Surface penetrating radar interrogates a region adjacent a pathway of the vehicle in response to activation by a user. An object detection system which is responsive to the radar transceiver is configured to recognize one or more spatial signatures of one or more detected objects in the region. A controller coupled to the radar transceiver and the object detection system is configured to (i) compare a respective spatial signature of at least one of the detected objects to a plurality of predetermined target signatures to detect an infrastructure asset, (ii) assess a perimeter around the detected infrastructure asset to estimate a severity of an obstruction blocking the infrastructure asset, and (iii) convey an alert message to the user when the estimated severity is greater than a threshold.

A control system and method for controlling a vehicle subsystem are provided. The control system includes a remote parameter sensor configured to generate a remote parameter signal indicative of a value of a universal parameter associated with an environment in which a vehicle is operating. The system further includes a local parameter sensor configured to generate a local parameter signal indicative of the value of the universal parameter and a local controller. The controller is configured to receive the local parameter signal along a first signal path, receive the remote parameter signal and a command signal configured for controlling a function of the vehicle subsystem along a second signal path, compare the local and remote parameter signals and implement the function of the vehicle subsystem responsive to the command signal if the remote parameter signal meets a predetermined condition relative to the local parameter signal.

Disclosed is a vehicle fuel economy evaluation method based on data analysis. The method combines data processing and a fuel model with an enhanced learning mechanism to predict fuel consumption, analyze driving behavior and output an improvement suggestion. With continuous enhanced learning and long-term dynamic improvement, the model will be able to predict economic fuel consumption in an increasingly accurate way, along with specific and intuitive driving behavior suggestions to help drivers to drive economically.

Vehicle environment data about operation of a plurality of vehicles and vehicle component data for a vehicle component is input into a machine learning program to obtain a transfer function that correlates vehicle environment data to vehicle component data within a specified range of vehicle component parameters. A first event is identified by determining that a datum in the vehicle component data is outside the specified range. A predictive damage model of a vehicle component is updated based on the first event. A virtual parameter of a component model is adjusted based on output from the predictive damage model.

The present technology is directed to customizing a driving behavior of an autonomous vehicle (AV) and verifying safety of the driving behavior based on a simulation. An AV management system can determine driving parameter(s) relating to a driving behavior of an AV and determine a degree of similarity between the driving parameters and a plurality of stored driving parameters. Based on a determination that the degree of the similarity is outside of a predetermined range, the AV management system can generate a scenario to test a safety of the driving parameters, perform a simulation of the AV in the scenario, and compare a simulation result with a predetermined safety threshold. Based on a determination that the simulation result is equal to or above the predetermined safety threshold, the AV management system can adjust the driving behavior of the AV based on the driving parameters.

In a method for operating a vehicle which has a communication interface, a position-determining unit and at least one road data set-determining unit, a database road data set is received by the communication interface, which data set is presumably made available by the database which is arranged externally with respect to the vehicle and which is representative of the position-dependent, road-related property. Depending on the database road data set and a vehicle road data set which is assigned thereto in terms of position, a trust characteristic value is determined which is representative of the level of trust in further database road datasets which relate to predefinable positions of the vehicle.

Data privacy for data associated with traveling in a vehicle that has access to passenger data, vehicle location data, vehicle navigation data, peripheral data; and/or itinerary data. Privacy is achieved by data classification and corresponding data security preferences and/or user selection including end of trip data disposition actions.

A method for classifying an underlying surface travelled by an agricultural utility vehicle includes acquiring a detail of a surface of the underlying surface in the form of optical data, classifying the optical data in a data processing unit with respect to different underlying surface classes, and determining an underlying surface class on the basis of the classifying step. Output data is output from the data processing unit representative of the determined underlying surface class as a classification result. A technical feature of the utility vehicle is adapted as a function of the classification result.

Embodiments of the present invention provide an electric machine control system for a vehicle, the electric machine control system comprising one or more controllers, wherein the vehicle comprises an electric machine arranged to be selectively coupleable to provide torque to at least one wheel of an axle of the vehicle, the control system comprising input means arranged to receive a speed signal (410) indicative of a speed of the vehicle and a status signal (470) indicative of a status of a coupling of the electric machine to the at least one wheel of an axle of the vehicle, output means (340) arranged to output a coupling signal to control coupling of the electric machine to the at least one wheel of the axle, and processing means arranged to determine (1210) a coupling state of the electric machine to the at least one wheel of the axle and to control the output means to output (1220) a coupling signal indicative of the determined coupling state, wherein the processing means is arranged, in dependence on the status signal being indicative of a failure (1230) to change the coupling state of the electric machine to the at least one wheel of the axle in dependence on a change in the determined coupling state, to control the output means to output the coupling signal (345) indicative of a retry (1250) of the change in the coupling state in dependence on the speed signal.

Described herein are various techniques, including a method that uses high-rate acceleration data for computing an accident score indicative of a potential collision and triggering an action in response to determining that the accident score indicates a potential collision. The method includes filtering out undesired high-rate acceleration trigger events such as noise and harsh braking events prior to determining the accident score. The accident score is based on contexts or scores computed from high-rate acceleration data, speed, and GPS data captured by a telematics monitor deployed in a vehicle.