Technologies and techniques for automatically generating labeled steering torque data, with which an artificial intelligence (AI) unit is trained to detect hands-off conditions when the vehicle is being operated.

A domain specific language for use in constructing simulations within real environments is described. In an example, a computing device associated with a vehicle can receive, from one or more sensors associated with the vehicle, sensor data associated with an environment within which the vehicle is positioned. In an example, the vehicle can be an autonomous vehicle. The computing device associated with the vehicle can receive simulated data associated with one or more primitives that are to be instantiated as a scenario in the environment. The computing device can merge the sensor data and the simulated data to generate aggregated data and determine a trajectory along which the vehicle is to drive based at least in part on the aggregated data. The computing device can determine instructions for executing the trajectory and can assess the performance of the vehicle based on how the vehicle responds to the scenario.

A universal modeling method is provided for a motor vehicle, the universal modeling method including: providing an input signal set, the input signal set comprising those signals of respective sensors of the motor vehicle which can be relevant for the control of devices of the motor vehicle; selecting a set of modeling signals from the input signal set as a function of a system architecture of the motor vehicle; and determining an output signal set by way of a time-discrete selective state space model modeling function taking into account the set of modeling signals. In this case, the output signal set functions as a signal set for controlling corresponding actuators of the devices of the motor vehicle.

A driver assist analysis system includes a processing system and a database that stores vehicle data, vehicle operational data, vehicle accident data, and environmental data related to the configuration and operation of a plurality of vehicles with driver assist systems or features. The driver assist analysis system also includes one or more analysis engines that execute on the processing system to determine one or more driving anomalies (e.g., accidents or poor driving operation) based on the vehicle operational data, and that correlate or determine a relationship between the driving anomalies and the operation of the driver assist systems or features. The driver assist analysis system then determines an effectiveness of operation of one or more of the driver assist systems or features as a statistical measure based on the determined relationship and the driver assist analysis system notifies a user or receiver, such as a manufacturer or an insurance company of the statistical measure.

The invention relates to a method for simulation-based analysis and/or optimization of a motor vehicle, preferably having the following working steps: simulating (SIOI) a driving operation of the motor vehicle (I) on the basis of a model (M) with at least one manipulated variable for acquiring values of at least one simulated variable which is suitable for characterizing an overall vehicle behaviour, in particular a driving capability, of the motor vehicle (I), wherein the model has at least one partial model, in particular a torque model, and wherein the at least one partial model is based on a function and preferably characterizes the operation of at least one component, in particular of an internal combustion engine of the motor vehicle (I); andoutputting (S I03) the values of the at least one simulated variable.

A motor unit includes a drive motor having an output shaft with a hollow portion, and a torque sensor arranged inside the hollow portion. The output shaft is formed by a magnetic body. A vehicle can include the motor unit. The drive motor can be a traction motor that generates traction drive force of the vehicle, for example.

Systems and methods for improving fuel economy in vehicles such as Class 8 trucks are provided. In some embodiments, signals indicating states of the powertrain are collected and used to generate fuel rate optimization values. Fuel rate optimization values may indicate a difference between optimum fuel flow rates and actual fuel flow rates during a vehicle drive cycle. Recorded fuel rate optimization values may be used to compare different vehicle configurations during testing, and may also be used to evaluate vehicle performance during real-world operation.

A motion of a vehicle is controlled according to a sequential compositions of the elementary paths following a transformation of one of a first pattern, a second pattern, and a third pattern. These three patterns are predetermined and form an exhaustive set of patterns. The three patterns are represented by corresponding functions stored in a memory. The functions representing the patterns are used, in response to receiving an initial state and a target state of the vehicle, to determine parameters of the minimum-curvature path. The motion of the vehicle is controlled according to the parameters of the minimum-curvature path.

A method for generating high-value test scenarios for autonomous vehicles. The method includes identifying multiple scenarios, each associated with a location on a road network. An injury value may be determined for each of the scenarios, and the scenarios and their associated injury values may be stored in a database. A desired location for testing an autonomous vehicle may be selected. At least one of the scenarios associated with the desired location and having an associated injury value that exceeds a predetermined threshold may be identified in the database. A virtual reality test environment may then be generated for the autonomous vehicle based on the at least one scenario. A corresponding system and computer program product are also disclosed and claimed herein.

Illustrative embodiments of the disclosure provide a method and apparatus for assessing a comfort level of a driving system. A method for generating an assessment model for comfort level in a driving system comprises acquiring a first assessment from a user on a comfort level of at least one traveling operation of the driving system, and acquiring a measurement of a traveling indicator of the driving system when performing the at least one traveling operation. The method further comprises generating the assessment model for the comfort level in the driving system based on the first assessment, the measurement of the traveling indicator, and a second assessment from the user on an overall comfort level of the driving system.