13 results about "Time to collision" patented technology

The invention discloses an intelligent driving collision monitoring method based on trajectory prediction, and the method comprises the steps: taking the following features of a vehicle, the interactive behavior features and the eye movement features of a driver as an LSTM-Attention deep learning model, inputting a predicted vehicle moving trajectory, and carrying out the prediction of the vehicle moving trajectory through employing a directed bounding box method on the basis of the predicted trajectory, and OBB) detecting whether there is a risk conflict possibility. According to the method, after the existence of the collision risk possibility is confirmed, a collision early-warning system based on TTC (Time to Collision) is adopted to perform early-warning grading on the severity of the risk collision, and corresponding early-warning measures are taken for different risk grades, so that a driver can timely obtain the potential collision risk condition to take corresponding risk avoiding measures, and the driving safety is improved. And the safety of road traffic is improved.

The present disclosure provides an event camera-based time-to-collision estimation method, an electronic device and a storage medium, which estimates the time-to-collision based on a stream of events acquired by an event camera, overcomes the problem of very large delays in the prior art, and improves the time-to-collision estimation accuracy rate and robustness in autonomous driving scenarios, while the feature of low energy consumption enables the method to be better adapted to embedded scenarios, reducing procurement and running costs, the TTC output frequency of the method of the embodiment can be up to 200 Hz, so the method has great application potential in real-time TTC tasks, and is more suitable for real-time TTC tasks with sudden change in relative speed.

The invention provides, amongst other aspects, a method for dynamically determining a time-to- collision, TTC, related quantity of a rail vehicle with an object, the rail vehicle comprising an imaging sensor and a laser-based ranging sensor, the method comprising obtaining image data from the imaging sensor and point cloud data from the laser-based ranging sensor; detecting the object in the image data and the point cloud data based on first training data relating to a plurality of objects; extracting information from the image data and the point cloud data, wherein the step of extracting comprises determining a rail track condition in the image data and the point cloud data based on second training data relating to a plurality of rail track conditions; and determining the TTC-related quantity of the rail vehicle with the object based on the extracted information from the image data and the point cloud data.

The invention relates to collision time estimation in a computer vision system. The invention provides a method for estimating a time-to-collision (TTC) of a detected object in a computer vision system, the method comprising: determining (200) a three-dimensional (3D) position of a camera in the computer vision system; determining (202) a 3D position of the detected object based on a 2D position of the detected object in an image captured by the camera and an estimated ground plane corresponding to the image; calculating (204) a relative 3D position of the camera, a velocity of the relative 3D position, and an acceleration of the relative 3D position based on the 3D position of the camera and the 3D position of the detected object, wherein the relative 3D position of the camera is relative to the 3D position of the detected object; and calculating (206) the TTC of the detected object based on the relative 3D position, the velocity, and the acceleration.

The present application provides an anti-collision method, device, computer device and storage medium for an autonomous vehicle. Obtain environmental information and the driving information of the vehicle itself; determine a plurality of potential collision objects existing in the current environment according to the environmental information; predict the movement trajectory of the vehicle itself within the first time period according to the driving information of the vehicle itself; determine the potential collision objects whose contours overlap with the movement trajectory among the plurality of potential collision objects as target collision objects; calculate the predicted collision time with the target collision objects; determine the target braking mode according to the predicted collision time, and execute the target braking mode. This method realizes the flexible and reasonable selection of the braking mode, and improves the riding experience on the basis of ensuring safety.

The invention relates to a safety action execution method of a body-equipped robot and a body-equipped robot system. The method comprises the following steps: receiving a task instruction, and executing a task indicated by the task instruction; in the process of executing the task, if a collision event is detected through a collision switch of the robot with the body, event information of the collision event is recorded, multiple pieces of event information are arranged according to the time sequence to form an event information sequence, and the event information comprises the collision position and the corresponding collision time; according to a first subsequence in a first sliding time window in the event information sequence, collision density information corresponding to a grid map of the robot with the body is generated, and the collision density information comprises collision density corresponding to each grid in the grid map; and under the condition that the collision density information represents that a first safety action execution condition is met, executing a safety action. By adopting the method, the safety can be improved.

This invention discloses a collision avoidance warning method based on fuzzy logic. First, the uncertainty of the driver's lateral dynamics is modeled using fuzzy logic to obtain the lateral dynamics membership coefficient. Based on this, the membership coefficient is used to weight the one-dimensional pre-collision time to collision to obtain a two-dimensional road safety indicator, FL-iTTC. The proposed measurement is then used as the basis for alarms, and the collision avoidance system alarm threshold is calibrated using natural driving data. The proposed method can accurately identify potential collision events in complex traffic scenarios, especially multi-lane deceleration and lane change scenarios, and has significant positive implications for road traffic safety and the design of collision avoidance warning systems.

A collision alert device includes a calculation part repeatedly calculating, at a prescribed time interval, a time-to-collision obtained by dividing a distance between moving objects by a relative velocity of two moving objects, the distance between moving objects being a distance between the two moving objects, and an output part outputting an alert in at least one of the two moving objects as a condition of the time-to-collision being equal to or less than a predetermined threshold, the relative velocity is a centripetal relative velocity of a moving velocity of each of the two moving objects, the centripetal relative velocity being calculated from a centripetal velocity that is a component along a line segment connecting a position of each of the two moving objects, and the time-to-collision is a centripetal time-to-collision obtained by dividing a distance between the two moving objects by the centripetal relative velocity.

Systems and methods are provided for generating collision avoidance directives. A current lateral incursion of an intruder aircraft onto a runway is determined based on a location of the intruder aircraft. An incursion line extending across the runway based on the location of the intruder aircraft is defined. A time to collision when an ego aircraft is expected to cross the incursion line is determined. A predicted lateral incursion of the intruder aircraft onto the runway based on the time to collision, the current lateral incursion, and a velocity of the intruder aircraft is generated. A determination is made whether a lateral margin between the ego aircraft and the intruder aircraft on the runway at the time to collision is greater than a lateral margin tolerance. A first directive alert to proceed with the one of a take-off and a landing via the runway is generated based on the determination.

The invention is suitable for the technical field of data processing, and provides a data storage method, a solid state disk and a program product, and the method comprises the steps: obtaining environment data of an area where a vehicle is located in a vehicle driving process; the environment data comprises obstacle data in a shooting range of the vehicle; inputting the environment data into a trained collision prediction model for processing to obtain collision prediction information between the vehicle and the obstacle; the collision prediction information comprises collision time; if the collision time is smaller than or equal to the first preset time, target data corresponding to the vehicle in the preset collision time period is solidified to the nonvolatile storage device. According to the method, the risk is pre-judged in advance through the collision prediction model, the data solidification process is actively triggered before the actual collision, the process does not depend on hardware signal feedback after the collision occurs, and the problems that the system fails and data cannot be written in when the collision occurs are solved.

A method of controlling a network, for example a 5G network, including a subject user equipment, UE, and a target UE, the method comprising: obtaining respective operational information, for example a

The invention discloses an AEB mistaken touch prevention method, device and equipment and a computer readable storage medium. According to the method, a vehicle and a target object are regarded as entities with specific boundary dimensions, and the motion trails of the vehicle and the target object in a period of time in the future are predicted. By judging whether the contours of the two predicted trajectories are overlapped or not and further calculating the area of the overlapped part, the collision risk is upgraded from a binary judgment of whether the collision occurs or not to a quantitative evaluation containing the occurrence possibility and the collision severity. Finally, the AEB is activated only when the predicted overlapping area is large enough and the collision time is short, so that a large number of low-risk misinformation scenes caused by slight intersection of tracks are filtered out on the premise of ensuring safety, and the AEB is enabled to be activated only under the conditions of high collision certainty and serious collision consequences.

The application relates to a kind of embodied robot safety action execution method and embodied robot system.The method comprises: receiving task instruction, executing the task indicated by the task instruction;In the process of executing the task, if collision event is detected by the collision switch of the embodied robot, record the event information of collision event, multiple event information is arranged in time sequence to form event information sequence, the event information includes collision position and corresponding collision time;According to the first sub-sequence in the first sliding time window in the event information sequence, the collision density information corresponding to the grid map of the embodied robot is generated, the collision density information includes the collision density corresponding to each grid in the grid map;In the case where the collision density information represents that the first safety action execution condition is met, safety action is executed.Using the method can improve safety.