A method for detecting faults in a vehicle control system comprising functional units having an associated unique prime number label is provided. The method comprises calling each of the functional units, the call comprising a readable and updateable integer traversal value, and in case the functional unit is operating correctly, updating the traversal value to be the product of the value in the call and the label of the currently called functional unit, and in the case of a fault, not updating the traversal value. Further, the method comprises determining from the traversal value if any functional units are faulty by a comparison with an expected traversal value, and, in the case that the traversal value is not equivalent to the expected traversal value, determining which functional units are faulty by a unique prime factorization algorithm.

A method for planning a vehicle diagnosis in a vehicle includes: estimation of an operating characteristic of the vehicle on a route to be traveled by the vehicle; and planning of the vehicle diagnosis based on a probability that the estimated operating characteristic of the vehicle will correspond to an operating characteristic suitable for the vehicle diagnosis.

The present disclosure relates to a control system (2) for a vehicle. The control system is configured to receive a request to initiate a diagnostic conversation, and attempt to initiate the requested diagnostic conversation while the vehicle is in a sleep state. The control system is further configured to determine one or more target participants that are required for participation in a requested diagnostic conversation, to determine an on-board energy status of the vehicle, and to energise the target participants without energising the entire vehicle and initiate the requested diagnostic conversation in dependence on the on-board energy status of the vehicle being sufficient to conduct the requested diagnostic conversation.

Diagnosing a sensor processing unit of an autonomous driving vehicle is described. An example computer-implemented method can include transmitting an executable image of a sensor processing application from a host system to the sensor processing unit via at least one of a universal asynchronous receiver-transmitter (UART) or an Ethernet connection. The method also includes causing the sensor processing unit to execute and launch the executable image of the sensor processing application in the DRAM from the eMMC storage device. The method also includes transmitting a sequence of predetermined commands to the executed sensor processing application to perform a plurality of sensor data processing operations on sensor data obtained from a plurality of sensors or sensor simulators associated with an autonomous driving vehicle. The method also includes comparing processing results of the sensor processing operations against expected processing results to determine whether the sensor processing application operates properly.

A method for the self-check of at least one driving function of an autonomous or semi-autonomous vehicle in vehicle operation, after an error message about the at least one driving function, in which in one step, at least one vehicle electronic system and/or at least one sensor is/are restarted, a check is made for a further appearance of the error message after each restart, and in the event an error message is not repeated after the restart, the driving function in question is checked during operation of the autonomous or semi-autonomous vehicle.

A sensor acquisition unit acquires an output result of a sensor mounted in a vehicle. An arithmetic unit uses a machine learning algorithm in which the output result of the sensor acquired by the sensor acquisition unit is set as an input, to calculate an inference result for controlling the vehicle. A degree of reliability estimation unit determines the degree of similarity between the output result acquired by the sensor acquisition unit and training data which has been used for learning of the machine learning algorithm, and estimates the degree of reliability of the inference result calculated by the arithmetic unit on the basis of the degree of similarity. A control output unit adds the degree of reliability estimated by the degree of reliability estimation unit to the inference result calculated by the arithmetic unit, and outputs the inference result with the degree of reliability as vehicle control information.

A vehicle judder diagnostic method using artificial intelligence applied to a mobile-based GDS according to the present disclosure is characterized in that the mobile-based GDS samples a plurality of sensor signals of a sensor mounted in a vehicle in a vehicle during operation in a judder evaluation mode to quickly, separately diagnose whether the judder phenomenon of the vehicle is a geometric judder or a friction judder by mounting a deep neural network (DNN) model, developed by the trial and error process of a DNN by using the plurality of sensor signals of a test vehicle mounted with a double clutch transmission (DCT), as a judder determination artificial intelligence model 30 in the mobile-based GDS.

The invention relates to a method for checking an AI-based information processing system used in the partially automated or fully automated control of a vehicle, wherein at least one sensor of the vehicle provides sensor data, the captured sensor data are evaluated by an AI-based information processing system arranged in a first control circuit of the vehicle and, from the evaluated sensor data, at least one output for controlling the vehicle is generated. The AI-based information processing system is checked by a testing circuit arranged in a second control circuit of the vehicle using at least one testing method, and wherein a test result of the at least one testing method is stored, with a reference to the tested AI-based information processing system and to the at least one testing method used, in a multi-dimensional data structure in a database arranged in the vehicle.

Methods and systems are provided for operating a vehicle that includes a piezoelectric device. The piezoelectric device may harvest energy that may be transferred between two masses, such as a chassis and a vehicle suspension, to power electrical components of a vehicle. In addition, output of the piezoelectric device may be monitored to identify degradation of electrical connectors.

The present disclosure relates to improved diagnostic devices, systems and methods for improving the accuracy and speed of diagnosing datalink/CAN errors on public and private networks within the electronic control systems of machines. The present disclosure includes incorporating one or more diagnostic modules to one or more datalinks within an existing electronic control system. These diagnostic modules can monitor datalink voltages and identify and transmit error messages and datalink voltage anomalies without the need for manual diagnosis by a repair technician. Alternatively, another diagnostic system and method includes installing microchips having datalink voltage measurements to existing modules, such as a telematics module, thereby enabling that the module to identify and report datalink voltage anomalies.