146results about "Automatic control systems" patented technology

This document describes trajectory planning in a three-dimensional (3D) search space with a space-time artificial potential field. An example system includes a processor that obtains an initial pose, a goal pose, and an obstacle map for an environment. The processor uses a parking trajectory algorithm to determine a trajectory by searching in a space-time artificial potential field. The trajectory includes a series of 3D waypoints, including two-dimensional (2D) positional coordinates and time coordinates, to navigate from the initial pose towards the goal pose. Operation of the host vehicle is then controlled to maneuver along the trajectory using an assisted-driving or autonomous-driving system. In this way, path searching is performed in the entire 3D search space rather than iterating between two 2D search planes. This allows motion planning for autonomous parking, especially with static and dynamic objects present, to determine an optimal trajectory in a single iteration.

The method for autonomous driving includes receiving vehicle data about an autonomous vehicle, creating, using the vehicle data, a multi-domain ontology data structure. The multi-domain ontology data structure includes information about tasks, objects, missions, events, and responses of the autonomous vehicle in a plurality of operational design domains. The method further includes generating use cases, requirements, and test cases for the autonomous vehicle based on the multi-domain ontology data structure. The method may include controlling the autonomous vehicle in accordance with the use cases generated using the multi-domain ontology data structure.

The invention relates to the technical field of driving control, in particular to a new energy automobile safe driving control method and system. The method comprises the following steps that a historical urgent danger avoiding data set and a corresponding user misoperation braking state are obtained through a new energy automobile control center, sample undersampling processing is conducted firstly, and an urgent danger avoiding balance sample is obtained; thirdly, analyzing user misoperation braking abnormity by using a balance sample, and performing disordered vector intensity calculation under a time dimension based on braking abnormity data to generate disordered vector segmentation clustering data; then, braking time sequence intervention correction sensing is carried out according to the clustering data, optimized braking intervention correction data are generated, finally, the optimized data are transmitted to a new energy automobile control terminal, and safe driving control is executed. According to the invention, the driving control technology is optimized, so that the driving control technology is more perfect.

A system for notifying emergency services of a vehicular crash may (i) receive sensor data of a vehicular crash from at least one mobile device associated with a user; (ii) generate a scenario model of the vehicular crash based upon the received sensor data; (iii) store the scenario model; and / or (iv) transmit a message to one or more emergency services based upon the scenario model. As a result, the speed and accuracy of deploying emergency services to the vehicular crash location is increased. The system may also utilize vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle features, and / or generate virtual reconstructions of the vehicle collision.

A vehicle control system including a sensor for detecting an object location, a memory for storing an environment map indicative of a driving environment coordinated with a host vehicle location, a processor configured to estimate a line and object quality, to determine an uncertainty in response to historical motion of the objects, to detect a loss of sensor data in response to the data signal, to generate a frozen scene construction in response to the line and object quality and the uncertainty, to localize a host vehicle location with respect to the frozen scene construction, to regenerate an object data and a lane data in response to the frozen science and the localized host vehicle location, and to generate a vehicle motion path in response to the object data and lane data, and a vehicle controller for controlling the host vehicle in response to the vehicle motion path.

An example method includes (a) obtaining sensor data descriptive of an environment of an autonomous vehicle; (b) obtaining a plurality of travel way markers from map data descriptive of the environment; (c) determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment; and (d) generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object.

A method for controlling automated vehicle acceleration and braking includes obtaining an image using at least one vehicle camera of a host vehicle, extracting machine learning model feature inputs based on the obtained image, detecting one or more objects in the obtained image, the one or more objects including at least one pedestrian, assigning attention weights to regions of the obtained image according to locations of the one or more objects in the obtained image, combining the attention weights with corresponding ones of the machine learning model feature inputs according to the regions of the obtained image, executing a machine learning model to generate a crossing intention prediction output associated with the at least one pedestrian, and in response to the crossing intention prediction output exceeding a crossing intention threshold, controlling automatic braking of the host vehicle according to a location of the at least one pedestrian.

This document describes trajectory planning in a three-dimensional (3D) search space with a space-time artificial potential field. An example system includes a processor that obtains an initial pose, a goal pose, and an obstacle map for an environment. The processor uses a parking trajectory algorithm to determine a trajectory by searching in a space-time artificial potential field. The trajectory includes a series of 3D waypoints, including two-dimensional (2D) positional coordinates and time coordinates, to navigate from the initial pose towards the goal pose. Operation of the host vehicle is then controlled to maneuver along the trajectory using an assisted-driving or autonomous-driving system. In this way, path searching is performed in the entire 3D search space rather than iterating between two 2D search planes. This allows motion planning for autonomous parking, especially with static and dynamic objects present, to determine an optimal trajectory in a single iteration.

A system for detecting a vehicular collision (i) receives occupant data from at least one internal sensor; (ii) receives external data from at least one external sensor; (iii) determines positional information for at least one occupant of a vehicle; and (iv) generates a virtual reconstruction of the vehicle collision. The virtual reconstruction indicates a severity of vehicle damage and a severity of injury to the at least one occupant caused by the vehicle collision. The system may also utilized vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle systems, and / or perform at least one action to reduce a severity of a potential injury.

This document describes trajectory search in a three-dimensional (3D) search space with space-time artificial potential fields. An example system includes a processor that obtains an initial pose, a target pose, and an obstacle map of an environment. The processor uses a parking trajectory algorithm to determine a trajectory by searching in a space-time artificial potential field. The trajectory includes a series of 3D waypoints including two-dimensional (2D) position coordinates and time coordinates to navigate from an initial pose toward a target pose. Operations of the primary vehicle are then controlled to maneuver along the trajectory using either an aided driving system or an autonomous driving system. In this manner, the path search is performed throughout the 3D search space without iterating between two 2D search planes. This allows motion planning for autonomous parking to determine an optimal trajectory in a single iteration, in particular in the presence of stationary and dynamic objects.

The embodiment of the invention provides a vehicle control method which comprises the steps that first road condition data and second road condition data of a preset road are obtained, the first road condition data are from a platform data source, and the second road condition data are from a vehicle-mounted data source; performing risk classification processing based on the first road condition data to obtain a target risk type of the preset road; determining a target risk level corresponding to the target risk type based on the second road condition data; based on the target risk type and the corresponding target risk level, determining a target driving strategy when driving on the preset road; and performing driving control on the vehicle based on the target driving strategy. Due to the fact that the first road condition data of the platform data source and the second road condition data of the vehicle-mounted data source are integrated, the road condition of an emergency can be obtained in advance, the corresponding driving strategy is determined, prospective vehicle control is achieved, and the response speed during vehicle control can be increased.

The present application provides an autonomous driving method, an auxiliary controller, a main controller, an autonomous driving system, an electronic device and a computer-readable storage medium, and relates to the technical field of autonomous driving. The method is applied to an auxiliary controller and comprises: acquiring state monitoring information, the state monitoring information being information obtained by monitoring an operation state of a main controller; and in response to determining, according to the state monitoring information, that the main controller is operating abnormally, controlling an operation state of an autonomous vehicle.

A method and system for estimating lane changing intention of a target vehicle is provided, and a method for estimating lane changing intention of a target vehicle according to an embodiment of the present disclosure comprises: detecting the target vehicle and at least one peripheral vehicle of the target vehicle using at least one sensor installed at a host vehicle; accumulating driving risk data of the target vehicle with respect to at least one peripheral vehicle while the target vehicle is driving straight; determining whether the driving risk data has been accumulated for a predetermined time; if the driving risk data has been accumulated for the predetermined time, generating a risk adaptability model based on the accumulated driving risk data; estimating the lane changing intention of the target vehicle based on the generated risk adaptability model; and controlling the host vehicle based on the lane changing intention of the target vehicle.

Aspects of the present disclosure relate switching between autonomous and manual driving modes. In order to do so, the vehicle's computer may conduct a series of environmental, system, and driver checks to identify certain conditions. The computer may correct some of these conditions and also provide a driver with a checklist of tasks for completion. Once the tasks have been completed and the conditions are changed, the computer may allow the driver to switch from the manual to the autonomous driving mode. The computer may also make a determination, under certain conditions, that it would be detrimental to the driver's safety or comfort to make a switch from the autonomous driving mode to the manual driving mode.

The invention relates to a driving data dynamic adaptive method and device, a vehicle and a storage medium. Comprising the steps of preprocessing actual driving data of a current vehicle, and determining current driving condition characteristics of the current vehicle based on the preprocessed actual driving data and GIS map information; determining at least one group of to-be-switched driving calibration data from a preset scene driving calibration database based on the basic hardware configuration data of the current vehicle and the current driving condition characteristics, and matching the preprocessed actual driving data with the at least one group of to-be-switched driving calibration data and the current driving calibration data of the current vehicle, and determining target driving data according to a matching result, so that the current vehicle drives based on the target driving data. Therefore, more adaptive vehicle driving calibration data is provided for the user in real time based on the actual driving scene, and the problem that the performance of existing driving calibration data in actual use cannot achieve the optimal cost performance in the whole scene is solved.

A method and system for modeling trust levels of drivers and modifying autonomous systems in real time in response to predicted trust levels. These modifications may be made by one or more systems, including an autonomous driving agent. An end-to-end attention network known as a Selective Windowing Attention Network (SWAN) learns directly from time-series data and assigns attention to critical areas.

The invention relates to a method for providing a plurality of driving modes (12) for at least one motor vehicle (14) by a central electronic computing device (10), comprising the steps of receiving a plurality of driving behaviors (16) of a plurality of motor vehicles (18) by the electronic computing device (10); (S1) extracting similarities in the plurality of driving behaviors (16) by the electronic computing device (10); (S2) clustering the plurality of driving behaviors (16) depending on the similarities by the electronic computing device (10); (S3) generating a distinct plurality of driving modes (12) depending on the clustering by the electronic computing device (10); (S4) and providing the plurality of driving modes (12) for the at least one motor vehicle (14). Furthermore, the invention relates to a computer program product, a non-transitory computer-readable storage medium, as well as to a central electronic computing device (10).

The disclosed technology teaches a system and method for providing driver assistance alerts to a driver using an end-to-end artificial intelligence advanced driver assistance system. The disclosed techniques further include receiving a series of environmental data of a driving state including at least video from a camera, return data from an optical sensor, and location data from a GNSS receiver, where the camera, the optical sensor, and the GNSS receiver are coupled to a processor carried by a vehicle; processing the environmental data as input to an end-to-end neural network, wherein the end-to-end neural network is trained to generate specified steering and speed control actions in response to a current driving state; analyzing hidden layer data and output data from the end-to-end neural network to estimate collision avoidance data; and presenting to the driver a user interface including a driver assistance alert based on the collision avoidance data.

A computer-implemented method for verifying a software-based behavior planner of an automated driving function. The method includes: providing a verification environment model to limit the state space of the behavior planner according to a specifiable traffic scene; providing a formal requirement as a criterion for the correctness of decisions of the behavior planner; generating a model checker representation of the behavior planner taking into account the provided verification environment model; analyzing the model checker representation using a model checking procedure with respect to the formal requirement. The verification environment model is used to determine a physically meaningful parameter interval for at least one location parameter and / or movement parameter of the participants of the given traffic scene. The model checking procedure systematically samples the parameter interval and thus checks the behavior planner for a representative selection of the possible temporal and spatial developments of the given traffic scene.

The present application provides a vehicle control method, a vehicle, and a storage medium, relating to the technical field of vehicle control. The method includes: receiving sensing data transmitted by an external vehicle sensing device to obtain first sensing data; screening the first sensing data to obtain second sensing data; and controlling the vehicle according to the second sensing data and third sensing data, where the third sensing data is obtained by a data acquisition module scanning the surrounding environment of the vehicle. The present application not only expands the sensing range of the vehicle but also improves the driving safety of the vehicle.

A method of coordinating road users via a server device which determines instruction data by a central model and transmits the instruction data as action instruction to respective control circuits of the road user, the central model being a virtual image of a real environment created by the server device from environment data, includes when a road user reaches the real environment a local model is created by onboard sensor data and delta data relating to the central model is determined. The delta data is transmitted to the server device by the control circuit of the road user which creates an updated central model by the delta data and the central model. The server device determines and transmits updated instruction data to the road user as readjustment of the instruction data.

An approach for operating a motor vehicle that is designed for an autonomous and / or partially autonomous driving operation in which the motor vehicle is moved along a predetermined driving trajectory. During the autonomous or partially autonomous driving operation, a driving trajectory running centrally or substantially centrally along the lane traveled is shifted to the edge of the lane on the basis of lane information describing the lane currently traveled by the motor vehicle, and on the basis of speed information describing a current speed of the motor vehicle, and on the basis of at least one piece of traffic information describing a current traffic volume in the surroundings of the motor vehicle. Alternatively, or in addition, the motor vehicle is stopped before a bottleneck or an intersection is entered on the basis of road-course information describing the bottleneck or the intersection along the road course to be traveled, and additionally on the basis of emergency vehicle information describing the presence of an emergency vehicle in the surroundings of the motor vehicle, and / or on the basis of accident information describing an accident along the road course to be traveled.

The present invention provides a vehicle driving assistance device that can prevent the vehicle's speed from temporarily falling below the legal minimum speed, even when performing driving assistance control that allows the vehicle to coast. [Solution] The vehicle driving support device 10 performs a first driving support control to drive the vehicle 100 by autonomously alternating between accelerating and coasting the vehicle so that the vehicle's speed is maintained within a range between a target upper speed and a target lower speed. The vehicle driving support device sets the target upper speed by adding a predetermined upper limit to the set vehicle speed set by the vehicle's user as the target value for the vehicle's speed, and sets the target lower speed by subtracting a predetermined lower limit from the set vehicle speed. When the first driving support control is executed, if the target lower speed is lower than the legally mandated minimum speed applicable to the vehicle, the predetermined lower limit is changed so that the target lower speed is equal to or greater than the legally mandated minimum speed.

A vehicle control method includes obtaining sensor information of different modalities of different onboard devices of a vehicle, performing modality transformation on the sensor information to generate short-cycle message information of a unified modality, sending the short-cycle message information to a first data model at a remote end, generating long-cycle message information by a second data model configured at the vehicle based on the sensor information with the different modalities, sending the long-cycle message information to the first data model at the remote end, receiving the vehicle control information returned by the first data model at the remote end, and controlling the vehicle based on the vehicle control information. The short-cycle message information has better real-time performance than the long-cycle message information, and the long-cycle message information is used to trigger the first data model to generate vehicle control information.

A method and system for estimating a lane change intention of a target vehicle are provided, and a method for estimating a lane change intention of a target vehicle according to an embodiment of the present disclosure includes: detecting the target vehicle and at least one peripheral vehicle of the target vehicle using at least one sensor installed on a host vehicle; collecting driving risk data of the target vehicle with respect to at least one peripheral vehicle while the target vehicle is traveling straight; determining whether the driving risk data has been collected for a predetermined time; when the driving risk data has been collected for the predetermined time, generating a risk adaptability model based on the collected driving risk data; estimating the lane change intention of the target vehicle based on the generated risk adaptability model; and controlling the host vehicle based on the lane change intention of the target vehicle.

An example method includes (a) obtaining sensor data descriptive of an environment of an autonomous vehicle; (b) obtaining a plurality of travel way markers from map data descriptive of the environment; (c) determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment; and (d) generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object.

This control device comprises: a recognition unit that recognizes the environment surrounding a mobile body; a diagnostic unit that, on the basis of the recognition results according to the recognition unit, diagnoses the strength of electromagnetic waves projected to the mobile body; and a route planning unit that determines a movement route for the mobile body, on the basis of the diagnosis results according to the diagnostic unit.

An autonomous driving method, an auxiliary controller, a main controller, an autonomous driving system, an electronic device, and a computer-readable storage medium, which relate to the field of autonomous driving technology. The method is used in an auxiliary controller, and includes a step of acquiring state monitoring information that is information obtained by monitoring the operating state of the main controller, and a step of controlling the operating state of an autonomous vehicle when an operating abnormality of the main controller is determined based on the state monitoring information.