A moving machine control program causes a computer to execute: acquiring requested external force regarding an actuator; reading out a reference kinetic model that defines moving machine behavior exhibited when the actuator generates external force corresponding to the requested external force; calculating, as requested moving machine behavior, the moving machine behavior exhibited when the actuator generates the external force corresponding to the requested external force, in accordance with the reference kinetic model; measuring actual moving machine behavior during traveling of the moving machine; correcting the requested external force such that the actual moving machine behavior measured in the measuring step approaches the requested moving machine behavior calculated in the calculating step; and controlling the actuator based on the corrected requested external force.

The present invention obtains a control system and a control method capable of appropriately suppressing front lift-up of a straddle-type vehicle.

In the control system and the control method according to the present invention, damping forces of suspensions and drive power generated to the straddle-type vehicle are controlled. The drive power adjustment control is executed to adjust the drive power generated to the straddle-type vehicle so as to suppress the front lift-up that causes a front wheel of the straddle-type vehicle to lift off from the ground, and initiation timing of the drive power adjustment control is controlled by using a physical quantity to which states of the suspensions are reflected.

Methods and apparatus to compensate for body roll in vehicle weight calculations are disclosed. An example method includes receiving sensor data from sensors of a vehicle, determining a weight of the vehicle and determining a body roll of the vehicle. The example method further includes comparing the body roll to a threshold and, if the body roll satisfies the threshold, adjusting the determined weight of the vehicle based on the determined body roll and properties of a suspension system of the vehicle.

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.

An electronic controller (10) for a motor vehicle (100), the controller being configured to determine when at least one wheel (111, 112, 114, 115) has lost traction, wherein when the controller (10) determines that at least one wheel (111, 112, 114, 115) has lost traction the controller (10) is configured to provide an output to a driver indicative of the at least one wheel (111, 112, 114, 115) that has lost traction.

Technologies and techniques for operating an assistance system in a motor vehicle with which a driving a shoulder by the motor vehicle is detected by means of at least one detection device in the assistance system, and with which a control signal is generated by means of an electronic computing device in the assistance system for engaging with a functional unit in the motor vehicle. The shoulder that is detected is evaluated in terms of the type of shoulder, and a critical or non-critical driving of the vehicle on the shoulder is determined on the basis of the type of shoulder, and the control signal is generated in the case of a critical driving on the shoulder. The present disclosure also relates to an assistance system.

The cloud includes a server and a storage device. The storage device includes a road surface information map. When a first sampling distance is equal to or longer than a first distance threshold, the server performs re-sampling to interpolate data in such a manner that sampling positions located at a second sampling distance and unsprung mass member displacements of the respective sampling positions exist so as to produce re-sampled data-for-producing-map. The server stores a sub-sectional unsprung mass displacement in a storage area corresponding to a sub-section of the road surface information map, based on the re-sampled data-for-producing-map.

A commercial vehicle with a cargo space includes a detection unit for detecting movement of a wheel suspension and/or an inertial measuring unit for detecting movements of a sprung mass of the vehicle in order to evaluate a loading state of cargo in the cargo space. To provide improved cargo capacity, improved user-friendliness, and improved driving behavior, the commercial vehicle has an optical signaling unit arranged in the cargo space which is electrically activatable using an evaluation electronics unit, wherein the evaluation electronics unit is configured to activate the signaling unit on the basis of signals of the detection unit and/or the inertial measuring unit which are generated upon placement of the cargo on a cargo surface of the cargo space in such a way that an optimum position of the cargo surface is displayable by means of the signaling unit.

The present technology is directed to dynamically adjusting an autonomous vehicle (AV) system based on deep learning optimizations. An AV management system can generate a downscaling signal based on a result of comparing a complexity of an environment for an AV to navigate with a predetermined complexity threshold. Further, the AV management system can perform a downscaling of a neural network associated with an AV system based on the downscaling signal and determine a scenario to test the downscaled neural network in a simulation. The AV management system can adjust one or more parameters of the AV system based on simulated outputs and perform the simulation of the AV based on the adjusted one or more parameters of the AV system and the downscaled neural network to generate simulated performance data. Furthermore, the AV management system can compare the simulated performance data with a predetermined performance threshold.