An automated vehicle diagnostic navigation system and method includes a diagnostic computer having a diagnostic application program operable to perform a diagnostic evaluation of a vehicle by performing a diagnostic scan of an electronic system of the vehicle, where the diagnostic application program includes a hierarchical structure for initiation of a diagnostic scan of the vehicle based on the make, model and year of the vehicle, and includes a diagnostic navigation program configured to interface with the diagnostic application program, with the diagnostic navigation program configured to receive an input of the make, model and year of the vehicle. The diagnostic navigation program is operable to provide automated sequential inputs to the diagnostic application program based on the make, model and year of the vehicle to navigate the hierarchical structure of the diagnostic application program to initiate and perform a diagnostic scan of the vehicle.

A vehicle diagnostic system includes: a vehicle diagnostic device including a communication unit that communicates with a vehicle which drives autonomously, and a diagnostic unit that performs, via the communication unit, diagnosis as to whether the vehicle is being hacked; and electrical apparatuses that communicate with the vehicle diagnostic device via a network. The diagnostic unit performs the diagnosis when an operational state of at least one electrical apparatus among the electrical apparatuses changes.

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 devices are provided for uploading driving data to a blockchain network. The method is executed at a vehicle node in the blockchain network and includes: packing driving data of the vehicle node within a predetermined time interval every predetermined time interval to obtain a vehicle data packet of the vehicle, and storing the vehicle data packet locally in the vehicle node; broadcasting the vehicle data packet to other vehicle nodes located nearby and in the blockchain network for the other vehicle nodes to receive and store; receiving and storing other vehicle data packets broadcast by the other vehicle nodes located nearby and in the blockchain network; and when connecting to a fixed node that belongs to the blockchain network, synchronizing the vehicle data packet and the other vehicle data packets as stored to the fixed node, wherein the fixed node participates in the consensus of the blockchain network.

A vehicle data processing method and device are provided. The vehicle data processing method includes: acquiring vehicle data; determining a degree of completion of a preset goal according to the vehicle data; and determining incentive reference data associated with an incentive to a user according to the degree of completion of the preset goal.

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.

An information processing device includes: a decision unit that decides upper limits of respective individual evaluation values for a plurality of calculation items such that a total of the upper limits is a previously determined value, the individual evaluation values being relevant to driving of a driver, the calculation items being calculation items for which the individual evaluation values are calculated; an acquisition unit that acquires vehicle information relevant to a vehicle; a calculation unit that calculates the respective individual evaluation values for the calculation items within the upper limits decided by the decision unit, based on the vehicle information acquired by the acquisition unit; and a control unit that displays, on a display unit, the respective individual evaluation values for the calculation items that are calculated by the calculation unit and a total evaluation value that is a total of the respective individual evaluation values for the calculation items.

A system for controlling a vehicle includes a terminal that collects at least one of a fault code of at least one controller of the vehicle, operation state data of the vehicle at a time the fault code is generated, and update data of the at least one controller, and a server that receives at least one of the fault code, the operation state data, or the update data of the at least one controller, in which the server labels fault code differently to classify the fault code depending on whether there is an update history of the at least one controller in which the fault code is generated.

Systems and methods for determining the performance of a driver of a vehicle based on changes, over time, in the context and environment in which the vehicle operates, and any resultant driver behavior are disclosed. A set of driver response data is created from based on an analysis of time-series data indicative of the driver's operation of the vehicle in conjunction with time-series data indicative of changes in the vehicle's context/environment. The driver response data indicates the types and magnitudes of the driver's responses to various changes in the vehicle's operating context/environment and the driver's time-to-respond for each of the responses. That is, the driver response data indicates how a driver compensated his or her behavior (if at all) in response to different changes in the vehicle's context and/or environment. The driver response data may be compared to one or more thresholds to determine the driver's performance.

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.