A method includes receiving a request from a vehicle to perform a computing task, selecting a machine learning model from among a plurality of machine learning models based at least in part on the request, and predicting an amount of computing resources needed to perform the computing task using the selected machine learning model.

A mobile object control method including: recognizing physical objects near a mobile object and a route shape; generating a target trajectory based on a result of the recognition and cause the mobile object to travel autonomously along the target trajectory; and determining that an abnormality has occurred in a control system for causing the mobile object to travel autonomously by performing the recognition when a time period from a timing when a degree of deviation between a reference target trajectory determined by the route shape and serving as a reference for generating the target trajectory and a position of the mobile object is greater than or equal to a predetermined degree to a timing when the degree of deviation is less than the predetermined degree is greater than or equal to a first predetermined time period and output a determination result.

A mobile object control method including: recognizing physical objects near a mobile object and a route shape; generating a target trajectory based on a result of the recognition and cause the mobile object to travel autonomously along the target trajectory; and determining that an abnormality has occurred in a control system for causing the mobile object to travel autonomously by performing the recognition when a time period from a timing when a degree of deviation between a reference target trajectory determined by the route shape and serving as a reference for generating the target trajectory and a position of the mobile object is greater than or equal to a predetermined degree to a timing when the degree of deviation is less than the predetermined degree is greater than or equal to a first predetermined time period and output a determination result.

An apparatus on a vehicle comprises one or more sensors, one or more nozzles that output fluid to clean the respective one or more sensors, and a compressor that generates fluid such as compressed air. The compressor is in fluid communication with the one or more nozzles. The apparatus further comprises one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to determine a current velocity of the vehicle and control an operation of the compressor based on the current velocity of the vehicle.

An apparatus on a vehicle comprises one or more sensors, one or more nozzles that output fluid to clean the respective one or more sensors, and a compressor that generates fluid such as compressed air. The compressor is in fluid communication with the one or more nozzles. The apparatus further comprises one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to determine a current velocity of the vehicle and control an operation of the compressor based on the current velocity of the vehicle.

An Autonomous Vehicle (AV) system, including: a tracking subsystem configured to detect and track relative positioning of another vehicle that is behind or lateral to an AV configured to comply with a safety driving model, and to check a safety driving model compliance status of the other vehicle; and a risk reduction subsystem configured to plan, based on the safety driving model compliance status of the other vehicle, an AV action, wherein if the safety driving model compliance status of the other vehicle is unknown or is known to be non-compliant, the AV action is administration of a safety driving model compliance test to the other vehicle, or is a maneuver by the AV to reduce risk of collision with a leading vehicle positioned in front of the AV.

An Autonomous Vehicle (AV) system, including: a tracking subsystem configured to detect and track relative positioning of another vehicle that is behind or lateral to an AV configured to comply with a safety driving model, and to check a safety driving model compliance status of the other vehicle; and a risk reduction subsystem configured to plan, based on the safety driving model compliance status of the other vehicle, an AV action, wherein if the safety driving model compliance status of the other vehicle is unknown or is known to be non-compliant, the AV action is administration of a safety driving model compliance test to the other vehicle, or is a maneuver by the AV to reduce risk of collision with a leading vehicle positioned in front of the AV.

Methods for reversing determination for a vehicle asset are provided. The methods include capturing by a telematics device coupled to the vehicle acceleration data from a three-axis accelerometer, determining by a reversing-determination machine learning mode, a machine-learning-determined reversing indication for the vehicle asset. The reversing-determination machine-learning model being trained by a vehicle reversing indication comprising a vehicle speed and a reverse gear indication.

Methods for reversing determination for a vehicle asset are provided. The methods include capturing by a telematics device coupled to the vehicle acceleration data from a three-axis accelerometer, determining by a reversing-determination machine learning mode, a machine-learning-determined reversing indication for the vehicle asset. The reversing-determination machine-learning model being trained by a vehicle reversing indication comprising a vehicle speed and a reverse gear indication.

Controlling an actual slip of at least one driven axle of a motor vehicle with at least one axle having at least one wheel and a one drive unit for providing a drive torque for the axle and for the wheel can be carried out by a control device for controlling the drive unit. The control device can be configured for establishing a first actual speed of the motor vehicle; establishing a second actual speed of the at least one wheel; calculating a target speed of the at least one wheel for the established first actual speed taking into account parameters; determining an actual slip of the at least one wheel with respect to a substrate on which the motor vehicle is being moved; when the actual slip exceeds a defined first limit slip, generating a limit torque by which the drive torque produced by the drive unit is adjusted.