Methods and systems for monitoring use and determining risks associated with operation of a vehicle having one or more autonomous operation features are provided. According to certain aspects, operating data may be recorded during operation of the vehicle. This may include information regarding the vehicle, the vehicle environment, use of the autonomous operation features, and/or control decisions made by the features. The control decisions may include actions the feature would have taken to control the vehicle, but which were not taken because a vehicle operator was controlling the relevant aspect of vehicle operation at the time. The operating data may be recorded in a log, which may then be used to determine risk levels associated with vehicle operation based upon risk levels associated with the autonomous operation features. The risk levels may further be used to adjust an insurance policy associated with the vehicle.

Computing systems for vehicle diagnostics are provided. In accordance with some aspects, a computing system may receive, from a vehicle (e.g., from a computing device installed in and/or at the vehicle), a diagnostic code generated by an on-board diagnostic (OBD) system of the vehicle. The computing system may determine an issue with the vehicle based on the diagnostic code and may determine, based on the issue, a remedial action for addressing the issue and a timeframe for performing the remedial action. The computing system may store data identifying the issue, the remedial action, and the timeframe in a record associated with the vehicle.

A system and a method for diagnosing a problem with a motor vehicle using sound. Ambient noise information for the motor vehicle is determined. Current sound information is received, and whether there is a variation in sound between the current sound information and the ambient noise information is determined. The ambient noise information is subtracted from the current sound information if the variation has been identified, to identify a sound anomaly. A sound anomaly signal is extracted and compared with predetermined anomaly signal information stored in a database. The predetermined anomaly signal information is associated with diagnostic information.

Methods and systems of enabling a transportation mode on a telematics device coupled to a vehicle are provided. One method includes detecting a first event or receiving a command for enabling a transportation mode, running a transportation mode power-saving scheme in response to receiving the first event or the command, and exiting the transportation mode power-saving scheme in response to detecting a second event.

A technique is described herein for automatically logging journeys taken by a user, and then automatically classifying the purposes of the journeys. In one implementation, the technique obtains journey data from one or more movement-sensing devices as a user travels from a starting location to an ending location in a vehicle. The technique generates a set of features based on the journey data, and then uses a machine-trainable model (such as a neural network) to make its classification based on the features. The machine-trainable model accepts at least one feature that is based on statistical information regarding at least one aspect of prior journeys that the user has taken. Overall, the technique provides a resource-efficient solution that rapidly provides personalized results to individual respective users. In some implementations, the technique performs its personalization without sharing journey data with a remote server.

There are provided a method of managing radio connectivity of a vehicle and a system thereof. The method comprises: continuously receiving by vehicle's telematic system a predictive model generated by remote system using data continuously collected from a plurality of vehicles, wherein the collected data comprise, for each given vehicle of the plurality of vehicles, data informative of its location, speed and of Radio Access Technology (RAT)-related measurements; and, responsive to a predefined event, applying, by the telematic system, a lastly received predictive model to current values of a predefined set of inputs associated with the vehicle to obtain instructions and respectively provide corrective actions related to radio connectivity of the vehicle. The corrective actions include modifying RRC measurement report(s) so as to force the cellular network to provide one of: intra-RAT handover, inter-RAT handover; excluding available connectivity with undesired RAT or band; and terminating the radio connectivity with further RAT re-selecting.

Systems and methods are disclosed with respect to using a blockchain for managing the subrogation claim process related to a vehicle accident, in particular, determining fault as part of the subrogation process. An exemplary embodiment may include receiving an electronic notification of a vehicle collision; receiving sensor data (such as telematics, image, audio, vehicle operational, or other sensor data) related to the vehicle collision; determining a percentage of fault of the vehicle collision for one or more vehicles, vehicle systems, and/or drivers based upon, at least in part, analysis of the sensor data collected; and creating a blockchain for the vehicle collision with one or more links to the sensor image data and an indication of the percentage of fault(s) determined to facilitate blockchain-based claim handling.

In one aspect, an example method includes (a) determining, via a thermal conductivity sensor of an automobile damage detection device, one or more thermal conductivities at one or more locations on an automobile; (b) transmitting, via a network interface of the automobile damage detection device, a request for anticipated thermal conductivity data from an automobile claims system, wherein the anticipated thermal conductivity data corresponds to anticipated thermal conductivities at the one or more locations on the automobile; (c) in response to transmitting the request, receiving, via the network interface from the automobile claims system, the anticipated thermal conductivity data; and (d) in response to receiving, from the automobile claims system, the anticipated thermal conductivity data, displaying, via a graphical user interface, a graphical representation of the determined one or more thermal conductivities and the anticipated thermal conductivity data.

According to one embodiment, a method of generating warning messages based on system load of an autonomous driving vehicle can relieve a safety operator of the burden of constantly monitoring the vehicle and outside driving environments. The method uses a threshold for each of a number of system load parameters to determine whether the vehicle has a heavy system load that needs the attention of the safety operator. In one example, the vehicle can use a CPU usage threshold and an end-to-end latency threshold to determine whether the vehicle has a heavy system load while travelling on a road segment. If any of the thresholds is exceeded, the vehicle can send a warning message to the safety driver. The system load thresholds may be determined from data collected from the autonomous driving vehicle when it previously travelled on the road segment.

A method includes obtaining sensor data captured by a sensor of an aircraft during a power up event. The sensor data includes multiple parameter values, each corresponding to a sample period. The method further includes determining a set of delta values, each indicating a difference between parameter values for consecutive sample periods of the sensor data. The method further includes determining a set of quantized delta values by assigning the delta values to quantization bins based on magnitudes of the delta values. The method further includes determining a normalized count of delta values for each quantization bin. The method further includes comparing the normalized counts of delta values to anomaly detection thresholds. The method further includes generating, based on the comparisons, output indicating whether the sensor data is indicative of an operational anomaly.