An artificial intelligence device mounted on a vehicle is provided. A sensing unit acquires a gyroscope sensor value, an acceleration sensor value, a GPS sensor value, and a proximity sensor value. If the acquired data satisfies a predetermined reference value, a processor inputs the acquired sensor values to an artificial intelligence model, acquires whether an impact requiring self-diagnosis occurs and impact direction information as a result value, selects an ECU module to perform self-diagnosis according to the acquired result value, and performs self-diagnosis.

A system for detecting anomalies in electrical wiring in a truck trailer. The system measures and compares current drawn from the truck tractor with the current drawn by a circuit at a particular trailer component or other location in the trailer wiring. If the two current measurements differ in excess of a predetermined threshold, the system can report a wiring anomaly. The location of the anomaly can then be determined by performing the operation for different trailer components thus testing multiple different branches of the trailer power distribution circuit, and using the results of these tests to determine that an anomaly is present, and possibly the location of the issue. The system may then send a notification to a remote computing device, to the truck tractor, or any combination thereof.

This travel control device is provided with a controller for controlling travel of a vehicle fitted with tires, using automated driving. The vehicle travels on a course including a testing section for testing the tire. The controller controls travel of the vehicle so that the vehicle travels on the testing section based on a prescribed standard for testing of the tire.

An apparatus for managing vehicle data collection including a remote access execution circuit, a property translation circuit, a parameter acquisition circuit, a remote operation circuit, and a parameter conditioning circuit. The remote access execution circuit interprets a remote access request value including a requested vehicle property and/or a vehicle function value. The property translation circuit determines: a property request value responsive to the requested vehicle property; and/or an actuator command value responsive to the vehicle function value. The parameter acquisition circuit interprets vehicle parameter values responsive to the property request value. The remote operation circuit provides the actuator command value to a vehicle network zone endpoint. The parameter conditioning circuit generates, responsive to the property request value, vehicle property data from the vehicle parameter values. The vehicle property data corresponds to the requested vehicle property. The remote access execution circuit transmits the vehicle property data to the requesting device.

A refuse vehicle comprising a chassis, a body assembly coupled to the chassis, the body assembly defining a refuse compartment, and a thermal event monitoring system comprising one or more sampling elements configured to sample an environmental condition associated with a portion of the refuse vehicle and a processing circuit configured to receive a sample from the one or more sampling elements and determine a presence of a thermal event indicating at least one of a fire or an overheating component.

A control unit arranged to monitor a load distribution of a vehicle when the vehicle is in motion, the control unit comprising an interface arranged to receive a plurality of load values associated with one or more vehicle axles and/or one or more vehicle wheels from a plurality of load sensors arranged on the vehicle, wherein the control unit is arranged to determine a vehicle load distribution based on the plurality of load values, to compare the determined vehicle load distribution to a pre-determined allowable vehicle load distribution, and to trigger an emergency procedure in case the determined vehicle load distribution differs from the pre-determined vehicle load distribution by more than an allowable amount.

A fault diagnosis device 80 includes an input unit 81 and a generation unit 82. The input unit 81 receives input of fault data obtained from a vehicle when a fault of the vehicle occurs and observation data observed in time series by each device of the vehicle until immediately before the fault occurs. The generation unit 82 generates a feature master that associates a content of the fault indicated by the fault data with features extracted from the corresponding observation data.

Methods and apparatus are provided for diagnosing a vehicle. In one embodiment, a method includes: initiating, by a processor, a recording of a noise by at least one microphone based on user selection data from a user of the vehicle; receiving, by the processor, audio signal data based on the recording; generating, by the processor, vector data based on the audio signal data; processing, by the processor, the vector data with at least one trained machine, by the processor, learning model to determine a classification of the noise; predicting, by the processor, an action to be taken based on the classification; and storing, by the processor, the audio signal data, the classification, and the action in a datastore.

A vehicle and a system and method for operating a vehicle. The system includes a state estimator and a processor. A detected value of a parameter of the vehicle is determined using sensor data obtained by in-vehicle detectors. The processor determines a check value of the parameter based on crowdsourced data, validates the detected value of the parameter based on the check value of the parameter, and operates the vehicle based on the validation.

System for automatically classifying vehicle vocation and benchmarking vehicle performance relative to other vehicles having the same vocation classification, independent of vehicle fleet groupings, industry vehicle application groupings and vehicle type groupings, is disclosed. The system includes a vehicle vocation classifier in communication with a data management system to store historical vehicle data including recurring vehicle usage data, and assign one or more predicted vocations for each vehicle based on the recurring vehicle usage data using a machine learning technique. The system also includes a benchmarking management system for grouping the historical vehicle data for vehicles of same determined predicted vocation, determining therefrom benchmarking vehicles having better performance characteristics than other vehicles of the same determined predicted vocation, and benchmarking performance of the other predicted vocation vehicles relative to the benchmarking vehicles.