A vehicle motion controller includes a feedback controlling unit that executes feedback control in which a difference between a target acceleration corresponding to a request value from a driver assistance device and an actual acceleration of a vehicle is an input, thereby calculating a control amount used to reduce the difference, a request outputting unit that calculates a request longitudinal force based on the control amount, the request longitudinal force controlling an actuator, and an obtaining unit that obtains, as availability, a range of a longitudinal force capable of being generated by the actuator, the availability being a controllable range of the longitudinal force. The feedback controlling unit prohibits the control amount from decreasing when the request longitudinal force is less than a minimum value in the availability.

A vehicle motion controller includes a feedback controlling unit that executes feedback control in which a difference between a target acceleration corresponding to a request value and an actual acceleration of a vehicle is an input, thereby calculating a control amount used to reduce the difference, a request outputting unit that calculates a request longitudinal force based on the control amount and outputs the request longitudinal force to the driving and braking devices, the request longitudinal force controlling the driving and braking devices, and a determining unit that, in a case where a driver of the vehicle is operating a braking operation member, obtains a braking command value and determines that operation interference by the driver has occurred when the braking command value is less than the request value. The feedback controlling unit prohibits the control amount from increasing in a case where the operation interference has occurred.

A method of estimating an operating parameter of industrial mechanical power transmission equipment is provided. The method includes acquiring data of a first parameter of the gearbox using a sensor, inferring a second parameter of a gearbox based on the acquired data of the first parameter by using a machine learning model, wherein the second parameter is of a different type from the first parameter and includes at least one of a torque of the gearbox, a rotational speed of the gearbox, or an overhung shaft force of the gearbox, and outputting the estimated second parameter.

A vehicle determines that a vehicle enhanced driving mode has been requested. The vehicle determines whether a condition for engaging such a mode has been met. Additionally, the vehicle offers a user-override, when the condition has not been met and the condition is a condition defined as over-rideable and responsive to override acceptance, engaging the requested driving mode. In other instances, the vehicle monitors mode usage against predefined maximum usage constraints, and may facilitate excess mode usage if constraints are exceeded.

Examples disclosed herein relate to an autonomous driving system in an ego vehicle. The autonomous driving system includes a radar system configured to detect and identify a target in a path and a surrounding environment of the ego vehicle. The autonomous driving system also includes a sensor fusion module configured to receive radar data on the identified target from the radar system and compare the identified target with one or more targets identified by a plurality of perception sensors that are geographically disparate from the radar system. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the ego vehicle.

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.

A failure cause analyzing system utilizes numerical data of vehicle equipment during vehicle operation and analyzes the equipment numerical data included in running data of the vehicle to select a failure inducible factor, thereby extracting the numerical data of each equipment from the running data of the vehicle even if a failure symptom does not persist and occurs intermittently, and analyzes the equipment numerical data to select the failure inducible factor, so as to reduce the time and the cost necessary for inspecting and repairing the vehicle equipment upon the occurrence of the failure symptom, and to avoid improper or excessive maintenance.

An automotive network switch includes multiple ports, a switch core and one or more processors. The ports are configured to receive packets from electronic subsystems of a vehicle over a computer network deployed in the vehicle, and to transmit the packets to other electronic subsystems of the vehicle over the computer network. The switch core is configured to receive the packets from one or more of the ports, to forward the packets to at least one of the ports, and to transmit the packets over network links of the computer network. The processors are configured to obtain at least some of the packets processed by the switch, to analyze the obtained packets to identify an anomaly in one or more of the electronic subsystems of the vehicle, and to send a notification of the anomaly over the computer network to a central processor that is external to the switch.

An abnormality detection device includes: an intake pressure sensor configured to detect an intake pressure of an internal combustion engine; an opening sensor configured to detect a throttle opening of the internal combustion engine; and a determination unit configured to determine, based on detection results of the intake pressure sensor and the opening sensor, whether the intake pressure sensor is abnormal. The determination unit determines, based on a result of intake pressure comparison between different throttle openings, whether the intake pressure sensor is abnormal.

The present disclosure describes a method for monitoring operations of an automated driving system (ADS) of a vehicle. For each monitored operation the method includes: determining a geographical position of the vehicle; determining an intended path of the vehicle; and determining one or more intended parameters associated with performing a driving manoeuvre of said vehicle from the determined geographical position along the intended path. For each monitored operation the method further includes: obtaining one or more parameters associated with performing the driving manoeuvre of said vehicle from said determined geographical position; and retrieving, from a statistical model, data indicative of a statistical distribution related to one or more corresponding intended and/or obtained parameters for said intended path. Based on said retrieved data, determining whether there is an anomaly associated with said monitored operation; and taking at least one action of a set of predefined actions if an anomaly is determined.