure state in which a pressure of one of the tires is lower than a reference range or a high pressure state in which the tire pressure is higher than the reference range, and a controller to control an operation of at least one of the agricultural machine and an additional agricultural machine to be linked to the agricultural machine. One of the agricultural machine and the additional agricultural machine is a work vehicle that is capable of self-driving, and the other one is an implement to be linked to the work vehicle. The controller causes, in response to detection of the low or high pressure state, at least one of the agricultural machines to perform a specific operation that is different from an operation to be performed when the pressure is in the reference range.

A road surface condition assessing device includes: a tire-mounted device; and a vehicle body system. The tire-mounted device includes: a vibration detector that outputs a detection signal of a vibration on a tire; a waveform processor that generates the road surface data; and a first data communication unit. The vehicle body system includes: a second data communication unit; and a road surface evaluation unit that evaluates the road surface condition. The tire-mounted device transmits an advertise signal including the road surface data indicative of a result of a waveform process on the detection signal and a waveform processing value corresponding to the road surface condition. The vehicle body system evaluates the road surface condition based on the waveform processing value.

The technology relates to determining whether a vehicle operating in an autonomous driving mode is experiencing an anomalous condition, for instance due to a loss of tire pressure, a mechanical failure, or a shift or loss of cargo. The actual current pose of the vehicle is compared to an expected pose of the vehicle, where the expected pose is based on a model of the vehicle. If a pose discrepancy is identified, the anomalous condition is determined from information associated with the pose discrepancy. The vehicle is then able to take corrective action based on the nature of the anomalous condition. The corrective action may include making a real-time driving change, modifying a planned route, alerting a remote operations center, or communicating with one or more other vehicles.

Systems and methods are disclosed herein for automatically controlling wheel lean in a work vehicle (e.g., a motor grader) comprising a front portion with an axle and a plurality of traction wheels configured to lean at a wheel-lean angle relative thereto. Based on output signals from one or more sensors mounted on the work vehicle, work conditions are detected comprising an actual wheel-lean angle of at least one wheel relative to the axle, an oscillation angle of the axle, and a slope of the terrain. In automatic control operations, wheel lean is automatically directed to a predetermined orientation (e.g., corresponding to a direction of gravity), based at least on detected work conditions. Wheel lean may further be automatically directed based on detected steering inputs for positioning of the traction wheels and a detected articulation angle for positioning of the front portion of the work vehicle relative to the rear portion.

The present disclosure relates to a method for controlling performance of a vehicle by estimating a center of gravity of the vehicle, the method comprising: receiving by a vehicle control unit, load parameters describing features of a load in the vehicle, collecting, by the vehicle control unit, a load weight measure of a load in the vehicle, from a load sensor, when the vehicle is in a steady state; and computing, by the vehicle control unit, using a neural network, longitudinal and lateral positions and height of a center of gravity of the vehicle, based on the load parameters and the load weight measure.

Provided is a travel evaluation method of making an evaluation related to travel of a vehicle capable of traveling in a leaning position, the method including: obtaining a tire force which is an external force exerted on a wheel of the vehicle from a ground surface; and deriving an evaluation index related to travel of the vehicle. The evaluation index includes a positive evaluation index as a rating of a positive evaluation related to travel of the vehicle. In deriving the evaluation index, the positive evaluation index is set higher as the tire force increases, and the evaluation index is corrected based on an influential parameter other than the tire force.

A device for estimating a state of a road surface on which a tire is running, the device including: an acceleration sensor 11 installed in the tire; an acceleration information acquiring means 12, 13, 14 that acquires acceleration information input to the tire from an output of the acceleration sensor 11; a storage means 15 that stores acceleration information of each road surface roughness set in advance; and a road surface state estimating means 16 that compares the acquired acceleration information with the acceleration information of each road surface roughness stored in the storage means 15 so as to estimate the state of the road surface.

The technology relates to determining whether a vehicle operating in an autonomous driving mode is experiencing an anomalous condition, for instance due to a loss of tire pressure, a mechanical failure, or a shift or loss of cargo. The actual current pose of the vehicle is compared to an expected pose of the vehicle, where the expected pose is based on a model of the vehicle. If a pose discrepancy is identified, the anomalous condition is determined from information associated with the pose discrepancy. The vehicle is then able to take corrective action based on the nature of the anomalous condition. The corrective action may include making a real-time driving change, modifying a planned route, alerting a remote operations center, or communicating with one or more other vehicles.

A computer-implemented method for training an artificial intelligence module to determine a tire type of a motor vehicle is disclosed. The method includes providing a measured value dataset on a data carrier, wherein the measured value dataset contains at least one data entry regarding ultrasound data, speed data and tire data, wherein the ultrasound data describe at least one ultrasonic wave that was produced by rolling of a tire of the motor vehicle, wherein the speed data describe a speed of the motor vehicle, wherein the tire data describe a tire type of the motor vehicle. The method further includes generating a modified training dataset based on the measured value dataset. Generating the modified training dataset includes (i) forming an input dataset based on the ultrasound data and the speed data of the measured value dataset, (ii) forming an output dataset based on the tire data of the measured value dataset, and (iii) training the AI module based on the modified training dataset.

Embodiments of the present application disclose a positioning method and apparatus, an autonomous driving vehicle, an electronic device and a storage medium, relating to the field of autonomous driving technologies, comprising: collecting first pose information measured by an inertial measurement unit within a preset time period, and collecting second pose information measured by a wheel tachometer within the time period; generating positioning information according to the first pose information, the second pose information and the adjacent frame images; controlling driving of the autonomous driving vehicle according to the positioning information. The positioning information is estimated by combining the first pose information and the second pose information corresponding to the inertial measurement unit and the wheel tachometer respectively. Compared with the camera, the inertial measurement unit and the wheel tachometer are not prone to be interfered by the external environment.