1062results about "External condition input parameters" patented technology

The invention discloses a vehicle idling start and stop control system and method. The control system judges whether a vehicle has idling start and stop conditions by monitoring the water temperatureof an engine, the capacity of a storage battery, a safety belt and other information, judges whether the vehicle has conditions of stopping the engine by monitoring the rotating speed of the engine, the speed of the vehicle, the brake air pressure and other conditions, and judges whether the vehicle has conditions of starting the engine by monitoring the gear of a gearbox, the speed of the vehicle, a hand brake and other information. When the relevant conditions are met, start and stop work signals are sent to the engine though a CAN bus. In order to guarantee the reliability of frequent startand stop of the engine, the system strengthens flywheel gear rings of a starter and the engine. In order to ensure that the battery does not lose power, a storage battery capacity sensor is added tothe system on the basis of increasing the capacity of the storage battery, and the idling start and stop are allowed when the state of charge (SOC) of the storage battery satisfies a safe starting threshold. The idling start and stop control system has advantages of low cost and high reliability, and is especially suitable for commercial vehicles.

An AI-connected-electrified (ACE) heavy truck system equipped with an intelligent multi-mode hybrid (iMMH) powertrain system and a vehicle supervision control strategy based on machine learning (ML) or reinforcement learning (RL) paradigm are presented. This system ensures industry-leading power and braking performance of the ACE heavy truck while automatically optimizes both energy saving and emission reduction based on the vehicle's dynamic driving data and 3D electronic map information of roads for any transport event. In this application, the conventional analog electronic control (AEC) method is replaced by a novel digital pulse control (DPC) method on the instantaneous power function of the engine. The DPC method converts the complex surface working conditions of the AEC engine of the hybrid vehicles into simpler pre-defined working condition lines of the DPC engine. Consequently, the multi-variable nonlinear technical problem of simultaneously optimizing real driving environment (RDE) fuel consumption and pollutant emissions of the ACE heavy truck is simplified into two decoupled quasi-liner optimization problems, and ensures the reduction of on-vehicle computing resources for real-time AI inference computation and the improvement of the optimal performance, the convergent rate, and the robustness of the corresponding fuel-saving algorithm for the trained ML model or the learned RL model. Ultimately, the ACE truck achieves in the engineering sense the global minimum RDE fuel consumption and pollutant emissions meeting the standard consistently at high performance to cost ratio for any transport event and the RDE fuel consumption is decoupled from the vehicle configuration parameters and the human driver.

The invention relates to a method for operating a virtual assistant in a vehicle (1), wherein the virtual assistant, at least taking into account a context determined for the vehicle (1) to assist a user (2), controls a vehicle function and / or causes the output of information to the user (2). The method according to the invention is characterized in that interactions performed by the user (2) are recorded, wherein a trigger module (3) classifies the interactions to generate interaction information; the context of at least the vehicle (1) is captured by a context module (4) and provided by the context module (4) in the form of context information; When a new interaction is recorded, the interaction information generated for this interaction and the context information available are fed to a conclusion module (5), which processes the interaction information and context information with a suitably trained large base model (6) and outputs conclusion information as a processing result, comprising control commands for controlling the vehicle function and / or output information for output to the user (2) via vehicle-specific output means.

The present application discloses a method, system, and computer system for detecting inclement weather driving conditions. The method includes obtaining an image captured by a camera mounted to a vehicle, determining a classification for road and weather conditions using a condition prediction model to analyze the image, in response to determining that the classification for road and weather conditions matches a particular predefined road and weather classification, determining an active measure associated with the particular predefined road and weather classification, and causing the active measure to be performed.

A vehicular control system includes a vehicle dynamics sensor disposed at a vehicle and capturing vehicle dynamics data representative of a state of motion of the vehicle. The vehicular control system, when the vehicle is at a first location on a road the vehicle is traveling along, determines the state of motion of the vehicle via processing of vehicle dynamics data captured by the vehicle dynamics sensor. The vehicular control system, after the vehicle has traveled along the road from the first location to a second location, predicts a second location of the vehicle based on the state of motion of the vehicle at the first location. The vehicular control system determines a sensitivity of the state of motion of the vehicle to change and corrects the predicted second location of the vehicle based at least in part on a global positioning system of the vehicle.

A remote assistance apparatus records, in an assistance content recording unit, assistance contents for remote assistance for an autonomous driving vehicle in response to the autonomous driving vehicle stopping as a result of an occurrence of a traveling abnormality, and then resumes autonomous traveling as a result of remote assistance for the autonomous driving vehicle being provided by an operator. The assistance contents include an assistance location and operator instruction contents for remote assistance instructed by the operator. The remote assistance apparatus detects an autonomous driving vehicle that is expected to pass through a predetermined assistance location included in predetermined assistance contents recorded in the assistance content recording unit. The remote assistance apparatus determines an assistance method of the remote assistance for the autonomous driving vehicle that is detected by the assisted vehicle detecting unit, based on the predetermined assistance contents that are recorded in the assistance content recording unit.

A vehicle for autonomous driving is capable of performing a minimum risk maneuver. The vehicle includes: at least one sensor, a processor and a controller, where the processor may detect whether a minimum risk maneuver (MRM) is required based on at least one of surrounding environment information and vehicle state information during autonomous driving of the vehicle, determine an MRM type based on a possibility of colliding with a neighboring vehicle when the MRM is required, and control to stop the vehicle based on the determined MRM type.

An apparatus for controlling a speed of an autonomous vehicle, includes: a sensor unit for measuring a speed of a wheel of the vehicle; a control unit for controlling the speed of the vehicle by generating a driving signal or a braking signal; and a torque applying unit for applying wheel torque to the wheel based on the driving signal or the braking signal. The control unit is configured to calculate a slip ratio based on the speed of the wheel, determine a road surface condition based on the wheel torque and the slip ratio, determine a maximum speed of the vehicle based on the road surface condition, and control the speed of the vehicle not to exceed the maximum speed. The road surface condition is classified according to a friction coefficient of the road surface, and the wheel torque is braking torque or driving torque applied to the wheel.

Systems and methods for agricultural lane following are described. For example, a method includes accessing range data captured using a distance sensor connected to a vehicle and / or image data captured using an image sensor connected to a vehicle; detecting a crop row based on the range data and / or the image data to obtain position data for the crop row; determining, based on the position data for the crop row, a yaw and a lateral position of the vehicle with respect to a lane bounded by the crop row; and based on the yaw and the lateral position, controlling the vehicle to move along a length of the lane bounded by the crop row.

To provide a system and a method available for adjusting field of view volumes of one or more sensors of an autonomous vehicle.SOLUTION: In a system and a method, each sensor in one or more sensors is configured to operate in accordance with a field of view volume equal to or less than a maximum field of view volume. The system and the method include determining an operation environment of an autonomous vehicle. The system and the method also include based on the determined operation environment of the autonomous vehicle, adjusting a field of view volume of at least one sensor in the one or more sensors from a first field of view volume to an adjusted field of view volume which is different from the first field of view volume. In addition, the system and the method include controlling the autonomous vehicle so as to operate using at least one sensor having the adjusted field of view volume.SELECTED DRAWING: Figure 9

Systems, methods, and other embodiments described herein relate to a learning model predicting event and behavior probabilities for an unexpected hazard using sensor data and a location profile along a planned path involving a vehicle. In one embodiment, a method includes acquiring sensor data and a location profile about a hazard on a road by a vehicle. The method also includes predicting an event probability and a behavior probability by a learning model for the hazard using the sensor data and the location profile about an area. The method also includes adapting a trajectory of the vehicle by a planning model using the event probability and the behavior probability.

A system includes sensors that obtain occupant data of an occupant within the ego vehicle. The ego vehicle is part of a platoon of vehicles. The system includes one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include determining, based on the occupant data, an impairment status of the occupant; and in response to determining the impairment status, selectively implementing an action to address the impairment status. The action includes communicating, using one or more coded status messages, at least a portion of the impairment status to one or more other vehicles in the platoon, and implementing a navigation action of the ego vehicle.

A trajectory planning system that calculates a laterally offset trajectory based on a maximum allowable in-lane lateral offset with respect to the center of a host lane of a vehicle includes one or more controllers including one or more processors that execute instructions to monitor perception data collected by a plurality of perception sensors until determining an occurrence of an offset event requiring the vehicle to deviate from an original trajectory. In response to determining the occurrence of the offset event, the one or more controllers calculate a non-linear error component of the maximum allowable in-lane lateral offset. The one or more controllers update the laterally offset trajectory of the vehicle with the maximum allowable in-lane lateral offset and apply a rate limiter to the maximum allowable in-lane lateral offset in real-time while instructing the vehicle to travel along the laterally offset trajectory.

Provided are a method, a computing device, and a recording medium for generating an artificial intelligence-based vehicle longitudinal control model for longitudinal control of autonomous vehicles. A method of generating an artificial intelligence-based vehicle longitudinal control model for longitudinal control of autonomous vehicles according to various embodiments of the present disclosure is a method of generating an artificial intelligence-based vehicle longitudinal control model for longitudinal control of autonomous vehicles, which is performed by a computing device. The method includes acquiring driving logging data for a vehicle, generating training data using the acquired driving logging data, and generating a vehicle longitudinal control model, which derives result data for vehicle longitudinal control, using the generated training data.

A travel controller includes a determiner, a curvature detector, a steering torque detector, an assistance torque setter, a first storage, and a controller. The assistance torque setter calculates one or both of a controlled variable to return a vehicle toward the middle of a lane and a controlled variable to avoid deviation of the vehicle, and sets assistance torque in accordance with the one or both of the controlled variables. The first storage holds map data in which a level of alertness of the driver determined by the determiner, a curvature of the lane detected by the curvature detector, and a first threshold value of the assistance torque are associated. The controller limits the one or both of the controlled variables at least when the steering torque becomes larger than the first threshold value, and afterward, allows the assistance torque setter to set the assistance torque.

An autonomy computing system of an autonomous vehicle for generating a virtual fence. The autonomy computing system receives data of a plurality of temporary barriers on a road based upon analysis of sensor data from one or more sensors of the autonomous vehicle, determines a closure intent associated with the plurality of temporary barriers exists, constructs a virtual fence associated with the plurality of temporary barriers, the virtual fence delineating a section of the road in which an autonomous vehicle is restricted from driving; and operates the autonomous vehicle based on the virtual fence.

The invention relates to a method for operating a virtual assistant in a vehicle (1), wherein the virtual assistant controls a vehicle function and / or brings about the outputting of information to a user (2), at least taking into account a context for assisting the user (2) as determined for the vehicle (1). The method according to the invention is characterized in that interactions carried out by the user (2) are detected, wherein a trigger module (3) classifies the interactions so as to generate interaction information; the context at least of the vehicle (1) is detected by a context module (4) and provided by the context module (4) in the form of context information; when a new interaction is detected, the interaction information generated for this interaction and the context information available in the process are supplied to a conclusion module (5) that processes the interaction information and context information with a suitably trained large basic model (6) and outputs, as a processing result, conclusion information comprising control instructions, able to be processed by a computing unit (7) in the vehicle (1), for controlling the vehicle function and / or output information for output to the user (2) via output means belonging to the vehicle.

Provided is a control system, including: a mobile object control unit configured to control a mobile object; an update control unit configured to receive an update program of the mobile object control unit from an external apparatus, and update the mobile object control unit with the update program; an instruction acquisition control unit configured to perform control to acquire a user instruction indicating that the update of the mobile object control unit with the update program is not executed; and a determination unit configured to determine whether an updatable condition is satisfied, which is a determination condition for determining the mobile object is in an updatable state or not, based on at least either of a driveline state of the mobile object or an opening or closing state of a door installed in the mobile object.

Provided is a computer-implemented method, system, and computer program product for predicting data insufficiency related to performing autonomous vehicle actions and dynamically mitigating the data insufficiency by collecting external data from additional data sources. A processor may direct, for an autonomous vehicle, an action in response to external data. The processor may collect contextual data about an environment in which the autonomous vehicle is operating. The processor may predict, based in part on the collected contextual data, an insufficiency of external data for performing the action. In response to the predicting, the processor may direct collection of additional external data from a second data source.

The present invention provides a control device and a control method that can improve assistance for a rider of a motorcycle. A control device for a motorcycle includes an execution unit (32) and a determination unit (33). The execution unit executes a speed control operation for controlling the speed of the motorcycle on the basis of positional relationship information, which is information relating to the positional relationship between the motorcycle and another vehicle traveling in the periphery of the motorcycle. The determination unit determines whether or not there is a need to stop or slow down the motorcycle while the motorcycle is traveling. During execution of the speed control operation, the execution unit executes a first automatic control operation that automatically stops or slows down the motorcycle when said need has been determined by the determination unit. The execution unit decelerates the motorcycle at least at one time point during execution of the first automatic control operation, regardless of the positional relationship between the motorcycle and the other vehicle.

The apparatus for lane following assist includes a reference position setting unit configured to set a reference position which is determined to be deviated to one side within a predetermined range from a center of a lane based on a driving tendency of a driver as a new reference position for the lane keeping assist, a deviation determining unit configured to determine whether the vehicle deviates from the reference position, and an offset following control unit configured to assist steering of the vehicle such that the vehicle follows the lane along the reference position.

An apparatus for driver assistance includes a controller detecting a preceding vehicle traveling in front of a host vehicle based on data output from at least one of a camera, a radar, and a light detection and ranging (LiDAR) and performing adaptive cruise control (ACC) maintaining a target inter-vehicle distance between the host vehicle and the preceding vehicle. The controller identifies whether the preceding vehicle is cutting out of a traveling lane according to movement of the preceding vehicle during the ACC, identify whether another lane to which the preceding vehicle is cutting in is a lane allowing the preceding vehicle to cut in in response to the preceding vehicle being cutting out of the traveling lane, and shorten the target inter-vehicle distance of the ACC in response to the another lane to which the preceding vehicle is cutting in being the lane allowing the preceding vehicle to cut in.

The disclosure relates to an electronic device, a method, and a storage medium for preventing a collision using a camera. The electronic device is for a vehicle and includes a memory for storing instructions, a communication interface, and at least one processor. The instructions, when executed by the at least one processor, enable the electronic device to: receive, from a camera included in the vehicle, a video regarding a surrounding environment of the vehicle through the communication interface; acquire position information regarding an external object included in the surrounding environment using the video; receive, from a wheel sensor included in the vehicle, rotation information representing an angle of a steering wheel of the vehicle through the communication interface; determine whether the external object is included in a dangerous area that the vehicle is expected to occupy according to movement according to the angle using the rotation information and the position information; and generate prompt information regarding the external object based on a determination that the external object is included in the dangerous area.

A roadside device for performing traffic coordination, and methods of use, are provided. The roadside device may comprise a transceiver configured to receive a first maneuver coordination message (MCM) from a cooperative automated vehicle (CAV), the MCM being configured to inform an intended maneuver of the CAV, a detector configured to detect a target object, and a processor configured to determine whether a predicted trajectory of the target object conflicts with the intended maneuver of the CAV and provide a coordination message as a signal viewable to a user of the target object when it is determined that the predicted trajectory of the target object conflicts with the intended maneuver of the CAV.

To provide a vehicle control device enabling prevention of unintended rapid deceleration of a vehicle due to a one-pedal control function when a speed control mode is canceled.SOLUTION: A vehicle control device can be applied with: a one-pedal control mode which can execute from acceleration of a vehicle 10 to deceleration due to braking in accordance with an operation amount of an accelerator pedal 12; and a speed control mode which automatically controls the speed of the vehicle 10. The vehicle control device includes a deceleration setting unit 33 which sets a first rate of change of deceleration or a first absolute value of deceleration of the vehicle 10 which occurs according to the operation amount of the accelerator pedal 12 in the one-pedal control mode to be smaller than a second rate of change of deceleration or a second absolute value of deceleration of the vehicle 10 which occurs according to the same operation amount of the accelerator pedal 12 in the one-pedal control mode other than a deceleration limitation period during the predetermined deceleration limitation period from the cancellation when the speed control mode is canceled while the one-pedal control mode and the speed control mode are applied.SELECTED DRAWING: Figure 2

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The application provides a kind of automatic driving collaborative perception method and device based on multimodal large model, the method comprises: by multimodal large model, the point cloud data of main end vehicle is handled, text information is obtained;Text features are extracted from text information, image features are extracted from the image data of main end vehicle, and depth map features are extracted from the depth map corresponding to point cloud data;According to text features, depth map features and image features are fused to obtain first fusion features;First fusion features and target end sent object features to be detected are fused to obtain second fusion features;Target end includes at least one of the collaborative end of main end vehicle and road end;Based on second fusion features, multi-terminal collaborative perception visual tasks are performed.The method improves the representation ability of perception features, and further improves the collaborative perception accuracy and robustness between multi-terminal vehicles.