76results about "Automatic control systems" patented technology

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 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.

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.

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 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.

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.

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.

The technology disclosed teaches a system and methods for providing driver assistance alerts to a driver using an end-to-end artificially-intelligent advanced driver assistance system. The technology disclosed further includes receiving environmental data for a sequence of driving states including at least video from a camera, returns from an optical sensor, and location data from a GNSS receiver, wherein 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 prescriptive steering and speed control actions in response to a present 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 driver assistance alerts based on the collision avoidance data.

The application provides a driving data collection and analysis method, system, readable storage medium and vehicle. The method comprises the following steps: obtaining a wake-up request sent by a CAN network of a whole vehicle, and making the vehicle wake up according to the wake-up request; actively obtaining driving data of a target vehicle every first preset time, obtaining driving behaviors of a driver in a current period according to a vehicle speed, an engine speed, a throttle pedal stroke value, a brake pedal stroke value, a steering wheel rotation angle and a steering wheel rotation angle acceleration; generating a trip report of the target vehicle according to driving behaviors corresponding to multiple continuous periods respectively, and uploading the trip report to a cloud service platform. The driving data collection and analysis method can intuitively reflect existing bad driving behaviors of a customer by collecting real-time data, and can help the customer to improve the existing bad driving habits, so that the behaviors of the customer are more standardized and economical.

The invention relates to lane selection using networked vehicle data including lane connectivity and input uncertainty information. A method may include generating a dynamic lane level forward graph including a plurality of nodes for a road. The method may include calculating a weight for each action of a vehicle traveling from one node to a next node. The method may include determining values for different actions of the vehicle based on a dynamic lane level forward graph starting from a node of the vehicle to a node of the destination and a weight for each action of the vehicle. The method may include selecting an action among the different actions based on a comparison of values of the different actions. The method may include instructing the vehicle to perform a selected action for the vehicle.

A system for providing an output signal based on a generated surroundings model of surroundings of a mobile platform is proposed. The system includes: a first subsystem, a second subsystem, wherein the second subsystem is configured to redundantly provide a functionality of the first subsystem, and a third subsystem, wherein the third subsystem is configured to redundantly provide the functionality of the first subsystem and / or of the second subsystem; a first comparison system, a second comparison system, wherein the first comparison system and / or the second comparison system are configured to detect at least one fault in the first subsystem and / or a fault in the second subsystem and / or a fault in the third subsystem and / or to identify the corresponding faulty subsystem.

Drive control device (1) for providing drive control for a hybrid vehicle by controlling a machine (2) and motor-generators (4, 5) which can be operated to output a charge of electrical power to a battery (21) and to receive a supply of electrical power from the battery (21) to supply the hybrid vehicle with a motive force obtained from the machine (2) and the motor-generators (4, 5), wherein the drive control device has a power sharing and composition system having four axes connected to each of the rotating elements of the two planetary gear sets (8, 9); wherein two motor generators (4, 5) are connected to the battery (21); wherein one of the motor generators (4, 5), the machine (2), a drive shaft (7) connected to a traction wheel (6), and the other of the motor generators (4, 5) lie on a collinear diagram, wherein the four axes of the power sharing and composition system are each connected to one motor generator (4, 5), the machine (2), the drive shaft (7), and the other motor generator (4, 5); the drive control device further comprises the following: a drive power target calculation function (42) for calculating a drive power target based on an accelerator pedal position and a vehicle speed; a charge / discharge power target calculation function (43) for calculating an electrical charge / discharge power target to / from the battery (21) based on a charge / discharge state of the battery (21); a first machine power target calculation function (44) for calculating a first machine power target based on the drive power target calculated by the drive power target calculation function (42) and the electrical charge / discharge power target calculated by the charge / The discharge power target calculation function (43) is calculated; a first machine operating point target calculation function (46) for calculating a first machine speed target and a first machine torque target, both of which correspond to the first machine power target calculated by the first machine power target calculation function (44), based on information about a machine operating point identified by a relationship between a machine speed and a machine torque; a first calculation function (47) for an upper machine speed target limit for calculating an upper limit of the first machine speed target based on the vehicle speed, wherein the upper limit of the machine speed is limited by an upper limit of the speed of one motor-generator (4, 5) and is subject to changes depending on the vehicle speed, and wherein the calculation function (47) of the upper first machine speed target limit calculates the upper limit of the first machine speed target based on the vehicle speed and the upper limit of the speed of one motor-generator (4, 5); a machine operating point target calculation function (48) to determine whether the first machine speed target calculated by the first machine operating point target calculation function (46) is greater than the upper limit, wherein the machine operating point target calculation function (48) is designed to: to set a second machine speed target to the upper limit and to calculate a second machine torque target corresponding to the second machine speed target, based on the information about the machine operating point, when it is determined that the first machine speed target calculated by the first machine operating point target calculation function (46) is greater than the upper limit; and to set the second machine speed target and the second machine torque target as the first machine speed target and as the first machine torque target if it is determined that the first machine speed target calculated by the first machine operating point target calculation function (46) is less than or equal to the upper limit; a second machine power target calculation function (49) for calculating a second machine power target based on the second machine speed target and the second machine torque target; a calculation function (50) for an electrical power target for calculating an electrical power target that specifies an amount of electrical power to be generated by driving the motor generators (4, 5) to charge the battery (21), or to be supplied from the battery (21) to the motor generators (4, 5) to drive the motor generators (4, 5), based on a difference between the drive power target calculated by the drive power target calculation function (42) and the second machine power target calculated by the second machine power target calculation function (49); a machine control (40) that is configured to to control a moment of the machine (2) based on the second machine moment target; and a motor-generator control unit (60) configured to control the motor-generators (4, 5) based on the second machine speed target, the second machine torque target, and the electrical power target.

The invention relates to a vehicle data configuration method, device and system and electronic equipment, and the method comprises the steps: responding to a target configuration instruction for a target scene, and displaying a configuration page of the target scene; in response to a condition configuration operation in the condition configuration area and a data item configuration operation in the data item configuration area, displaying a configured scene triggering condition in the condition configuration area, and displaying a configured data item in the data item configuration area; and in response to a confirmation operation on the configured scene triggering condition and the configured data item, generating scene configuration information corresponding to the target scene. The scene triggering condition of the target scene is determined through the condition configuration operation, the data item needing to be collected by the vehicle when the target scene is triggered is determined through the data item configuration operation, and the scene configuration information corresponding to the target scene is generated based on the configured scene triggering condition and the configured data item. Therefore, fine configuration of vehicle data acquisition schemes in various driving scenes is realized.

Provided is a driver information acquisition unit that acquires the observation information of the driver of the vehicle and a data processing unit that inputs the observation information and executes data processing are included. The data processing unit divides the observation information into conversion unnecessary data and conversion necessary data, executes abstraction processing or encryption processing for the conversion necessary data, and stores conversion data such as abstraction data or encryption data in a storage unit. The data processing unit executes the abstraction processing or the encryption processing for the individually-identifiable data included in the observation information or the data for which recording processing is not permitted in the personal information protection regulation as the conversion necessary data.

The invention relates to an energy consumption control method, device and equipment of a hybrid vehicle, a storage medium and a product. The method comprises the following steps: acquiring current energy consumption data, current navigation information, current energy data and user preference information of a vehicle driven by a user, and identifying current road condition information of a current driving route of the vehicle of the user; based on the user preference information of the user, a preferred energy supply and consumption mode and energy supplement limitation information of the user are identified, and an energy supply and consumption control strategy of the vehicle is generated through the preferred energy supply and consumption mode of the user; and generating an energy supply and consumption control scheme of the vehicle and energy supplement prompt information of the user based on the energy supply and consumption control strategy of the vehicle. By adopting the method, the energy consumption supply decision accuracy of the hybrid vehicle can be improved.

The present application relates to the technical field of driving control, and particularly relates to a new energy vehicle safe driving control method and system. The method comprises the following steps: obtaining a historical emergency avoidance data set and a corresponding user misoperation braking state through a new energy vehicle control center, first performing sample undersampling processing to obtain an emergency avoidance balanced sample. Then, the balanced sample is used to analyze user misoperation braking abnormalities, and based on the braking abnormality data, an unordered vector intensity calculation in the time dimension is performed to generate unordered vector segmented clustering data. Subsequently, braking timing intervention correction perception is performed according to the clustering data to generate optimized braking intervention correction data. Finally, the optimized data is transmitted to a new energy vehicle control terminal to perform safe driving control. The present application optimizes the driving control technology to make the driving control technology more perfect.

A vehicle driving assistance apparatus executes a first driving assistance control of causing a host vehicle to travel by autonomously repeating powering and coasting of the host vehicle alternately such that a traveling speed of the host vehicle is maintained within a range between target upper and lower limit speeds. The apparatus sets a speed obtained by adding a predetermined upper limit variation amount to a set vehicle speed as the target upper limit speed, and sets a speed obtained by subtracting a predetermined lower limit variation amount from the set vehicle speed as the target lower limit speed. When the target lower limit speed is lower than a legal minimum speed during execution of the first driving assistance control, the apparatus changes the predetermined lower limit variation amount such that the target lower limit speed is equal to or higher than the legal minimum speed.

A method for processing image data and sensor data in an autonomous vehicle includes receiving image data and sensor data generated by one or more sensors of an autonomous vehicle, encoding the sensor data into a multichannel sensor imaging tensor to generate encoded sensor data, providing the image data and the encoded sensor data to an autonomous driving system trained to control the autonomous vehicle, and executing, by the autonomous driving system, one or more operations for controlling the autonomous vehicle based at least in part on the image data and the encoded sensor data.

The embodiment of the invention provides an intelligent driving control method, electronic equipment, a storage medium and a program product. The method comprises the following steps: determining a current driving scene of a vehicle according to running state data, environment sensing data and navigation data of the vehicle; under the condition that the current driving scene meets the scene switching condition, target configuration parameters of the intelligent driving function are determined based on the current driving scene; the target configuration parameters comprise state parameters and target control parameters; and according to the state parameters and the target control parameters, current configuration parameters of the intelligent driving function are adjusted. The method is used for flexibly distributing the computing power based on the driving scene, and the effect of improving the response efficiency of intelligent driving under the condition of avoiding the waste of the computing power is achieved.

Provided is a vehicle driving assistance device that can prevent the driving speed of a host vehicle from temporarily falling below a legal minimum speed while performing driving assistance control for inertial running of the host vehicle. A vehicle driving assistance device (10) performs first driving assistance control for causing a host vehicle (100) to run by autonomously alternately repeating powered running and inertial running of the host vehicle in such a way as to maintain the running speed of the host vehicle within a range between an upper target speed and a lower target speed. The vehicle driving assistance device sets, as the upper target speed, a speed obtained by adding a predetermined upper limit amplitude to a set speed set as a target value for the running speed of the host vehicle by a user of the host vehicle, and sets, as the lower target speed, a speed obtained by subtracting a predetermined lower limit amplitude from the set speed, and changes the predetermined lower limit amplitude so that the lower target speed is equal to or higher than a legal minimum speed applied to the host vehicle when the lower target speed is lower than the legal minimum speed applied to the host vehicle while the first driving assistance control is being performed.

An object recognition apparatus may include a sensor, configured to obtain a point cloud associated with an object, and a processor. The processor may be configured to divide, based on a size of the point cloud, a specified space into a plurality of subspaces. The point cloud may be projected into the specified space. The specified space may include a plurality of voxels included in the plurality of subspaces. The processor may be further configured to identify, among the plurality of voxels and based on sequentially exploring the plurality of voxels, one or more valid voxels that include at least a part of the point cloud; assign, to the one or more valid voxels, at least one index; recognize, based on the one or more valid voxels and the at least one index, the object; and control, based on the recognized object, an operation of a vehicle.

This document describes techniques and systems for associating measurement points to spline points in determining a free space boundary for a vehicle assistance system. An exemplified method includes obtaining a set of measurement points that define a subset of a free space boundary near a host vehicle. A parametric curve that approximates spatial information representing the free space boundary is also obtained. The parametric curve includes a plurality of spline samples. The method then includes determining, for each respective spline point of the plurality of spline samples, a respective measurement point of the set of measurement points that corresponds to the respective spline point. In this way, a more efficient technique is disclosed for a vehicle assistance system to associate measurement points to spline points that make up a parametric-curve representation of the free space boundary.

A system (10) for operating an autonomous vehicle includes a passenger-detector (18) and a controller-circuit (32). The passenger-detector (18) is operable to determine a passenger-count (20) of passengers (22) present in a host-vehicle (12). The controller-circuit (32) is in communication with the passenger-detector (18) and vehicle-controls (26) of the host-vehicle (12). The controller-circuit (32) is configured to operate the host-vehicle (12) in an autonomous-mode (14) and in accordance with a parameter (40). The parameter (40) is set to an empty-value (46) when passenger-count (20) is equal to zero, and the parameter (40) is set to an occupied-value (48) different from the empty-value (46) when the passenger (22) count is greater than zero.

An autonomous vehicle including a microphone array of a plurality of microphones, a visual sensor network configured to receive visual signals, at least one processor, and at least one memory storing instructions is disclosed. The instructions, when executed by the at least one processor, cause the at least one processor to: (i) generate spatial beamforming maps locating a sound source using a beamforming model corresponding to acoustic signals received at the plurality of microphones of the microphone array; (ii) apply a synthetic aperture expansion to the acoustic signals to increase resolution of the spatial beamforming maps; and (iii) generate a future visual frame based at least partially upon temporal information extracted from the spatial beamforming maps and visualization maps generated based on the visual signals received by the visual sensor network.