A method and an apparatus for automated driving, a device, and a computer-readable storage medium are provided. The method for automated driving includes: obtaining data associated with a parameter and an external environment of a vehicle, the data being collected according to a collection policy for a target scenario for the vehicle; generating an automated driving model for the target scenario based on the obtained data; and updating the collection policy by testing the automated driving model.

A method for identifying scenarios of interest for development, verification and/or validation of an ADS of vehicle. Obtaining risk map of surrounding environment of vehicle, risk map is formed based on actuation capability of vehicle and location of free-space areas in surrounding environment. The actuation capability comprises uncertainty estimation for actuation capability and location of free-space areas comprises uncertainty estimation for estimated location of free-space areas. Risk map includes risk parameter for each of a plurality of area segments comprised in surrounding environment of vehicle. Determining compounded risk value of ADS based on risk parameters of a set of area segments of risk map. Monitoring scenario trigger by monitoring at least one of determined compounded risk value against compounded risk trigger threshold, a development of risk map over time against a map volatility trigger threshold, and a development of compounded risk value over time against a risk volatility threshold.

In various examples, sensor data used to train an MLM and/or used by the MLM during deployment, may be captured by sensors having different perspectives (e.g., fields of view). The sensor data may be transformed—to generate transformed sensor data—such as by altering or removing lens distortions, shifting, and/or rotating images corresponding to the sensor data to a field of view of a different physical or virtual sensor. As such, the MLM may be trained and/or deployed using sensor data captured from a same or similar field of view. As a result, the MLM may be trained and/or deployed—across any number of different vehicles with cameras and/or other sensors having different perspectives—using sensor data that is of the same perspective as the reference or ideal sensor.

Systems and methods are disclosed for optimizing values of a set of tunable parameters of an autonomous driving vehicle (ADV). The controllers can be a linear quadratic regular, a “bicycle model,” a model-reference adaptive controller (MRAC) that reduces actuation latency in control subsystems such as steering, braking, and throttle, or other controller (“controllers”). An optimizer selects a set tunable parameters for the controllers. A task distribution system pairs each set of parameters with each of a plurality of simulated driving scenarios, and dispatches a task to the simulator to perform the simulation with the set of parameters. Each simulation is scored. A weighted score is generated from the simulation. The optimizer uses the weighted score as a target objective for a next iteration of the optimizer, for a fixed number of iterations. A physical real-world ADV is navigated using the optimized set of parameters for the controllers in the ADV.

Enclosed are embodiments for sampling driving scenarios for training machine learning models. In an embodiment, a method comprises: assigning, using at least one processor, a set of initial physical states to a set of objects in a map for a set of simulated driving scenarios, wherein the set of initial physical states are assigned according to one or more outputs of a random number generator; generating, using the at least one processor, the set of simulated driving scenarios in the map using the initial physical states of the objects in the set of objects; selecting, using the at least one processor, samples of the simulated driving scenarios; training, using the at least one processor, a machine learning model using the selected samples; and operating, using a control circuit, a vehicle in an environment using the trained machine learning model.

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.

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.

The present invention provides a technology for comprehensive verification of the safety of the design of functions, on the basis of a safety analysis result. The disclosed vehicle control system verification device is equipped with a storage device that stores programs for verifying the safety of the logical architecture of a vehicle control system, and a processor that reads the programs from the storage device and verifies the safety of the logical architecture. On the basis of safety analysis result information that is supplied, the processor executes a process for verifying whether the logical architecture has logical functions corresponding to the safety analysis result.

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.

A method for operating a motor vehicle incorporates polling regarding the control of the motor vehicle, leading to an improvement in the working conditions of a plurality of evaluation units accessing sensor units of the motor vehicle. Control commands for controlling the motor vehicle are determined from this polling by a conflict checking unit. The conflict checking unit determines the feasibility of the control commands, taking into consideration predetermined verification criteria with regard to conflicts between the individual control commands and the practicability of the individual control commands. The conflict checking unit also determines a control specification for a vehicle control unit based on the feasibilities and certain decision criteria. Finally, the motor vehicle is controlled by use of the vehicle control unit in accordance with the control specification.