224 results about "Stationary object" patented technology

This document describes techniques and systems related to a radar system using a machine-learned model for stationary object detection. The radar system includes a processor that can receive radar data as time-series frames associated with electromagnetic (EM) energy. The processor uses the radar data to generate a range-time map of the EM energy that is input to a machine-learned model. The machine-learned model can receive as inputs extracted features corresponding to the stationary objects from the range-time map for multiple range bins at each of the time-series frames. In this way, the described radar system and techniques can accurately detect stationary objects of various sizes and extract critical features corresponding to the stationary objects.

The invention discloses a slow space driving anti-collision early warning method based on a risk field model, and the method comprises the steps: firstly constructing a potential energy field intensity model for static objects and road traffic markings in a slow shared space on an urban non-motor vehicle lane, and then constructing a kinetic energy field intensity model for non-motor vehicles driving on the urban non-motor vehicle lane, a behavior field intensity model is constructed for non-motor vehicle drivers driving on urban non-motor vehicle lanes, then potential energy field, kinetic energy field and behavior field force models in a risk field model are calculated, and finally a comprehensive risk field model is obtained. Based on field force vector analysis, the application path of the potential energy of the risk field in the shared space of the non-motor vehicles is defined, and a conflict evaluation index under the risk field theory is provided on the basis. The index can be used for dynamically identifying potential conflicts, and provides a basis for early warning and intervention of non-motor vehicles.

The invention discloses an occupied grid labeling method and device, and the method comprises the steps: extracting an inter-frame pose transformation matrix and a detection frame tracking identifier corresponding to time sequence data when multiple frames of time sequence data are received; according to the inter-frame pose transformation matrix, carrying out occupied grid labeling on a static object in the time sequence data, and generating a static labeling result; tracking the position of the detection frame in each frame of time sequence data in real time according to the detection frame tracking identifier, and constructing a motion track of a target to which each detection frame belongs; in each frame of time sequence data, carrying out occupied grid labeling on the region where each motion track is located, and generating a motion labeling result; and fusing the static labeling result and the motion labeling result to generate an occupied grid labeling result corresponding to the time sequence data. Therefore, the occupancy grid label of the static object is propagated in combination with the inter-frame pose transformation matrix, and meanwhile, by fusing the occupancy grid label of the static object and the occupancy grid label of the motion trail, the requirements of the occupancy grid label for efficiency, precision and generalization are effectively met.

Provided is a driving assistance device that is safe and convenient in a situation where reverse playback cannot be performed in passing each other on narrow paths or the like. Driving operation of driver and surrounding obstacle information are stored at forward movement. At this time, the information of the front sensor (or the front camera) and the information of the rear sensor are integrated to determine and store the object type (stationary object, moving object). After the backward movement assistance is started, the surrounding obstacles of the rear sensor are collated with the stored surrounding obstacles, and execution of the backward movement assistance based on the stored driving operation is determined according to the collation result.

A method and device for vehicle camera autocalibration during vehicle travel for driver assistance systems and automated driving. A vehicle camera images a region of vehicle surroundings through a window. The method includes: providing a vehicle camera projection model including multiple extrinsic parameters, at least one intrinsic vehicle camera parameter and at least one window parameter; capturing a sequence of camera images during vehicle cornering; determining a curve type during the cornering; estimating the parameters using pixels in the sequence of images of stationary objects in the vehicle surroundings, the current vehicle movement, and the determined curve type, by minimizing an error function indicating the deviation between pixels which correspond to stationary objects in the vehicle surroundings and which are established from the sequence of images and pixels of the stationary objects which are projected by the projection model; and outputting at least one of the estimated parameters.

Methods and apparatus for monitoring user performance in launching objects at sporting events are described. A described method includes capturing, with a camera on glasses worn by a player participating in a sporting event, a plurality of two-dimensional (2-D) images of an object launched by a player participating in the sporting event. The launched object is identified and tracked with the camera for which an orientation is determined using an accelerometer. Based on the tracking, a position of the launched object at a point along a trajectory relative to a stationary object is determined, and an output indicative of user performance is provided based on the determined position of the launched object.

A vehicle travel control device (10) for controlling the travel of an own vehicle based on a predicted route which is a future travel route of the own vehicle, the device comprising: Object detection means (11, 12) for detecting the position of a stationary object located on the roadway or along the side of the roadway on which the host vehicle is traveling; Position storage means (25) for time-serially storing a position of a preceding vehicle, which is a position of the preceding vehicle traveling ahead of the host vehicle; Movement trajectory determining means (20) for comparing a movement locus of another vehicle, which is the movement trajectory of the position of the preceding vehicle stored in the position storing means, with the position of the fixed stationary object detected by the object detecting means to determine whether or not the movement locus of the other vehicle lies along the roadway; and Course calculation means (20) for validating the movement locus of the other vehicle when the movement locus of the other vehicle has been determined by the movement trajectory determination means to be along the road course, for invalidating the movement locus of the other vehicle when it is determined that it is not along the road course, and for calculating the predicted route based on the validated movement locus of the other vehicle.

An impact monitoring system includes multiple sensor nodes including multiple stationary sensor nodes affixed to stationary objects in an environment, the multiple stationary sensor nodes including a first stationary sensor node affixed to a first stationary object; and one or more mobile sensor nodes affixed to mobile objects in the environment, the one or more mobile sensor nodes including a second mobile sensor node affixed to a second mobile object; and a monitor configured to wirelessly receive first data characterizing an impact event experienced by the first stationary sensor node and second data characterizing the impact event experienced by the second mobile sensor node, and to identify that the second mobile object impacted the first stationary object based at least in part on the first data and the second data.

An apparatus for winding at least one cable includes a cover, a top portion attached to the cover, and a base attached to the top portion. A surface of the base is configured to be attached to a stationary object. The top portion includes a sloped surface configured to receive the at least one cable, such that the at least one cable remains detangled and is externally invisible when is fully wound around the sloped surface.

Methods and systems are disclosed for employing an ultrasonic sensor to estimate a range for a moving object. At least one processor separates returned signals received by an ultrasonic transducer into a stationary object signal and a moving object signal. A gain is determined based at least in part on the stationary object signal. The stationary object signal is combined with the moving object signal using the gain and then filtered. The range for the moving object is estimated using the filtered combined stationary and moving object signal.

A driver assist apparatus includes one or more processors configured to collectively provide instructions of settings with group, the settings being set to driver assist functions included in the group, and the group being configured so that the driver assist function of a vehicle is allowed to be added to or deleted from the group, wherein a plurality of the driver assist functions included in the group, to which the instructions are provided, is a driver assist function group including at least a function concerned with alert in a case where the vehicle approaches an obstacle and a function concerned with detection of a stationary object around when parking.

To provide a radar device with which it is possible to calculate azimuth errors with high accuracy.SOLUTION: A radar device is mounted to a mobile body 80, and comprises a detection unit 410, a speed acquisition unit 411, a tracking unit 413, a stationary object determination unit 414, a distance acquisition unit 415, an assumed angle calculation unit 416, and an error calculation unit 417. The tracking unit acquires the time sequence of object information corresponding to at least one object which is determined to be the same as a first object. The distance acquisition unit acquires a first distance which was acquired when a vehicle passed through the side of the first body out of the acquired time sequence of object information, when it is determined that the first object is a stationary object. The assumed angle calculation unit calculates an assumed angle on the basis of the acquired first distance and a second distance detected at this round of time. The error calculation unit calculates the difference between the calculated assumed angle and a first azimuth angle as the amount of an azimuth error at the first azimuth angle detected at this round of time.SELECTED DRAWING: Figure 9A

The invention discloses an active sonar target identification method based on message passing multi-hypothesis tracking, and belongs to the technical field of underwater target detection. The method comprises the following steps: firstly, processing single-cycle sonar data by using adaptive constant false alarm detection, obtaining observation points, and forming an effective observation set through hierarchical clustering; thirdly, extrapolating the existing target, calculating the association probability of the existing target and the current observation by adopting a message passing method, and generating a plurality of groups of association hypotheses; then, the target state is corrected through Kalman filtering, target rejection and new management are executed, and a high-probability hypothesis is reserved; and finally, estimating the motion speed based on the target multi-period position sequence, and distinguishing a static object from a suspicious motion target according to a speed threshold. Through the multi-hypothesis tracking and probability message passing mechanism, clutter interference and static target influence in a complex underwater environment are effectively overcome, and the stability of target tracking, the accuracy of speed estimation and the overall reliability of system identification are remarkably improved.

An outside sensing information processing device for suppressing adverse influence on warning of an advanced vehicle safety system. The vehicle safety system receives position information of an object detected by another vehicle or a road, the positions of feature points of a stationary object, and configuration information of an environmental map. When the system receives the information, the position of the object on the environmental map is calculated. Another vehicle also transmits position information of itself. The present vehicle checks an error of the position information of the vehicle detected by the other vehicle from the position information of the another vehicle and the relation of the position of a feature point detected by the another vehicle and the position of the feature point detected by the present vehicle. In the case where the error is large or unstable, use of the position information from the other vehicle is avoided.

This document describes techniques and systems for stationary object detection and classification based on low-level radar data. Raw electromagnetic signals reflected off stationary objects and received by a radar system may be preprocessed to produce low-level spectrum data in the form of range-Doppler maps that retain all or nearly all the data present in the raw electromagnetic signals. The preprocessing may also filter non-stationary range-Doppler bins. The remaining low-level spectrum data represents stationary objects present in a field-of-view (FOV) of the radar system. The low-level spectrum data representing stationary objects can be fed to an end-to-end deep convolutional detection and classification network that is trained to classify and provide object bounding boxes for the stationary objects. The outputted classifications and bounding boxes related to the stationary objects may be provided to other driving systems to improve their functionality resulting in a safer driving experience.

The present application relates to an apparatus for determining the ego-motion of a radar apparatus, the apparatus configured to: process radar-point-cloud information comprising a plurality of points by neural network models, the processing comprising: pointwise mapping the features thereof to a greater number of mapped-features to provide mapped-radar-point-cloud information, determining a global feature vector indicative of characteristics of the mapped-radar-point-cloud information, generating a feature matrix wherein each point therein is characterized by a feature-set based on a combination of the features, the mapped-features and the global feature vector, determining a pointwise-weight representing a likelihood that the point represents a stationary object in the space, a pointwise-offset comprising a correction to be applied to the Doppler velocity measurement to at least reduce the Doppler velocity measurement for points having a greater than expected Doppler velocity, and providing an estimate of the motion of the radar apparatus.

The invention relates to a method for determining a position of a stationary object (1), comprising the steps of: detecting (S1) by a plurality of vehicles (2) a distance of the object (1) from the respective vehicle (2); determining (S2) a distance between the detected stationary object (1) and a building (3) running along the road; determining (S3) an absolute longitudinal position component of the stationary object (1) along the direction of extension of the road and a relative lateral position component of the stationary object (1), wherein the absolute longitudinal position component is determined from a respective geodetic position of a vehicle (2), and wherein the determined distance of the stationary object (1) from the building (3) is used as the lateral position component.

The utility model discloses intelligent sensing radar monitoring equipment, and relates to the technical field of monitoring equipment, the radar monitoring equipment comprises a shell, and the interior of the shell is hollow; the monitoring camera is fixedly arranged on the shell and is used for monitoring a preset monitoring area; the circuit board is fixedly arranged in the shell, and a radar sensing device and a main control device are arranged on the circuit board; the radar sensing device is used for sensing a moving object or a static object in the predetermined monitoring area, and the main control device is connected with the monitoring camera and the radar sensing device through different interfaces and is used for sending the moving object or the static object to the radar sensing device under the condition that the radar sensing device senses an object of a preset type. And controlling the monitoring camera to start video recording. The utility model aims to improve the sensing accuracy of the sensing monitoring equipment.

The embodiment of the invention provides a road curvature determination method and device, electronic equipment and a readable storage medium, and relates to the field of data processing, and the method comprises the steps: obtaining environment data around a vehicle; wherein the environment data comprises environment point cloud data and / or environment image data; performing target object detection based on the environment data to obtain a target object detection result; establishing a road geometric model by adopting a target object detection result; a road curvature is determined based on the road geometry model. According to the method, the road geometric model can be established according to the static objects above the ground, the road curvature can be predicted according to the road geometric model, and the static objects above the ground are generally not easy to shield, so that the detection accuracy of the road curvature can be improved under the condition that accurate road surface information is difficult to obtain.

This route acquisition system includes: a plurality of marks for identifying installation positions, the plurality of marks being provided at different positions in a travel passage or on a stationary object in the vicinity of the travel passage; a sensor that is mounted on a moving body and reads the marks; and a route acquisition device that, on the basis of the result of reading by the sensor, detects a passing position and a passing time of the moving body and acquires the travel route of the moving body.

A computer-implemented method is provided for estimating velocity of a moving object. The method includes receiving an image, which includes a first strip generated by a first sensor and a second strip generated by a second sensor, and recognizing a moving object in the first strip of the image. The method also includes selecting a plurality of stationary objects that are captured with the moving object in the first strip of the image, while excluding stationary object(s) in the second strip of the image, wherein the plurality of stationary objects are non-moving features. The method further includes combining misregistration values for the plurality of stationary objects in the first strip of the image and calculating a velocity of the moving object, based on a raw velocity of the moving object and the combined misregistration values.

A method for moving a vehicle to a component of an object at a distance therefrom, the vehicle having a navigation module which has a camera and an evaluation electronics, and an identification element is attached to the object in a predetermined position in such a way that it is recognized by the camera in a far range (Dmax) of the vehicle from the object, and a reverse driving line of the vehicle is calculated by the evaluation electronics from the perspective position of the camera in relation to the identification element. The method improves the approach of a vehicle to a stationary object. In a start position (S) of the vehicle, the navigation module generates a static object coordinate system (KO) and a reverse driving line is calculated from the start position (S) to a pre-positioning point (SVi, SVii, SViii).

A motorized turf trimming system with integrated control for a carrier vehicle and rotary trimming attachments is disclosed. The system can be configured for automatically trimming around stationary objects such as trees, signposts, guard rail posts, barrier posts, light bases, etc.

This traveling route deciding device includes: an obstacle information acquisition unit which acquires obstacle information at regular intervals; an obstacle determination unit which determines whether each obstacle is a moving object or a static object, for all obstacles acquired by the obstacle information acquisition unit; a moving object list update unit which, in a case where it is determined that the obstacle is a moving object, registers the obstacle in a moving object list; a traveling prohibited area update unit which, in a case where it is determined that the obstacle is a static object, registers an area including the obstacle as a traveling prohibited area; and a traveling route deciding unit which performs calculation for avoiding collision between the vehicle and each obstacle on the basis of the updated moving object list and traveling prohibited area without using the continued traveling prohibited area, thus correcting a preset traveling route.

The techniques and systems herein enable ghost object detection. Specifically, a reflection line is determined that indicates a potential reflection surface between a first mobile object and a second mobile object. If enough stationary objects are within a region of the reflection line, then it is determined whether one or more of the stationary objects within the region are within a distance of a reflection point. The expected velocity of the second object is then determined and checked against the velocity of the second object. If the expected velocity is close to the velocity, then the second object is determined to be a ghost object. By doing so, the system can effectively identify ghost objects in a variety of environments, allowing downstream operations to function as designed.

An apparatus and a mehtod may enable a user equipment (UE) to more accurately identify whether certain radar detected objects are non-road objects / non-traffic participants, hence those non-road objects / non-traffic participants may be excluded from being fused as road objects / traffic participants. This may improve the accuracy and performance of assisted / autonomous driving. A UE detects a set of objects using at least one radar and at least one camera. The UE obtains at least one of: (1) a first indication of a first subset of objects in the set of objects being overridable or underridable based on a radar detection, or (2) a second indication of a second subset of objects in the set of objects being stationary based on a camera detection. The UE identifies a set of traffic-related objects in the set of objects based on at least one of the first indication or the second indication.

The present invention relates to a driving assistance device, a driving assistance method, and a program product, the driving assistance device including one or more processors configured to perform instructions of setting in a unified manner for a group, the setting being a setting for a driving assistance function of a vehicle included in the group, and the setting being a setting for a driving assistance function of the vehicle included in the group. The group is configured so that a driving assistance function can be added to the group or deleted from the group. The plurality of driving assistance functions included in the group on which the instruction has been made are a driving assistance function group including at least a function pertaining to an alarm when the vehicle approaches an obstacle and a function pertaining to a function pertaining to detection of a surrounding stationary object when the vehicle is parked.

The invention relates to a method for controlling an automatic overtaking manoeuvre involving a lateral lane change for passing a stationary object (340) in the current lane. The method comprises determining a necessary first maximum deceleration value (130) for braking in order, in the course of a first execution variant of the lane change, to come to a standstill at a predefined first relative distance (136) in front of the obstacle (340) in the current lane (370) before the lateral lane change is executed. Furthermore, a necessary second maximum deceleration value (132) for the lateral lane change in the course of a second execution variant of the lane change is determined, without coming to a standstill in front of the obstacle (340) in the current lane (370). The first maximum deceleration value (130) is compared with the second maximum deceleration value (132), and the execution of the lateral lane change is controlled in accordance with one of the two execution variants of the lane change according to a result of the comparison.