10152results about "Anti-collision systems" patented technology

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention discloses a guardrail collision warning method and system, and the method comprises the steps: carrying out the nonlinear phase alignment of a multi-source heterogeneous signal through an adaptive variational mode decomposition algorithm according to the vibration acceleration, strain tensor and acoustic emission signals collected in real time through a multi-mode sensor array disposed at a guardrail key node, generating a three-dimensional dynamic strain field distribution vector; inputting the three-dimensional dynamic strain field distribution vector into a nonlinear dynamics reconstruction module, and extracting a chaotic feature fingerprint spectrum of the collision event; performing collision intensity grading processing on the chaos feature fingerprint spectrum, and outputting a quantitative evaluation matrix including a collision grade, a damage radius and a residual intensity prediction value; and triggering a multi-mode alarm protocol according to the quantitative evaluation matrix, and synchronously transmitting the multi-mode alarm protocol to a traffic management center and an adjacent vehicle OBU terminal. According to the embodiment of the invention, the state of the guardrail can be monitored in real time, and a collision event can be accurately evaluated and warned.

The invention discloses an expressway emergency management and control method and system, and the method comprises the steps: building a multi-dimensional heterogeneous data fusion model through a time-space diagram neural network according to the traffic flow, meteorological data and accident reports collected by roadside sensing equipment, and carrying out the dynamic prediction of the risk level of a road segment, and obtaining a real-time risk level map of a whole road network; based on the real-time risk level map, outputting a dynamic shunting scheme containing variable lane marks and a speed limiting strategy; according to the dynamic shunting scheme, generating a vehicle trajectory optimization strategy; and outputting an anti-interference optimization control strategy based on a vehicle trajectory optimization strategy in combination with sudden obstacle data polled by the unmanned aerial vehicle in real time. According to the embodiment of the invention, rapid and accurate path adjustment and cooperative control can be carried out, and the emergency management level of the expressway is improved.

The invention relates to the technical field of data processing, in particular to a data model processing method and system. The method comprises the following steps: acquiring multi-modal vehicle-mounted sensing data, and performing vehicle environment sensing to obtain dynamic vehicle environment sensing data; behavior pattern modeling is carried out through a vehicle-mounted sensing network, and a driver behavior prediction model is obtained; performing driving scene risk assessment based on the driver behavior prediction model and the dynamic vehicle environment perception data to obtain driving scene risk assessment data; performing vehicle multi-level early warning strategy analysis according to the driving scene risk assessment data to obtain a vehicle early warning instruction set; performing driver behavior prediction through a vehicle-mounted sensing network to obtain real-time early-warning driver behavior prediction data; and carrying out vehicle emergency intervention measure automatic decision making on the real-time driving early warning scene simulation data to obtain an emergency intervention decision execution instruction set. According to the invention, the driving safety and the response efficiency can be improved.

According to an embodiment, it is a system comprising, a sensor, a communication module, and a processor, wherein the processor storing instructions in a non-transitory memory that, when executed, cause the processor to scan, via the sensor of a host vehicle at a first frequency to observe surroundings for an erratic behavior, determine an erratic vehicle by analyzing for the erratic behavior, scan via the sensor at a second frequency to determine an identity of the erratic vehicle and analyze a traffic condition around the erratic vehicle, determine a possibility of a collision, alert via the communication module a nearby vehicle by sending a message, and determine an evasive action to avoid the collision.

The invention provides an electric two-wheeled vehicle collision detection and early warning method and related equipment, and the method comprises the steps: obtaining environment information in front of and behind a vehicle, the environment information comprising point cloud data collected by a radar sensor and image data collected by a camera; performing time synchronization and space coordinate conversion processing on the point cloud data and the image data, and constructing a sensing data frame under unified space-time reference; based on the sensing data frame, radar features and visual features of the target object are extracted respectively, feature fusion is carried out to form a joint feature vector, target matching and association are carried out based on the joint feature vector, and a fused obstacle sensing result is generated; and based on the obstacle sensing result, in combination with the current motion state of the vehicle, predicting and evaluating a potential collision risk, determining a collision risk level, and executing a corresponding early warning prompt according to the collision risk level. And the driving safety and the system response efficiency of the electric two-wheeled vehicle in a complex traffic environment are integrally improved.

The invention discloses a road intelligent induction and dynamic early warning method and system integrated with meteorological perception, and relates to the technical field of intelligent traffic and road safety. The method comprises the following steps: acquiring real-time weather, traffic and road data, performing multi-source data fusion by adopting an improved Kalman filtering and attention mechanism, and generating unified state estimation; dynamic risk assessment is carried out in combination with Bayesian reasoning and a Markov model, and speed-limiting adaptive adjustment is realized based on safety, traffic efficiency and energy consumption multi-objective optimization; and further calculating the length and position of the dynamic early warning area, and controlling devices such as intelligent spikes to issue induction information. The system comprises a data acquisition unit, a fusion estimation unit, a risk prediction unit, a speed adjustment unit, an early warning calculation unit and an induction unit. According to the invention, real-time monitoring, risk prediction and intelligent regulation and control of the road traffic environment in complex weather are realized, and the driving safety and the traffic efficiency are improved.

The invention discloses a multi-modal space-time fusion-based surrounding vehicle intention prediction method and related equipment. The method comprises the steps of obtaining historical trajectory data; inputting the obtained historical trajectory data into a trained weekly vehicle intention prediction model, and outputting a prediction result; wherein the model comprises two branches: one branch is a time sequence feature extraction branch, a long short-term memory network is adopted to encode historical tracks of vehicles, and time sequence features of the tracks are dynamically captured through an attention mechanism; 2, a spatial feature extraction branch: modeling a spatial interaction relationship among a plurality of vehicles by using a GraphSAGE network, and then distributing adaptive weights for different vehicles in combination with an attention mechanism of a graph attention network, so that the model can highlight the importance degree of key interference vehicles; and performing adaptive weighted combination on the features from the time sequence feature extraction branch and the spatial feature extraction branch, and obtaining probability distribution of various driving intentions at the future moment according to the fused features.

The technology relates to detection of a nearby occluded object based on illumination emitted from that object. Illumination by the occluded object of one or more areas in the surrounding area, for instance by headlights of an occluded vehicle, is detected by a perception system of a self-driving vehicle. The self-driving vehicle can classify the detected object to determine whether the illumination is caused by a vehicle or other road user, or from objects in the surrounding environment. Illumination data and other information can be evaluated by the self-driving vehicle, for instance to identify a type of the object, a location of the object along a roadway, to disambiguate the direction of travel of the other object, etc. As a result, the self-driving vehicle may infer the behavior of the other object and modify its own driving operations to account for the other object's presence and likely behavior.

Methods and systems include determining actions for agents in a driving scenario using a diffusion model, based on individual controllable behavior patterns for the agents. A state of the driving scenario is updated based on the determined actions for the plurality of agents. The determination of actions and the update of the state are repeated in a closed-loop fashion to generate simulated trajectories for the plurality of agents. A planner model is trained to select actions for an operating agent based on the simulated trajectories.

Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and / or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and / or after a time associated with the potential collision is analyzed to determine whether the collision occurred and / or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and / or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.

In various examples, embodiments are directed to navigation-road and perception-lane matching for autonomous and semi-autonomous systems and applications. In this regard, lane-road matching is performed using geometric similarity to generate effective lane-road mappings for use in lane planning and decision making, among other things. In some embodiments, road data representing at least one road section and lane data representing a lane associated with a location of an ego-machine are received. Thereafter, a determination is made that one or more consecutive road sections match a lane based at least on a geometric similarity between the lane and the one or more consecutive road sections. A representation of the lane being mapped to the one or more consecutive road sections is generated based at least on the determining that the one or more consecutive road sections matched the lane.

The present invention relates to a warning system and in particular to a warning system that is movable to a monitoring zone. The invention has been developed primarily for use for early notification of danger by approaching vehicles to a monitored zone such as an emergency in an emergency lane of a roadway. The warning system of the invention comprises at least two forms of detectors for detecting and tracking vehicles and obtaining characteristics of the tracked vehicle relative to the monitored zone a determinator for receiving the characteristics of the tracked vehicle relative to the monitored zone and assessing the expected relative impact of the vehicle on the monitored zone and an alarm system for providing an alarm action according to a predetermined danger of the assessed expected relative impact of the vehicle to the monitored zone.

The invention relates to a multi-scene self-adaptive high-speed obstacle early warning method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring multi-source data of traffic sensing equipment, and carrying out space-time calibration and semantic analysis on the data to obtain a vehicle track feature vector set; thirdly, calculating real-time traffic flow parameters of the gridding area according to the feature vector set, and further generating a traffic feature database; secondly, modeling is carried out on the traffic characteristic database according to time periods and weather, and theoretical speed baseline parameters of all scenes are obtained; and finally, constructing a multi-dimensional feature matrix according to the theoretical speed baseline parameters, inputting the multi-dimensional feature matrix into the classification model to calculate an abnormal probability value, and if it is detected that a continuous abnormal probability value exceeds a dynamic threshold value in a preset time window, generating an early warning signal. By adopting the method, real-time monitoring and early warning of obstacles in multiple scenes of the expressway can be realized, and more effective guarantee is provided for driving safety of the expressway.

According to an embodiment, it is a system comprising, a communication module, and a processor; wherein the processor storing instructions in a non-transitory memory that, when executed, cause the processor to receive, a first message by a host vehicle via the communication module from a source, wherein the first message comprises information on an erratic vehicle, wherein the information comprises one or more of a license plate number, a make and model, a color, a first contact zone, a location of the erratic vehicle, and a direction of travel of the erratic vehicle; and transmit a second message, via the communication module, to alert a second vehicle, wherein the second message comprises a part of the first message, a second contact zone, and an evasive action for the second vehicle.

The invention relates to the technical field of tunnel parking guidance, and discloses a tunnel emergency parking strip guidance emergency system and method, and the system comprises a data collection module, an analysis decision module, and an emergency indication module. Wherein the data acquisition module is used for acquiring environment data and vehicle data in a tunnel; the analysis decision module divides the tunnel into different emergency sections based on the environment data, sets an emergency indication strategy for each emergency section, and adjusts the emergency indication strategies based on the vehicle data; the emergency indication module performs emergency guidance on an emergency parking strip in the tunnel based on the emergency indication strategy; the analysis decision module also performs abnormal vehicle detection based on the vehicle data and generates an emergency guidance strategy; the emergency indication module performs emergency stop guidance on the abnormal vehicle based on the emergency guidance strategy; the indication and guidance modes of the emergency parking strip in the tunnel can be reasonably set and adjusted, and tunnel traffic safety can be guaranteed.

The invention relates to the technical field of traffic safety, in particular to a beforehand prevention and control strategy generation method for traffic safety risks of a highway network in a mountainous area. Comprising the following steps: risk diagnosis and data preparation: collecting road basic information, historical traffic flow data, meteorological data and accident-prone point data of a mountainous area expressway network; constructing a risk assessment model: selecting road alignment, traffic flow, weather and environmental toughness indexes, determining index weights by adopting an improved analytic hierarchy process, constructing the risk assessment model through a fuzzy comprehensive evaluation method, and calculating the traffic safety risk level of each road section; a prevention and control strategy is generated; and strategy verification and iteration. According to the method, the total factor evaluation model is constructed by integrating the road network topological structure, the traffic flow characteristics and the meteorological sensitive section data, so that the problem of one-sided risk identification caused by single factor analysis in the prior art is solved, and systematic description of the mountainous area highway composite risk scene is realized.

The invention discloses a multi-agent traffic event real-time response system and method, and the system comprises an event perception fusion module which is disposed on vehicle-mounted and roadside equipment, fuses the vehicle motion state, environment perception data and meteorological data, and generates an unstructured traffic behavior set in real time through a lightweight recognition model; the edge decision-making module is used for defining decision-making responsibilities by adopting a dynamic role binding mechanism and decomposing tasks based on a hierarchical memory storage architecture to generate response instructions; the central collaborative decision-making module is used for realizing strategy decoupling through role adapter layering and coordinating cross-domain response measures by adopting a three-level conflict resolution mechanism; and the strategy optimization module updates center parameters through federated learning and optimizes a response strategy in combination with a reinforcement learning reward model, so that second-level identification and dynamic response of traffic events can be realized, the congestion dissipation time is shortened, the false report and missing report rate is reduced, the secondary accident rate is reduced, and the disposal efficiency and safety of complex traffic scenes are comprehensively improved.

An object detection device includes an azimuth sensor that measures an azimuth region of an object that is present around a vehicle, a plurality of distance sensors each of which measures a distance to the object, and an object determination section that calculates arcs of circles whose radii are the distances measured by the respective distance sensors and calculates intersection points of a tangent to adjacent ones of the arcs and the azimuth region measured by the azimuth sensor.

The invention relates to the technical field of intelligent traffic systems, in particular to a curve early warning method, system and terminal based on target detection, and the method comprises the steps: generating a visual twin image of a target curve; the visual twin images are displayed on display devices on the two sides of a target curve; if the movement track of the moving target is overlapped with the position of the obstacle or the movement track of the moving target is overlapped with the estimated movement track of the obstacle, early warning information is sent, and the early warning information comprises sound prompt information and image prompt information. The graphic prompt information comprises that a visual twin image corresponding to the moving target is rendered on a display device on one side of the road consistent with the moving direction of the moving target. A visual twin image of a target curve is generated through a multi-sensor fusion technology, the motion states of a moving target and an obstacle are reflected in real time, and early warning information is sent out when a potential collision risk is detected, so that the traffic accident rate of a curve area is effectively reduced.

The invention relates to an automatic driving risk quantification method based on a conflict risk field. Comprising the steps of 1, constructing a basic risk field; step 2, under a basic risk field framework, constructing a conflict risk field by taking an ADV as a center; step 3, regarding the conflict risk field as a repulsive force acting on the ADV, and using the repulsive force to quantify the dynamic influence of the environmental elements and various TP states on the driving safety of the ADV; the method comprises the following steps: firstly, constructing a basic risk field on the basis of traffic vehicle distribution and motion characteristics, further forming a vehicle forward, lateral, backward and traffic regulation constraint multi-dimensional conflict risk field on the basis, and converting an abstract traffic conflict relationship into a computable repulsive force model; dynamic quantitative characterization of driving risks in a state dimension, a space dimension and a time dimension is realized, and continuous optimization and evolution of an end-to-end automatic driving algorithm are supported by means of a repulsive force model constructed based on a conflict risk field.

A vision language model (VLM) may be used to evaluate signs that designate restricted or toll lanes, determine whether it is permissible (and / or the cost) to merge into a restricted or toll lane, and / or determine when to merge out of a restricted or toll lane based on the cost. Frames from one or more (e.g., front-facing) camera(s) may be evaluated for applicable signs (e.g., using a sign recognition DNN or a VLM). If detected, the (e.g., cropped) image of the sign may be provided as input to a VLM with a textual prompt instructing the VLM to determine whether to drive in the restricted or toll lane (e.g., whether it can be taken within budget) and / or what the cost would be. The generated response may be provided to an ADAS to trigger an initiation of a merge left or right or a determination to stay in the current lane.

The invention relates to a road section risk early warning method based on thunder-vision fusion perception, and the method comprises the following steps: S1, obtaining the high-precision space-time position information of a vehicle in a tunnel through the multi-source information fusion of a millimeter-wave radar and machine vision; s2, mapping the vehicle position under the global coordinate system to a coordinate system expanded along a road center line, and representing the vehicle position as a longitudinal projection position and a transverse offset along the road direction; s3, under the coordinate system, taking anti-collision time as a core index, combining lane judgment and relative speed, constructing a dynamic longitudinal risk grading model, and performing linkage triggering with a warning system; s4, constructing a regional risk integral model; and S5, speed limiting information is issued to all driving-in vehicles in real time through the variable speed limiting screen, and the rear-end collision risk is actively prevented and controlled. According to the method, the sudden risk response time can be shortened, the speed limit control response precision can be improved, the rear-end collision rate of the diversion and convergence areas can be reduced, false alarm interference can be reduced, and the tunnel driving safety and the information guiding efficiency can be improved.

Collaborative enhanced sensing systems and techniques are described. A system obtains sensor data of an environment from one or more sensors. The sensor data includes different representations of one or more portions of the environment from multiple perspectives. The system identifies a subset of the different representations of the one or more portions of the environment based on context corresponding to a vehicle in the environment. The context can include, for example, a position, a direction of travel, a speed, and / or a planned movement of the vehicle. The system causes an enhanced representation of the environment for the vehicle to be generated at least in part based on consolidating and / or fusing together the subset of the different representations. The system can cause an indication of the enhanced representation of the environment to be output at the vehicle.

The invention relates to the technical field of intelligent traffic, and discloses an expressway parking risk dynamic early warning method and system based on spatial-temporal characteristics and an adaptive threshold, and the method comprises the steps: collecting vehicle driving data through a multi-type sensor, obtaining vehicle internal data through 5G communication, and employing a D-S evidence theory for fusion; performing spatio-temporal feature processing on the fused data, constructing a spatio-temporal matrix, and extracting features by using a spatio-temporal convolutional neural network; constructing a parking risk assessment model by combining a gating circulation unit of an attention mechanism based on the extracted features, and outputting a parking risk probability in a period of time in the future; according to road sections, time periods and weather conditions, an early warning threshold value is dynamically adjusted by using a reinforcement learning algorithm, multi-channel early warning is triggered when the threshold value is exceeded, and the system comprises a data acquisition and transmission subsystem, a data processing and analysis subsystem and an early warning decision and release subsystem. The objective of the invention is to realize prediction and timely and effective early warning of the parking risk of the high-speed vehicle.

An assisted driving method and apparatus. The assisted driving method comprises: acquiring multi-modal perception information, wherein the multi-modal perception information is collected by a plurality of sensors provided in a vehicle (100); acquiring a plurality of groups of display information, wherein the display information comprises target areas to be displayed and grid granularities; performing perception prediction on the basis of the multi-modal perception information and the plurality of groups of display information, so as to obtain a plurality of pieces of occupancy grid information, wherein the occupancy grid information is used for describing the distribution of obstacles around the vehicle (100); and displaying a plurality of occupancy grid pictures on a human-computer interaction interface on the basis of the plurality of pieces of occupancy grid information. By means of the present application, not only can the panoramic perception capability of a driver for obstacles within a large range be improved, but the refined perception capability of the driver for obstacles within a short-distance range can also be improved, and the perception capability of the driver for surrounding environments can be improved by means of a combination of the panoramic perception capability and the refined perception capability, thereby avoiding scratching.

A warning system according to the present disclosure includes: a character information generation unit that generates, by using a captured image of the inside or the outside of a mobile body, character information indicating a character representing an event appearing in the captured image; and a warning information notification unit that issues a notification of warning information corresponding to the character information generated by the character information generation unit. In a warning method according to the present disclosure, a computer: generates, by using a captured image of the inside or the outside of a mobile body, character information indicating a character representing an event appearing in the captured image; and issues a notification of warning information corresponding to the generated character information.

The invention relates to a method (50) and system (10) for assessing situations presenting an accidentology risk to a vehicle driving on a road network in a determined environment, the method comprising: - monitoring (51) the progress of the vehicle on the road network and in the environment into which the road network is integrated and through which the vehicle is moving; - detecting (52) a risky situation presenting a plurality of accidentology risk factors (13); -warning (53) the driver when a risky situation is recognized; - analysing (54) data recorded before detection (52) of the risky situation, in order to determine an origin of the risky situation; and - producing (55) driving advice depending on determined contextual parameters (16), the driving advice identifying the detected risky situation and recommending a driving behaviour in order to reduce the risks run by the vehicle when confronting the situation. The invention also relates to a vehicle in which the system (10) is embedded, and executing the method (50).

The invention relates to the technical field of intelligent traffic, in particular to an integrated fog measurement type highway safe driving guidance device and method, and provides the following scheme that local visibility data is acquired based on a plurality of highway safe driving guidance devices and uploaded to a region controller to generate guidance information. And the regional controller performs research and judgment calibration and gradient calibration on the data, corrects measurement errors and generates road visibility space distribution information by screening calibration anchor points and adopting an exponential attenuation function and iterative gradient calculation. Based on the information, a connectivity algorithm is used to identify a fog area, and in combination with road environment and historical accident data, an optimal induction strategy is matched, and induction information is dynamically adjusted. The fog area detection accuracy is improved, the induction information matching is optimized, and the driving safety and the passing efficiency under the low-visibility condition are improved.

The invention relates to an expressway interleaving area man-machine mixed driving traffic flow intelligent network connection vehicle control method, and belongs to the technical field of intelligent traffic, and the method comprises the steps: obtaining the historical state data of a target vehicle and surrounding vehicles; the historical state data are input into a completely trained lane changing intention prediction model, the lane changing intention of the target vehicle is obtained, and the completely trained lane changing intention prediction model comprises an input layer, a CNN layer, an LSTM layer, an Attention layer and an output layer which are connected in sequence; and when the lane changing intention is lane changing, controlling the target vehicle and the cooperative vehicle on the target lane to execute a cooperative lane changing strategy. According to the method, the lane changing intention prediction model of the expressway interleaving area based on the CNN, the LSTM and the attention mechanism is constructed for the complex traffic scene of the interleaving area, the lane changing intention of the intelligent network connection vehicle in the expressway interleaving area is effectively and accurately predicted, and the vehicle is controlled to execute the corresponding lane changing strategy.