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.

A computer-based system for testing a server-based vehicle function, which is designed to implement a method comprising the following steps: a function model of the vehicle function is simulated by a first simulator on a server, an at least partial vehicle model is simulated by a second simulator and the vehicle function is tested, while a data connection between the first simulator and the second simulator is systematically influenced.

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.

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 controller system and method may be used for controlling an autonomous vehicle (AV). The controller may be configured to self-develop, self-tune, or both, based on a design of experiments (DOE) test matrix. The methods and systems disclosed herein may be used online, offline, or a combination thereof. The controller and method may use one or more optimization algorithms to self-develop, self-tune, or both. The one or more optimization algorithms may be based on machine learning, artificial intelligence, or a combination thereof.

A system for controlling a motion of a vehicle from an initial state to a target state includes a path planner to determine a discontinuous curvature path connecting the initial state with the target state by a sequential composition of driving patterns. The discontinuous curvature path is collision-free within a tolerance envelope centered on the discontinuous curvature path. The system further includes a path transformer to locate and replace at least one treatable primitive in the discontinuous curvature path with a corresponding continuous curvature segment to form a modified path remaining within the tolerance envelope. Each treatable primitive is a predetermined pattern of elementary paths. The system further includes a controller to control the motion of the vehicle according to the modified path.

The invention relates to a system comprising a plurality of first sensors to measure parameters characterizing a vehicle operating state; a second sensor to measure a parameter characterizing an emission of an fuel processing apparatus; a control device to measure repeatedly over a predefined time period and to determine a vehicle operating state based on a first data set with measured values from the plurality of first sensors and predefined parameter ranges describing a predefined vehicle operating state; an allocation device to allocate a second data set comprising measured values from the second sensor to the predefined vehicle operating state; and an evaluation device to determine a characteristic value for assessing or optimizing the operating behavior of the vehicle based on the vehicle operating state and the second data set, wherein the characteristic value characterizes an energy efficiency of the vehicle or an emission behavior of the fuel processing apparatus.

A device for operating an apparatus comprising a first controller configured to be controlled by a first control signal, a second controller configured to be controlled by a second control signal, a control unit operatively connected to the first controller and the second controller, wherein the first controller and the second controller are both configured to operate the apparatus.

Systems, methods and controllers are provided for controlling autonomous vehicles. The systems, methods and controllers implement autonomous driver agents and a policy server for serving policies to autonomous driver agents for controlling an autonomous vehicle. The system can include a set of autonomous driver agents, an experience memory that stores experiences captured by the driver agents, a set of driving policy learner modules for generating and improving policies based on the collective experiences stored in the experience memory, and a policy server that serves parameters for policies to the driver agents. The driver agents can collect driving experiences to create a knowledge base that is stored in an experience memory. The driving policy learner modules can process the collective driving experiences to extract driving policies. The driver agents can be trained via the driving policy learner modules in a parallel and distributed manner to find novel and efficient driving policies and behaviors faster and more efficiently.

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.