Patent Description:
The present disclosure relates generally to in-vehicle networks, and particularly to methods and systems for diagnosing faults in cables of in-vehicle networks.

Modern vehicles, including, in particular, autonomous vehicles, operate while generating very large amounts of data that is being sensed or otherwise detected, analyzed, and transmitted over the in-vehicle network via cables. Faults in these cables may result in the in-vehicle network functioning at reduced speeds, functioning incorrectly, or not functioning at all. Faults in the cables typically result from aging and physical damage. Should the cables fail, or function improperly, the vehicle may become unsafe or inoperative, with repairs to the vehicle and the in-vehicle network being costly.

The description above is presented as a general overview of related art in this field and should not be construed as an admission that any of the information it contains constitutes prior art against the present patent application. <CIT> relates to an apparatus and method for measuring echo responses of communication links used in in-vehicle networks with high resolution and high dynamic range, allowing for diagnostics of various failures and/or degradations with high precision. Additional information can be provided to indicate signaling quality, insertion loss, and return loss of the communication links used in in-vehicle networks.

It is the object of the present invention to enable an improved fault diagnosis in one or more cables of a vehicular communication network.

The present invention is defined by the attached independent claims. Other preferred embodiments may be found in the dependent claims. The present application discloses a method for fault diagnosis in one or more cables (118a, 118b) of a vehicular communication network (<NUM>), the method comprising:obtaining at least one return echo signal corresponding to a signal communicated over a cable (118a, 118b) in a vehicle (<NUM>);applying, to the at least one return echo signal, a computer trained model (<NUM>) that distinguishes among a plurality of predetermined fault types in cables (118a, 118b), so as to identify a predetermined fault type, based on characteristics of the return echo signal learned from computer analysis of a multiplicity of previous echo signals, from amongst at least two different predetermined fault types, in the cable (118a, 118b), wherein the characteristics of the return signal include a shape of the return echo signal, and wherein the applying the computer trained model (<NUM>) includes analyzing the shape of the return echo signal against a predetermined shape of at least two return echo signals, each of the predetermined shapes associated with a predetermined fault type from amongst at least two different predetermined fault types; and initiating an action in response to the identified predetermined fault type. The present application discloses a system for diagnosing faults in one or more cables (118a, 118b) of a vehicular communication network (<NUM>), the fault diagnosis system comprising:one or more processors (<NUM>);a program memory (<NUM>) storing executable instructions that, when executed by the one or more processors (<NUM>), cause the system to:obtain at least one return echo signal corresponding to a signal communicated over a cable (118a, 118b) in a vehicle (<NUM>);apply, to the at least one return echo signal, a computer trained model (<NUM>) that distinguishes among a plurality of predetermined fault types in cables (118a, 118b),so as to identify a predetermined fault type, based on characteristics of the return echo signal learned from computer analysis of a multiplicity of previous echo signals, from amongst at least two different predetermined fault types, in the cable (118a, 118b);apply the computer trained model (<NUM>) to analyze the characteristics of the return echo signal including a shape of the return echo signal, to determine the probability of the identified predetermined fault type from amongst at least two different predetermined fault types, in the cable (118a, 118b); and initiate an action in response to the identified fault type. The present application discloses an Ethernet physical layer, PHY, device (<NUM>) for communication over a cable (118a, 118b) in an automotive or industrial network, the Ethernet PHY device (<NUM>) comprising:a transmitter (<NUM>) for transmitting outbound Ethernet communication signals to the cable (118a, 118b);a receiver (<NUM>) for receiving inbound Ethernet communication signals from the cable (118a, 118b), the inbound Ethernet communication signals comprising return echo signals caused by the transmitted outbound Ethernet communication signals;an echo canceller (<NUM>) for estimating the return echo signals and canceling the return echo signals in the received inbound Ethernet communication signals;storage media (<NUM>) in communication with the echo canceller (<NUM>) for storing the estimated return echo signals, the stored estimated return echo signals being available for readout by a device external to the ethernet physical layer device (<NUM>); and a processor (<NUM>) configured for determining, based on the shape of the return echo signals, the probability of the identified predetermined fault type from amongst at least two different predetermined fault types, in the cable (118a, 118b).

<FIG> shows a vehicle <NUM>, for example, an automobile, with an in-vehicle network (IVN) <NUM>, also known as a vehicular communication network, these terms used interchangeably herein. The vehicle <NUM>, may be a standard non-autonomous vehicle, or an autonomous vehicle, classified at any of Society of Automotive Engineers (SAE) Levels, <NUM> No Automation, <NUM> Driver Assistance, <NUM> Partial Automation, <NUM> Conditional Automation, <NUM> High Automation, <NUM> Full Automation. The vehicular communication network includes, for example, integrated circuit (IC) chips for performing various functions. There are also multiple cables, which connect the various components of the vehicular communication network. With this arrangement in the IVN, the disclosed subject matter works on the link and network levels.

The vehicular communication network <NUM> includes processors <NUM>, for instance Central Processing Units (CPUs), Graphics Processing Units (GPUs) or other suitable computer processing device, which are representative of a vehicle computer, also known herein as a local computer. The processors <NUM> communicate with controllers <NUM> and switches/gateways <NUM>, via physical layer devices (PHYs) <NUM>, over Ethernet links 118a, directly or indirectly. The switches/gateways <NUM> communicate with each other via PHYs <NUM> over Ethernet links 118a, directly or indirectly. The switches/gateways <NUM> communicate with PHYs <NUM> over Ethernet links 118b, directly or indirectly.

The links 118a, 118b comprise, for example, cables, also known as ethernet cables, for transmission of data and/or for delivering electrical power. The links 118a, 118b are also referred to hereinafter as "cables" or "ethernet cables". The cable types may be shielded or unshielded, single stranded or multi-stranded, with the multi-stranded cables being, for example, twisted pair cables. The data carrying capacity may not be the same for all cable types. For example, in an embodiment, the ethernet links 118a support <NUM>/<NUM> Gbps data rates, while the Ethernet links 118b support <NUM>/<NUM>/<NUM> Gpbs data rates.

Each PHY <NUM> links, typically via a suitable Medium Access Control (MAC) device, link partners, such as between an electronic unit and a local computer, i.e., processors <NUM>. The electronic unit includes, for example, cameras <NUM>, radar/lidar/sonar <NUM>, or other suitable sensors <NUM>, such as temperature sensors, magnetic field sensors, and the like. The PHYs <NUM> communicate over cables with local computers, i.e., processors <NUM>, typically via switches <NUM> and controllers <NUM>.

Each link 118a, 118b has at least one end connected to a switch <NUM>. Typically, an echo signal acquired at one end of a link is sufficient for diagnosing the Ethernet cable. Thus, in an embodiment, the switch <NUM> reads an echo signal, for example, from the memory <NUM> (<FIG>) of the PHY <NUM> it is locally connected to, and relays the echo signal, in the form of echo measurements representative of the return echo signals, to the processor <NUM>. A trained computer model <NUM>, such as an Artificial Intelligence (AI) model, downloaded by and running in the processor <NUM>, receives this echo measurement as input.

In an embodiment, the PHYs <NUM> receive echo signals, e.g., return echo signals, from the cables 118a, 118b, estimate corresponding return echo signals, and, via the switch <NUM>, transmit the estimated return echo signal back over the ethernet cable 118a, 118b, for example, as echo measurements. An example PHY <NUM> for receiving return echo signals, and estimating the echo cancellation signals as echo measurements, is shown for example, in <FIG>, which is detailed below.

As seen in the insert <NUM> of <FIG>, for the processor <NUM>, the processor <NUM> downloads a model <NUM>, for example, a rules based (non-AI) model or an AI model. The processor <NUM> is also associated with at least one transceiver <NUM> to receive return echo signals and/or data associated therewith, input the signals and/or data into the model <NUM>, and transmit the model-generated output as metrics to other system computers, processors, components, and the like, as discussed below.

<FIG> shows a system <NUM> external to the vehicle <NUM> and the IVN <NUM> for detecting cable faults in the ethernet cable 118a, 118b connected to a PHY <NUM> of the vehicle <NUM>. The system <NUM> includes a computer <NUM>, which has downloaded an Artificial Intelligence (AI) model <NUM> or other downloadable model, including non-AI models (such as rule-based models), to receive echo responses, e.g., echo cancellation signal(s) and/or data associated therewith, from the PHY <NUM> to determine whether the ethernet cable 118a, 118b has a fault and if there is a fault, to determine the type of cable fault.

The configuration of the IVN <NUM>, depicted in <FIG>, and the composition of network components and/or deployment of the components, such as processors <NUM>, controllers <NUM>, switches <NUM>, PHYs <NUM> and the various other system elements such as camera <NUM>, radar/lidar/sonar <NUM>, or other suitable sensors <NUM>, such as temperature sensors, magnetic field sensors, and the like, are example configurations that are depicted solely for the sake of clarity. In alternative embodiments, any other suitable configurations can be used.

The various elements of the IVN <NUM>, e.g., such as processors <NUM>, controllers <NUM>, switches <NUM>, PHYs <NUM> and other systems elements such as camera <NUM>, radar/lidar/sonar <NUM>, or other suitable sensors <NUM>, such as temperature sensors, magnetic field sensors, and the like, may be implemented using dedicated hardware or firmware, such as using hard-wired or programmable logic, e.g., in one or more Application-Specific Integrated Circuits (ASIC) and/or one or more Field-Programmable Gate Arrays (FPGA). Additionally or alternatively, some functions of the aforementioned components of the IVN, may be implemented in software and/or using a combination of hardware and software elements. Elements that are not mandatory for understanding of the disclosed techniques have been omitted from the figure for the sake of clarity.

In some embodiments, the processors <NUM> of the internal computer and/or the processors of the external computer <NUM> (which may also be one or more computers), comprise one or more programmable processors, which are programmed in software to carry out the functions described herein. The software may be downloaded to any of the processors in electronic form, over a network, for example, or it may, alternatively or additionally, be provided and/or stored on non-transitory tangible media, such as magnetic, optical, or electronic memory.

<FIG> shows an example PHY <NUM>, represented as a circuit <NUM>, in accordance with an embodiment that is described herein. PHY <NUM> of <FIG> performs echo cancellation for its internal use (for improving reception performance.

In the present example, PHY <NUM> comprises a data modulator <NUM>, which outputs an Ethernet signal comprising data symbols. A transmitter (TX) <NUM> transmits the Ethernet signal via a hybrid combiner <NUM> (the hybrid combiner for splitting and combining signals to/from different channels) to cable 118a/118b. The transmitted data is destined to a peer PHY <NUM> (not shown) at the far side of the cable.

On reception, an Ethernet signal from the peer PHY <NUM> is received from cable 118a, 118b, via hybrid combiner <NUM>, and provided to a filter <NUM>. Filter <NUM> filters the received signal, e.g., using low-pass or bandpass filtering, to remove unwanted frequency components. An Analog-to-Digital Converter (ADC) <NUM> digitizes (samples) the signal. For example, the ADC <NUM> may digitize the received signals in batches of sixteen phases with <NUM> samples per phase, for a total of <NUM> samples.

The digitized signal is the filtered by a Feed-Forward Equalizer (FFE) <NUM>. A decision circuit <NUM> (e.g., a slicer) decodes bits from the equalized signal. The decoded bits are passed through a physical coding sublayer (PCS - not shown in the figure), and then provided as output, e.g., to a Medium Access Control (MAC) device (not shown). For example, the MAC device may be in a switch <NUM>. In the present example the signal is also equalized by a Decision-Feedback Equalizer (DFE) <NUM> whose input is taken from the output of decision circuit <NUM>.

PHY <NUM> further comprises an Echo Canceller (EC) <NUM>, which cancels echoes of the transmitted Ethernet signal that may leak into, and thus interfere with, the received Ethernet signal. Typically, EC <NUM> estimates the temporal waveform (the shape in time-domain) of the echo signal based on the transmitted signal, and cancels the echo signal from the received signal. Generally speaking, EC <NUM> generates a replica of the transmitted Ethernet signal with a magnitude and phase that matches the estimated echo signal, and then subtracts this replica from the from the received Ethernet signal. The cancellation operation is illustrated by an adder <NUM> in the figure.

In an embodiment, PHY <NUM> further comprises a storage medium, e.g., a memory <NUM>. EC <NUM> stores the estimated echo signal as a vector of digital values in memory <NUM>. The memory <NUM> is such that the echo signal is accessible for readout by any suitable external processor or system (e.g., by processor <NUM> or by computer <NUM>), for use in detecting and classification of a possible fault in cable 118a, 118b. In an example embodiment, although not necessarily, the estimated echo signal comprises <NUM> digital values sampled with a sampling rate of <NUM> using <NUM> phases, yielding a resolution of <NUM> nS per sample.

As seen in the figure, FFE <NUM>, DFE <NUM> and EC <NUM> are controlled by suitable adaptive algorithms running on a suitable processor of the PHY device (not shown). In the present example, the input to these algorithms is an error signal (denoted err in the figure), which is indicative of the difference between the received signal and the respective bit decisions.

Either the local or internal computer, i.e., processor <NUM>, or the computer <NUM> (also referred to herein as the "external computer <NUM>") of the external cable fault detection system <NUM>, performs an example process of cable fault detection and identification of the detected cable fault type based on the echo signal reported by PHY <NUM>. The example process is shown at a high level in <FIG>, to which attention is now directed.

In <FIG>, the process starts at block <NUM>. At this START, in an embodiment, the PHY <NUM> or the IVN <NUM> is in diagnosis mode, ready to perform cable diagnosis. The process moves to block <NUM>, where a model, for example, a computer trained model, for detecting cable faults and identifying the type of cable fault for the detected cable fault in the cable, is obtained. The model <NUM> serves to distinguish among a plurality of predetermined fault types in cables (cable fault types), so as to identify a predetermined fault type, based on characteristics, e.g., shape, of the return echo signal (and/or data associated therewith, known as "signal data"), the predetermined fault types, for example, learned from computer analysis of a multiplicity of previous echo signals, from amongst at least two different predetermined fault types, in the cable.

For example, the model <NUM> may be a trained neural network model. This is typically achieved by the local computer <NUM> or the external computer <NUM> downloading the trained neural network model, from locations along a network, and/or the cloud. The process moves to block <NUM> where signal data, corresponding to return echo signals received in the PHY <NUM> from one or more cables 118a, 118b, is obtained from the requisite PHY <NUM>. For example, the PHY <NUM> also uses this signal data to create, for example, by estimation, echo cancellation signals, which are used internally by the PHY <NUM>.

Moving to block <NUM>, the obtained signal data is applied to the trained model. The trained model analyzes the signal data, and determines whether the corresponding cable has a fault, and if there is a detected fault, identifying the fault, including classifying, the type of the cable fault (cable fault type) among known types of cable faults. In an optional process of block <NUM>, the trained model may also determine the probability of the type of cable fault from amongst at least two predetermined fault types, or a set of at least two predetermined fault types.

For the cable being analyzed by the model, the detected fault type, the identified predetermined fault for the detected fault type, and optionally, the probability of the detected fault type from amongst at least two predetermined fault types, are metrics which are outputted by the model, at block <NUM>.

The process moves to block <NUM>, where based on the outputted metrics, and in response thereto, one or more actions are initiated, and taken, based on the identified fault type. For example, the action initiated may be one or more of: logging the fault, issuing an alert, notifying the driver or other user associated with the vehicle <NUM>, and switching to a back up cable.

The process then moves to block <NUM>, where the aforementioned signal data is optionally uploaded, in order to retrain or augment the training of the trained model, based on the detected cable fault and the identity of the detected cable fault. The process ends at block <NUM>, and may be repeated for as long as desired.

<FIG> is an architectural block diagram of a trained model <NUM>, such as neural network model (e.g., an Artificial Intelligence (AI) model or machine learning (ML) model), which is downloaded into the vehicle or local computer, i.e., typically one of the processors <NUM>, or the external computer <NUM>, in accordance with embodiments of the disclosure. The trained model <NUM> (e.g., computer trained model) receives inputs, which are return echo signal data <NUM>, for example, data associated with return echo signals from the cable 118a, 118b, the PHY <NUM> having received return echo signals over the ethernet cable 118a, 118b. The echo cancellation signals are expressed as values or data representative of signal shapes, for each ethernet cable 118b of the IVN <NUM>. The echo cancellation signals and corresponding signal data are typically generated at intervals of a predetermined time, and typically are generated when the vehicle <NUM> is started. The aforementioned examples are for signal shapes in the time domain. Alternately, the described techniques can also be adapted to detection of signal shape anomalies in the frequency domain.

In alternate embodiments, the aforementioned signal data is received responsively to periodic queries, for example, by the onboard processor <NUM> in the vehicle <NUM>, or by the external computer <NUM>, or are received, for example, periodically in an automated manner.

In an embodiment, the trained model <NUM> (e.g., computer trained model) is downloaded from remote locations over external networks, such as the Internet, and portions thereof may be stored in the cloud <NUM>. There is also an associated module for run-time recording <NUM>. The trained model <NUM> running in the processor <NUM>, or computer <NUM>, outputs a set of metrics in response to input information, i.e., signal data representative of the return echo signal for the ethernet cable 118a, 118b, received by the PHY <NUM>.

The set of metrics are, for example, indications, including detection of a cable fault <NUM>, identification of the type of cable fault of the detected cable fault <NUM>, and, the probability of the type of cable fault of the detected cable fault, from amongst two or more types of cable faults (e.g., a set of types of cable faults) <NUM>. The results of these metrics are analyzed, for example, by the processor <NUM> or external computer <NUM> to initiate actions to be taken in the vehicle based on the identified cable fault type <NUM>. The initiated actions include, for example, logging the cable fault (and type of cable fault), issuing an alert, notifying the driver of the vehicle or other party associated with the vehicle, and, switching to a backup cable.

The trained model may be a rule-based model. A rule- based model operates, for example, by analyzing main peaks of a signal. As shown in <FIG>, at box <NUM>, a positive main peak is indicative of an open fault, a negative main peak is indicative of the fault being a short, while should their not be a significant main peak, the cable is normal.

Attention is now directed to <FIG>, which is a diagram of the network architecture for an example trained model <NUM>, which is a neural network model, in accordance with an embodiment of the disclosure. The neural network model <NUM>, for example, is a seven layer neural network (e.g., a seven layer convoluted feature extractor <NUM>), with a two headed network structure (output layer with a customized loss function, which is detailed below) for fault detection 504a and fault type identification, including classification 504b. The loss function combined mean square error (MSE) for the detector 504a and cross entropy for the classifier 504b, and is expressed mathematically below.

Input, i.e., signal data, is fed into layer <NUM> of the neural network model <NUM>, and, output is received from the detector 504a and classifier 504b. The number of layers, the types of layers, including hidden layers, and/or the size thereof, depends, for example, on the number of transformations to be made and the complexity of the transformations.

In an embodiment, each layer (Conv1D to Conv7D) in the feature extractor <NUM>, is for example, a complete convolution layer <NUM>, shown, for example, by layer <NUM> (Conv1D), in <FIG>. For example, as shown in <FIG>, the size of the first layer (CONV1D_1) is <NUM> x <NUM>, the size of the second layer (CONV1D_2) is <NUM> x <NUM>, the size of the third layer (CONV1D_3) is <NUM> x <NUM>, the size of the fourth layer (CONV1D_4) is <NUM> x <NUM>, the size of the fifth layer (CONV1D_5) is <NUM> x <NUM>, the size of the sixth layer (CONV1D_6) is <NUM> x <NUM>, and the size of the seventh layer (CONV1D_7) is <NUM> x <NUM>. The neural network model <NUM> includes, for example, an activation function of nodes which are sigmoid, or which are a rectified linear unit (ReLU), to perform the desired transformations.

This particular neural network architecture is shown here solely by way of example, and the principles of the present disclosure may alternatively be implemented in other sorts of neural networks, with larger or smaller numbers of layers, and larger or smaller numbers of inputs, outputs, and nodes within each layer. The pictured neural network model <NUM> is applicable for ethernet cable fault determination and identification, including classification of the determined cable fault, and determining the probability of the type of fault for the determined fault, from amongst two or more (e.g., a set of) two or more predetermined cable fault types. Typically, the processor <NUM> or processor of the external computer <NUM>, runs multiple models of this sort, to process cable fault information with respect to multiple ethernet cables associated with communication links and other components.

The neural network model <NUM>, which is downloaded, for example, from the cloud or other centralized processor serving multiple vehicles, into the vehicle processor <NUM> or external computer <NUM>, is trained in accordance with an objective function, for the classifier 504b. As the training typically large computational resources and consumes large amounts of power, the training may be done suitably in the cloud. For discrete outputs, for example, the objective function for the classifier 504b is based on a cross-entropy (CE) expressed as: <MAT> where,.

For example, if the fault type is "open", y1~ y4 may be [<NUM>, <NUM>, <NUM>, <NUM>], corresponding to [po, ps, pn&g, po&g] for the classifier 504b.

For continuous outputs, such as the fault determination and identification, including classification of the fault, and determining the probability of the fault type, from amongst two or more predetermined cable fault types, a loss function is determined, for the ethernet cable. The loss function is based on a mean-square-error (MSE), for the detector 504a, and cross entropy (CE) for the classifier 504b. The final or customized loss function (e.g., Total loss (TL)) is expressed as a weighted sum, with weights w1 and w2: <MAT>.

Any suitable algorithm may be used to train the neural network model <NUM> based on this customized loss function. For example, the algorithm may be an Adam Optimization algorithm, and random initial weights may be used. The learning rate for the customized loss function can be controlled by adjusting the learning rate during training of the neural network model.

The trained neural network model <NUM> is trained, for example, in a remote server along a network or in the cloud. The training of the neural network model is, for example, by receiving inputs which are signal shapes, indicative of a normal cable or various cable fault types from detected cable faults, from numerous vehicles (IVNs), different from the vehicle <NUM>, which are uploaded to the neural network model <NUM>. Training the neural network model <NUM> using inputs from many vehicles is, for example, typically centralized, so that the trained model benefits from a large volume of inputs, so that the trained model can serve a given vehicle (e.g., typically an individual vehicle), such as the vehicle <NUM>.

For example, the model <NUM> is trained based on known or predetermined signal shapes, for various types of cable faults, obtained from numerous IVNs of numerous vehicles. The aforementioned predetermined echo signal shapes for a normal cable and various cable fault types are shown, for example, in <FIG>. For an ethernet cable length of approximately <NUM> meters: <NUM>)box <NUM> shows return echo signal shapes for normal (a cable without faults), an open fault, or a short fault, in the ethernet cable; <NUM>) box <NUM> shows return echo signal shapes for an open fault, or an open and ground fault, in the ethernet cable; and, <NUM>) box <NUM> shows return echo signal shapes for normal (a cable without faults), and normal and ground fault, in the ethernet cable. The model <NUM> analyzes the shape of the return echo signal, from the signal data, against the shape of the signal for each cable fault type to determine, whether there is a cable fault, and if there is a cable fault, to identify or otherwise classify the type of the cable fault, or determine the probability of the type of cable fault, for the detected cable fault, from amongst at least two different types of cable faults, based on signal shapes.

For example, the trained neural network model <NUM> is trained based on: collected signal data from components, i.e., PHYs <NUM> of the IVN <NUM>, in a set or plurality of vehicles, different from the vehicle (e.g., the given vehicle) <NUM>, to which the neural network model <NUM> is being downloaded to a local computer (e.g., processor <NUM>), or external computer <NUM>, thereof, over a period of time. The model, for example, determines whether there is a predetermined fault in the cable, and distinguishes among a plurality of predetermined fault types in cables in a vehicular network, so as to identify a predetermined fault type, for the determined predetermined fault, in the cable, based on characteristics of a return echo signal in the cable, for example, the predetermined fault type learned from computer analysis of a multiplicity of previous echo signals, from amongst at least two different predetermined fault types (or from a set of at least two different predetermined fault types), and/or optionally determining a probability for the identified predetermined fault type in the cable from predetermined from amongst at least two different predetermined fault types (or from a set of at least two different predetermined fault types). For example, the predetermined fault types in the cable include: short circuit (short), open circuit (open), open and ground, normal and ground, one wire shorted to ground, one wire shorted to power, one wire open, two wires shorted to ground, two wires shorted to power, and two wires open.

The collected signal data is uploaded, for example, periodically, to a centralized processor for training the trained neural network model <NUM>, where the trained neural network model <NUM> resides, such as along a network remote from the vehicle <NUM>, the local computer <NUM> of the vehicle <NUM>, the external computer <NUM>, or the cloud.

The neural network model <NUM> is trained to generate, based on the collected signal data, a set of metrics, for example, as shown and described for <FIG>. The set of metrics indicates a detected fault in the cable <NUM>, the type (identified type) of cable fault for the detected cable fault <NUM>, and for the detected cable fault, the probability of the fault type from amongst two or more predetermined fault types <NUM>, along with the action to be initiated in response to the detected and identified cable fault type <NUM>.

The trained neural network model is deployed, for example, as a software package (that may be downloadable, for instance when the vehicle is parked and connected to an external wired or wireless network connection), for installation on the local vehicle <NUM> computer, e.g., processors <NUM>, or external computer <NUM> (e.g., processors). The trained neural network model is typically associated with an application programming interface (API) to update the trained neural network model <NUM>.

<FIG> is a diagram of a training setup for the neural network model <NUM>. Training rate is based, for example, on a loss function, a combined mean square error and cross entropy, an optimization algorithm, such as a Root Mean Square Propagation (RMSProp). The diagram shows an adjustable learning rate for the model <NUM>, as a learning rate (LR) (y-axis), against Epochs (x-axis), a time-based value. Initially, training is performed at a rapid rate, and slows down over time.

Attention is now directed to <FIG> and <FIG> which show a flow diagram detailing a computer-implemented process in accordance with embodiments of the disclosed subject matter. The aforementioned process, which includes subprocesses, is performed, for example, automatically and/or in real time.

The process begins at a START block <NUM>, where the local computer associated with the vehicular communication network, such as the local computer <NUM> of the IVN of the vehicle <NUM>, i.e., a given vehicle, or the external computer <NUM>, is activated. The process moves to block <NUM>, where a model, e.g., a computer trained model, is obtained.

The computer trained model distinguishes among a plurality of predetermined fault types in cables in a vehicular communication network, so as to identify a predetermined fault type in the cable, based on characteristics of a return echo signal in the cable, learned from computer analysis of a multiplicity of previous echo signals, from amongst at least two different predetermined fault types (or from a set of at least two different predetermined fault types), and/or optionally determining a probability for the identified predetermined fault type in the cable from predetermined from amongst at least two different predetermined fault types (or from a set of at least two different predetermined fault types). For example, the predetermined fault types in the cable include: short circuit (short), open circuit (open), open and ground, normal and ground, open and ground, normal and ground, one wire shorted to ground, one wire shorted to power, one wire open, two wires shorted to ground, two wires shorted to power, and two wires open.

The characteristics of the return echo signal include, for example, the shape of the return echo signal. Applying the computer trained model includes, for example, analyzing the shape of the return echo signal against a predetermined shape of at least two return echo signals, each of the predetermined echo signal shapes associated with a predetermined cable fault type from amongst at least two different predetermined fault types.

Additionally, the at least one obtained return echo signal may be, for example, a plurality of return echo signals, each of the return echo signals acquired from a corresponding cable of the IVN <NUM>. Applying the computer trained model includes analyzing each return echo signal to identify a predetermined cable fault type in each cable, from amongst at least two predetermined fault types.

The computer trained model for example, may be an Artificial Intelligence (AI) model, which is pre-trained to distinguish the predetermined fault type in the cable from amongst the at least two predetermined fault types (or from a set of at least two different predetermined fault types). The AI model, for example, comprises multiple feature extraction layers, which are configured to determine that the cable has a fault and to identify the fault type, such that the detected fault is identified as one of the predetermined fault types. The AI model may, for example, include one or more of a trained neural network and/or a machine learning model.

The process moves to block <NUM> where the computer trained model is downloaded to the local computer associated with the vehicular communication network, e.g., the local computer <NUM>, or external computer <NUM>, associated with the vehicular communication network of the given vehicle. The computer trained model is then deployed by the local computer associated with the vehicular communication network, e.g., the local computer <NUM>, or external computer <NUM>, of the given vehicle, at block <NUM>. The process moves to block <NUM>, where at least one return echo signal (and/or data associated therewith) corresponding to a signal communicated over a cable in the given vehicle, is obtained.

The process moves to block <NUM>, where the at least one return echo signal (and/or return echo signal data) from the cable of the given vehicle is input, or otherwise applied, into the computer trained model. Next, at block <NUM>, output from the model is received, the output is based on the operations performed by the computer trained model, corresponding to the at least one return echo signal (and/or return echo signal data) which was inputted into the computer trained model, for the given vehicle.

The process moves to block <NUM>, where a response is made to the received output, by initiating an action in response to the identified predetermined fault type, including one or more of: logging the identified predetermined cable fault, issuing an alert, notifying the driver or other user associated with the given vehicle, and switching to a backup cable.

Moving to block <NUM>, return echo signals with characteristics of known predetermined cable fault types are provided (e.g., periodically) to update and/or add to the at least two different fault types (e.g., the set of at least two different predetermined fault types). Next, at block <NUM>, characteristics of return echo signals from one or more of the given vehicle and/or other vehicles, are provided (e.g., periodically) to the computer trained model, to further train the model.

The process moves to block <NUM>, were it ends. The process may be repeated for as long as desired.

The processors <NUM> (of the local computer in the vehicle) or the external computer <NUM>, for example, may comprise general-purpose computers, which are programmed in software, including trained neural network models, to carry out the functions described herein. The software may be downloaded to the computer in electronic form, over a network, for example, or it may, alternatively or additionally, be provided and/or stored on non-transitory tangible media, such as magnetic, optical, or electronic memory.

Claim 1:
A method for fault diagnosis in one or more cables (118a, 118b) of a vehicular communication network (<NUM>), the method comprising:
obtaining at least one return echo signal corresponding to a signal communicated over a cable (118a, 118b) in a vehicle (<NUM>);
applying, to the at least one return echo signal, a computer trained model (<NUM>) that distinguishes among a plurality of predetermined fault types in cables (118a, 118b), so as to identify a predetermined fault type, based on characteristics of the return echo signal learned from computer analysis of a multiplicity of previous echo signals, from amongst at least two different predetermined fault types, in the cable (118a, 118b), wherein the characteristics of the return signal include a shape of the return echo signal, and wherein the applying the computer trained model (<NUM>) includes analyzing the shape of the return echo signal against a predetermined shape of at least two return echo signals, each of the predetermined shapes associated with a predetermined fault type from amongst at least two different predetermined fault types; and
initiating an action in response to the identified predetermined fault type.