Systems and methods for determining an estimated weight of a vehicle are provided. The system includes at least one data storage and at least one processor. The at least one data storage is configured to store vehicle data associated with the vehicle. The at least one processor is configured to: identify a plurality of vehicle maneuvers based on the vehicle data, each vehicle maneuver being associated with a portion of the vehicle data, each portion of the vehicle data comprising a measured torque profile; generate a plurality of simulated torque profiles for each vehicle maneuver; generate a plurality of error profiles, an error profile being generated for each vehicle maneuver based on differences between the plurality of simulated torque profiles and the measured torque profile corresponding to that vehicle maneuver; and determine the estimated weight of the vehicle based on the plurality of error profiles.

Disclosed embodiments relate to performing updates to Electronic Control Unit (ECU) software while an ECU of a vehicle is operating. Operations may include receiving, at the vehicle while the ECU of the vehicle is operating, a software update file for the ECU software; writing, while the ECU is operating, the software update file into a first memory location in a memory of the ECU while simultaneously executing a code segment of existing code in a second memory location in the memory of the ECU; and updating a plurality of memory addresses associated with the memory of the ECU based on the software update file and without interrupting the execution of the code segment currently being executed in the second memory location in the memory of the ECU.

A method to determine, in real time, a use-life of a steering system includes tracking an attribute signal associated with the steering system. The method further includes, based on a determination that the attribute signal rises above an upper threshold and subsequently falls below a lower threshold, selecting a subset of categories based on a frequency content of the attribute signal. The method further includes selecting a category from the subset of categories based on a peak load occurring in sequence to the attribute signal via a secondary attribute signal. The method further includes incrementing a counter for the selected category. The method further includes computing the use-life based on a ratio of the counter for the selected category and a predetermined count for said selected category.

A method for adapting a driving behavior of a vehicle or of a vehicle combination based on vehicle parameters, in particular by a control unit. In the method, static vehicle parameters are received, preferably prior to starting the drive, measuring data being collected and received during at least one initiated driving maneuver, the measuring data received during the at least one initiated driving maneuver being evaluated for ascertaining at least one dynamic vehicle parameter, and the static vehicle parameters and/or ascertained dynamic vehicle parameters being transmitted to a vehicle control unit for the vehicle-specific adaptation of a driving behavior. A control unit, a computer program, and a machine-readable memory medium are also described.

A method for monitoring a vehicle system configured to detect an environment of a vehicle, wherein the vehicle system has a sensor system with at least two sensor units configured to capture the environment of the vehicle, and an evaluation unit configured to detect objects in the environment of the vehicle by merging sensor data of the at least two sensor units. The method includes, for each of the detected objects, using sensor data to determine separately for each of the at least two sensor units a probability of existence indicating a probability of the detected object representing a real object, and a probability of detection indicating a probability with which the detected object can be captured by the sensors. The method includes determining whether the vehicle system is in a robust state based on the probability of existence and the probability of detection.

A system and method of augmenting a delivery route. Exemplary methods include first location information from a first position device and receiving second location information from a second position device, storing first and second location information, and determining, based on at least one of the first and second information, a time and a location for each of a plurality of stops. Exemplary methods also include reconciling determined time and locations of each of the plurality of stops with a stored route, the stops having a stored location and a stored time associated therewith and estimating a plurality of transition times between pairs of the plurality of stops. Exemplary methods include constructing an updated timeline based on the plurality of stored stops, the plurality of determined stops, and the plurality of transition times and updating the delivery route based on the constructed timeline.

A system for a vehicle includes a first input device and a second input device that provides a plurality of selectable input options. The plurality of selectable input options comprises a plurality of control input options for controlling a plurality of vehicle functions and an association input option for associating the first input device with at least one vehicle function suggested from among the plurality of vehicle functions. The at least one vehicle function is suggested based on historical use of the control input option that controls the at least one vehicle function and an accessibility score of the control input option.

Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, the operating status of the features, the identity of a vehicle operator, risk levels for operation of the vehicle by the vehicle operator, or damage to the vehicle may be determined based upon sensor or other data. According to further aspects, decisions regarding transferring control between the features and the vehicle operator may be made based upon sensor data and information regarding the vehicle operator. Additional aspects may recommend or install updates to the autonomous operation features based upon determined risk levels. Some aspects may include monitoring transportation infrastructure and communicating information about the infrastructure to vehicles.

A vehicle learning system includes a first execution device mounted on a vehicle, a second execution device outside the vehicle, and a storage device. The storage device stores mapping data including data, which is learned by machine learning and defines mapping that receives input data based on a detection value of an in-vehicle sensor and outputs an output value. The first execution device and the second execution device execute, in cooperation with each other, an acquisition process of acquiring input data, a calculation process of calculating an output value with the input data as an input of the mapping, and a relationship evaluation process of evaluating a relationship between a predetermined variable different from a variable corresponding to the output value and accuracy of the output value. The first execution device executes at least the acquisition process, and the second execution device executes at least the relationship evaluation process.

An autonomous vehicle, a control system for remotely controlling the same, and a method thereof provides a control system including a processor configured to search for an external person around an autonomous vehicle when receiving a request for help of the external person from the autonomous vehicle, and to request the help of the external person through a user terminal of the searched external person or an external notification of the autonomous vehicle.