The disclosure relates to a method for developing a vehicle function to be executed by means of hardware of a motor vehicle. The method may comprise: identifying a vehicle function to be developed; creating a design for the vehicle function; carrying out a first permissibility check on the design for the vehicle function; approving the design for the vehicle function if first permissibility criteria are met; transmitting the design for the vehicle function; carrying out a second permissibility check on the design for the vehicle function; approving the design for the vehicle function if second permissibility criteria are met; transmitting the design for the vehicle function to the motor vehicle; and implementing the vehicle function in the motor vehicle.

A control method is provided for controlling a lateral position of a motor vehicle. The control method includes calculating a sighting distance of a detector means embedded in the vehicle, calculating a first component of a steering angle setpoint of a steered wheels of the vehicle, and calculating a second component of the steering angle setpoint. The first component is an open loop component of a control system, while the second component is a closed loop component of the control system. The first component is weighted by a gain that is a decreasing function of the sighting distance.

A driver assist design 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 design 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 statistical relationship between the driving anomalies and the operation of the driver assist systems or features. The driver assist design analysis system then determines an effectiveness of operation of one or more of the driver assist systems or features based on the statistical relationship to determine a potential design flaw in the driver assist systems or features, and the driver assist design analysis system notifies a user or receiver of the potential design flaw.

The present invention relates to a method and apparatus that utilize production vehicles to develop new path planning features for Automated Driving Systems (ADSs) by using federated learning. To achieve this the “under-test” path planning module's output is evaluated in open-loop in order to produce a cost-function that is subsequently used to update or train a path planning model of the path planning module.

Producing a motion planning policy for an Autonomous Driving Machine (ADM) may include producing a search tree, including a root node representing a current condition of the ADM and derivative nodes linked thereto, representing predicted conditions of the ADM, following application of an action on the ADM. The nodes may be interlinked by actions and associated quality factors. A neural network (NN) may select a plurality of quality factors. The search tree may be expended to add interlinked derivative nodes according to the NN's selection, until a terminating condition is met. Backward propagating and updating one or more quality factors along trajectories of the expanded tree may occur. The NN may be trained according to the current condition of the ADM and the updated quality factors to select an optimal action. The selected optimal action may be applied on at least one physical element of the ADM.

A computer-implemented method for training a machine learning system to generate driving profiles of a vehicle. The method includes first travel routes are selected from a first database having travel routes, a generator of the machine learning system receives the first travel routes and generates first driving profiles for each of the first travel routes, travel routes and associated driving profiles determined during vehicle operation are stored in a second database, second travel routes and respective associated second driving profiles determined during vehicle operation are selected from the second database, a discriminator of the machine learning system receives pairs made up of one of the first travel routes with the respective associated first generated driving profile and pairs made up of second travel routes with the respective associated second driving profile determined during vehicle operation, as input variables.

The present disclosure relates to driving decision-making methods, apparatuses, and chips. One example method includes building a Monte Carlo tree based on a current driving environment state, where the Monte Carlo tree includes a root node and N-1 non-root nodes, each node represents one driving environment state, and a driving environment state represented by any non-root node is predicted by a stochastic model of driving environments. Based on at least one of an access count or a value function of each node in the Monte Carlo tree, a node sequence that starts from the root node and ends at a leaf node is determined, and a driving action sequence is determined based on a driving action corresponding to each node in the node sequence.

The present invention relates to a universal powertrain for controlling an effort request and/or a flow request to a powertrain based on a demanded effort or demanded flow for the powertrain. The universal controller includes a configurable powertrain model and a configurable optimiser module. The universal controller is configurable to control a class of generic powertrains comprising J generic power sources, K generic power sinks, and L generic couplings. The universal controller is arranged to receive an input file of a plurality of input parameters to configure the universal controller to control a specific powertrain having a powertrain architecture with N power sources, M power sinks, and X couplings, the configurable powertrain model comprising: (a) a generic powertrain component library configured to provide a model of each of the N power sources, M power sinks and X couplings of the specific powertrain, and (b) a connection parameter module configured to define a model architecture of the N power source models, M power sink models and X coupling models which is representative of the powertrain architecture based on flow weight parameters and effort weight parameters of the input file, the configurable optimiser module comprising: a generic performance objective function library comprising a plurality of configurable performance objective functions from which a cost function is configurable based on input parameters of the input file, wherein the configurable optimiser module is configurable to calculate at least one of an optimised effort request or an optimised flow request for each of the N power sources of the specific powertrain based on: the cost function, the powertrain model of the specific powertrain, the demanded effort request of demanded flow request.

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); and—outputting (S I03) the values of the at least one simulated variable.

A computer-implemented method for training a machine learning system for generating driving profiles and/or driving routes of a vehicle including: a generator obtains first random vectors and generates first driving routes and associated first driving profiles related to the first random vectors, driving routes and respectively associated driving profiles recorded in driving mode are stored in a data base, second driving routes and respectively associated second driving profiles recorded in driving mode are selected from the database, a discriminator obtains first pairs made up of first generated driving routes and respectively associated first generated driving profiles and second pairs made up of second driving routes and respectively associated second driving profiles recorded in driving mode, the discriminator calculates outputs that characterize each pair, and a target function is optimized as a function of the outputs of the discriminator.