The present invention provides a vehicle control apparatus that controls automated driving of a vehicle, comprising: a first controller configured to perform travel control of the vehicle by controlling a first actuator; and a second controller configured to perform travel control of the vehicle by controlling a second actuator which is different from the first actuator, as alternative control to be performed in a case in which degradation of a control function is detected in the first controller, wherein in a case of starting the alternative control, the travel control of the vehicle by the first controller is gradually shifted to the travel control of the vehicle by the second controller.

Methods for management of a powertrain system in a vehicle. The methods receive data or signals from multiple sensors associated with the vehicle. Optimum thresholds for classifications of the sensor data can be changed based injecting signals into the powertrain system and receiving responsive signals. Expected priorities for the sensor signals can be altered based upon attributes of the signals and confirming actual priorities for the signals. Look-up tables for engine management can be modified based upon injecting signals into the powertrain system and measuring a utility of the responsive signals. The methods can thus dynamically alter and modify data for powertrain management, such as look-up tables, during vehicle operation under a wide range of conditions.

A system and methods thereof for monitoring proper behavior of an autonomous vehicle are provided. The method includes generating a plurality of agents, wherein each of the plurality of agents describes a physical object, wherein at least one of the plurality of agents is an agent for the DUT, generating a plurality of scenarios, wherein each scenario models a behavior of at least one of the plurality of agents, and monitoring an interaction between the plurality of agents and the DUT agent for a scenario modeling the respective agent.

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.

A method for determining information related to a lane change of a target vehicle includes obtaining information related to an environment of the target vehicle. The information related to the environment relates to a plurality of features of the environment of the target vehicle. The plurality of features are partitioned into two or more groups of features. The method further determines two or more weighting factors for the two or more groups of features. An attention mechanism is used for determining the two or more weighting factors. The method further determines the information related to the lane change of the target vehicle based on the information related to the environment of the target vehicle using a machine-learning network. A weighting of the plurality of features of the environment of the target vehicle within the machine-learning network is based on the two or more weighting factors for the two or more groups of features.

A method of generating vehicle control data includes: storing, with a storage device, relationship prescription data; operating, with an execution device, an operable portion of an internal combustion engine; acquiring, with the execution device, a detection value from a sensor that detects the state of the vehicle; calculating, with the execution device, a reward; and updating, with the execution device, the relationship prescription data using update mapping determined in advance, the update mapping using the state of the vehicle based on the detection value, an operation amount used to operate the operable portion, and the reward corresponding to the operation as arguments, and returning the relationship prescription data which have been updated such that an expected profit for the reward calculated when the operable portion is operated in accordance with the relationship prescription data increases.

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.

The invention relates to a method for converting a first neural network with a first architecture into a second neural network with a second architecture for use in a vehicle controller in order to obtain the knowledge of the first neural network and transfer same to the second neural network. In a first step of the method, a conversion (701) of at least one layer of the first neural network into at least one layer of the second neural network is carried out. In a second step, a random initialization (702) of the at least one converted layer is carried out in the architecture of the second neural network. In a third step, a training process (703) of the at least one converted layer is carried out in the second neural network. In a fourth step, a fine-tuning process (704) of the non-converted layer is carried out in the second neural network or in the entire second neural network. The conversion of the first neural network into the second neural network is carried out in multiple cycles or iterations, wherein for each cycle, the conversion (701), random initialization (702), training (703), and simultaneous fine-tuning (704) steps are carried out.

Methods for management of a powertrain system in a vehicle. The methods receive data or signals from multiple sensors associated with the vehicle. Optimum thresholds for classifications of the sensor data can be changed based injecting signals into the powertrain system and receiving responsive signals. Expected priorities for the sensor signals can be altered based upon attributes of the signals and confirming actual priorities for the signals. Look-up tables for engine management can be modified based upon injecting signals into the powertrain system and measuring a utility of the responsive signals. The methods can thus dynamically alter and modify data for powertrain management, such as look-up tables, during vehicle operation under a wide range of conditions.

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.