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.

An autonomous driving control apparatus may include a processor configured to determine a wear degree of a tire of a vehicle based on image data of the tire during autonomous driving of the vehicle, and to perform vehicle control depending on the wear degree of the tire; and a storage electrically connected to the processor and configured to store the image data and algorithms driven by the processor.

According to an aspect, there is provided a computer-implemented method for condition monitoring of a vehicle. The method comprises applying a dynamic model associated with a vehicle (800), the dynamic model having been determined by obtaining status information from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle (200), obtaining, based on vehicle identity information, vehicle dynamics information (202), obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data (204), analyzing behavior of the vehicle based on the status information and the vehicle dynamics information (206), and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data (208); analyzing historical changes in at least one calibration parameter associated with the dynamic model of the vehicle (802); analyzing effects of the three-dimensional road map associated with the roads and the road characteristics data in at least one position on the behavior of the vehicle (804); and determining, based on the analyzed historical changes and the effects, at least one change in at least one vehicle characteristic (806).

This disclosure relates to method and system for validating an Autonomous Vehicle (AV) stack. The method may include receiving an Operational Design Domain (ODD) and real-world data for evaluating at least one of an Advanced Driver Assistance System (ADAS) and the AV. The ODD is based on at least one feature of at least one of the ADAS and the AV. For each of a plurality of iterations, the method may further include generating a driving scenario based on the ODD of the AV and the real-world data through a Quality of Ride Experience (QoRE)-aware cognitive engine, plugging and running at least one of the ADAS and the AV algorithm based on the driving scenario, and determining a set of performance metrics corresponding to the at least one feature of at least one of the ADAS and the AV in the driving scenario based on the simulating.

A system includes an inertial navigation system module (INS module) that detects vehicle yaw rates and vehicle lateral speeds, a controller circuit communicatively coupled with the INS module. The controller circuit determines a tire cornering stiffness (C.sub.f, C.sub.r) based on vehicle physical parameters and vehicle dynamic parameters. The controller circuit determines a vehicle moment of inertia (Iz) based on the vehicle physical parameters, the vehicle dynamic parameters, and the tire cornering stiffness (C.sub.f, C.sub.r).

A travel control system for a vehicle provided with a drive source, a wheel having a wheel body connected to the drive source via a power transmission member and a tire mounted on the wheel body, and a braking device for braking the wheel includes: an estimation unit configured to estimate a tire torsional stiffness and a road surface friction coefficient based on at least the rotation speed of the drive source, the rotation speed of the wheel body, the vehicle body speed, and the torque applied to the wheel body; and a control unit configured to control at least one of the drive source and the braking device such that the tire does not exceed an adhesion limit derived from the tire torsional stiffness and the road surface friction coefficient.

Provided is an intelligent safe vehicle speed measurement method and an system capable of considering a state of a road surface, including: a laser scanning unit configured to obtain road surface textures and simulate contact under different vehicle loads and tire patterns; a wireless communication unit configured to obtain the model, load and tire information of incoming vehicles, offer a best-matching braking distance by search through database; a skid resistance prediction unit configured to obtain environmental parameters to correct the obtained best-matching braking distance, and offer a safe speed for incoming vehicles; and a warning reminder unit configured to broadcast the safe running speed to incoming vehicles. This system communicates between the vehicle and road, obtains the information from incoming vehicles in real time, and broadcasts the safe speed to incoming vehicles, ensuring the safety of the incoming vehicle during running.

A system and to a method executed in a vehicle control unit for controlling wheel slip of a vehicle, wherein the vehicle comprises at least two wheels driven by at least primary actuator via an open differential. The primary actuator is controlled to rotate at a speed resulting in a slip λ.sub.em of the primary actuator. A signed wheel slip limit λ.sub.lim is determined by adding a configurable value to the slip λ.sub.em of the primary actuator, such that λ.sub.lim>λ.sub.em. The at least two wheels are controlled to rotate at wheel speeds resulting in respective wheel slips λ.sub.l, λ.sub.r below the signed wheel slip limit λ.sub.lim, wherein each one of λ.sub.l, λ.sub.r and λ.sub.em are signed numerical values.

Realized is a technique of highly accurately calculating a state quantity of a vehicle. An ECU (600) of a vehicle (900) includes a ground contact load calculating section (610), an input quantity calculating section (620), a first state quantity calculating section (630), an observable calculating section (640), a second state quantity calculating section (650), and a damper ECU (660). The ECU (600) calculates a first state quantity of the vehicle (900) by inputting, into a vehicle model, a value calculated from a G sensor value and/or the like, and calculates a second state quantity of the vehicle (900) by correcting the first state quantity with use of an observable which is calculated from a ground contact load and a spring constant gain of a tire.

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.