51 results about "Time headway" patented technology

The invention discloses a data-driven network traffic modeling and simulation method based on dynamic modulus decomposition. The method comprises the following steps: collecting traffic data through sensors arranged in urban roads; filling missing values in the traffic data by adopting a data reconstruction method based on equilibrium state hypothesis; reconstructing and predicting the nonlinear dynamics of the space average speed in the traffic data based on the Koopman operator theory; calculating an expected time headway corresponding to a space average speed predicted by dynamic modulus decomposition KMD based on time headway distribution estimated offline, and calculating an outflow rate, namely traffic capacity, according to the expected time headway; and predicting the traffic density change based on a cellular transmission model and the traffic capacity calculated in the previous step. According to the method, the time headway (which not only represents the dynamic characteristics of traffic capacity in a congestion state, but also is a core parameter of a CTM model) is introduced, and a dynamic outflow flow rate estimation method based on speed constraint is constructed, so that accurate modeling of a flow density nonlinear relation is realized.

The invention discloses an automatic driving scene risk assessment method fusing a diffusion model and a random forest, and belongs to the technical field of automatic driving control. The problems of early recognition and early warning of potential risks of automatic driving are solved. A diffusion model is improved on the basis of a traffic scene state, a condition variable is introduced to obtain a condition generation distribution model, and a traffic scene sample data set is generated; setting the minimum collision time and the minimum time headway as key risk indexes, and generating a risk label for each generated traffic scene sample; constructing a continuous risk quantitative index meeting a functional safety standard as exposure degree and severity, and constructing a training data set; a random forest model is adopted to construct a risk assessment model, the training data set is utilized to train the risk assessment model, the training target is to learn the mapping relation between the exposure degree and severity and the risk label, and the trained risk assessment model is obtained; and evaluating the performance of the risk evaluation model by adopting a multi-dimensional evaluation index system to realize automatic driving scene risk evaluation.

The application belongs to the technical field of intelligent transportation, and particularly relates to a highway traffic state discrimination method based on vehicle-level macro and micro evaluation. First, millimeter wave radar data of a target section in a target period is acquired, and the millimeter wave radar data is preprocessed. Then, evaluation indexes are calculated according to the millimeter wave radar data, including two types of indexes, i.e., micro and macro indexes. The correlation between the indexes is tested, and some indexes with strong correlation are removed, so as to obtain micro indexes including vehicle yaw angle variance, lane changing behavior frequency, vehicle head time interval variance, acceleration variance and aggressive driving behavior variation index variance. Macro indexes include average flow, speed variation coefficient, large vehicle mixing rate and congestion index. Finally, based on the macro and micro evaluation indexes, traffic state entropy values of each section in the target period of the target section are calculated, and the traffic state is classified according to the traffic state entropy values. The method proposes the aggressive driving behavior variation index for measuring the risk degree of vehicles caused by sudden acceleration, deceleration or too fast driving speed, so as to more carefully reflect the traffic state from the micro aspect, and simultaneously consider the mutual influence between the sections to accurately quantify the traffic state.

The application discloses a kind of control methods of multiple vehicles into CAV special lane under network environment, comprising:1, calculate influence interval and judge whether the headway of adjacent target CAV car of second lane is less than influence interval;2, obtain the speed of adjacent target CAV car at initial time, calculate the speed of adjacent target CAV car at lane-changing stable time;3, calculate the speed index of CAV car into special lane;4, calculate the headway of adjacent target CAV car and its preceding vehicle at initial time and lane-changing stable time respectively;5, calculate the safety index of CAV car into special lane;6, calculate the comprehensive index of CAV car into special lane;7, take the maximum comprehensive index strategy, output lane-changing decision.The application can provide optimal merging decision through information interaction between network vehicles and network vehicles under network environment when multiple adjacent CAV cars simultaneously issue merging demand into special lane, thereby improving the efficiency of CAV car merging into special lane and improving road traffic capacity.

The application discloses a mountain road automatic driving car rear vehicle safety detection method and system, and belongs to the technical field of vehicle active safety detection. The application collects the position, speed and acceleration of a driver following an automatic driving car at each moment in a no-lane-changing overtaking scene, and then extracts eight features including maximum speed, minimum speed, speed average, speed standard deviation, acceleration, deceleration, distance and headway. The driver is divided into three types of aggressive, conservative and intermediate by using principal component analysis and K-means clustering algorithm. A following safety threshold model is constructed for each type of driving style. The following data of the rear vehicle is collected in real time, the features are extracted and the driving style is matched, the safety threshold of the corresponding style is called to complete safety detection, and a warning is given if the threshold is exceeded. The application can effectively avoid the collision risk of the automatic driving car rear vehicle at the mountain curve, and provides technical support for the driving safety of the mountain road automatic driving scene.

The present invention relates to the field of urban road traffic technology, and discloses a generalized estimation method and system for saturated headway at urban road signal intersections, comprising obtaining license plate recognition data and extracting an intersection signal cycle; matching the license plate recognition data with floating vehicle data, extracting saturated headway samples of vehicles queuing at the intersection under all intersection signal cycles; screening out factors influencing the saturated headway; using the saturated headway samples as input, and constructing a probability estimation model of the saturated headway and various saturated headway influencing factors based on the NGBoost model according to the saturated headway influencing factors; using each saturated headway influencing factor as input to the probability estimation model, and outputting a generalized estimation result of the saturated headway. The beneficial effect of the present invention is that it can generalize the saturated headway based on existing and easily obtainable license plate recognition data and floating vehicle data.

The invention discloses a double-layer cooperative control method for improving the traffic capacity of an uphill road section in a network connection environment, which is suitable for a road basic road section containing a ramp bottleneck, and comprises the following steps: 1, obtaining a current traffic flow state, calculating an equivalent time headway and mixed traffic flow basic diagram parameters, and predicting a traffic state of a next time period; 2, calculating the entrance sending capacity of the upslope bottleneck road section and the supply capacity and falling capacity of the downstream straight road section, and determining the upper flow limit of the upslope bottleneck road section; and 3, when the regional transfer total demand flow exceeds the regional transfer total supply and the regional transfer total supply flow is greater than the falling capacity, judging that the capacity falling is about to occur, activating the double-layer cooperative control framework, and performing rolling optimization to obtain a variable speed limit value and a special road layout scheme, and issuing and implementing the variable speed limit value and the special road layout scheme. According to the method, modeling and management and control requirements of a ramp capacity falling mechanism are considered, generation of capacity falling can be inhibited, the bottleneck queuing length of the ramp is reduced, and the traffic capacity and the traffic efficiency of the basic road section of the road are improved.

The invention discloses a traffic flow parameter real-time sensing method based on air-ground cooperative sensing, which can provide an accurate geometric alignment basis for fusion of air-based hovering video data and ground-based radar data by analyzing the tracking life cycle of a vehicle target to adaptively divide sensing areas and optimize calibration parameters. On the basis, through cross-modal feature matching and data association, the wide coverage advantage of an air-based video and the accurate distance measurement advantage of a ground-based radar are effectively converged, so that large-range vehicle accurate detection and tracking are realized, and the detection accuracy is improved. Furthermore, various key traffic flow parameters including queuing length, section traffic flow, interval vehicle number, vehicle existence and time headway are synchronously calculated and output.

An attention calling system includes a time headway (THW) acquisition unit that repeatedly acquires THW at predetermined time intervals, the THW being a value obtained by dividing an inter-vehicle distance between own vehicle and a preceding vehicle by vehicle speed of the own vehicle, an identification unit that identifies a type of the preceding vehicle, and a notification unit that outputs to an occupant of the own vehicle an approach alarm that notifies an approach to the preceding vehicle when the THW becomes a first threshold or less. The notification unit outputs the approach alarm by using the first threshold that takes different values depending on the type of the preceding vehicle, and / or by setting a saliency level that is a degree of saliency of the approach alarm to a first level that has different magnitudes depending on the type of the preceding vehicle.

The application provides a traffic signal optimization method based on parameter calibration and improved Webster timing method, the method comprising: arranging detection coils in each lane in front of a stop line at an intersection, collecting vehicle passing information and storing locally; based on traffic flow theory analysis, calibrating saturated headway and calculating saturated flow of each lane according to the vehicle passing information, reducing the influence of oversaturated traffic flow on signal timing; for phase overlap and stage secondary release, the concept of phase residual flow ratio is proposed to solve the adaptation problem of Webster timing method under the signal timing requirements of the phase overlap and the stage secondary release; in the signal timing self-optimization link, if the signal timing meets the minimum green constraint relationship, the signal timing ends; if the signal timing does not meet the minimum green constraint relationship, the minimum green adjustment process is entered; when real-time optimization is performed, the updated signal timing is executed in the next cycle and the vehicle passing information is recorded again; when timing optimization is performed, adjustment is performed within a certain fixed time period based on historical data. The problems of unreasonable setting of basic parameters and limited use of traditional timing methods in actual application scenarios are solved, and a fast, accurate and stable traffic signal adaptive optimization system is established.

The application provides a driver behavior analysis and prediction method based on causal inference, belongs to the field of traffic engineering human factor analysis, and mainly evaluates and understands the driver behavior by constructing four types of driving index evaluation systems, including the headway, the time to collision, the speed and brake amplitude, and the eye tracking information. Then, the causal inference model of double machine learning is adopted to deeply study the driving behavior of the driver in various scenes and the difference thereof, and accurate causal estimation of the driver behavior is provided, so that the driver behavior can be further predicted. The method comprehensively analyzes the relationship between the driving behavior and the driving environment, can be close to the actual situation, improves the driving safety and the driving efficiency, and has high practical value for the fields of intelligent driving and traffic safety research.

An operation optimization control method for an intelligent connected bus based on a heterogeneous line scenario relates to an operation optimization control method for an intelligent connected bus. The present invention aims to solve the technical problem that the coexistence of current manually driven bus lines and intelligent connected bus lines causes uneven headway time of intelligent connected buses traveling on bus lanes, as well as congestion at stations and intersections. The intelligent connected bus operation hierarchical optimization control model established by the present invention outputs the optimal speed control scheme and signal priority scheme under the condition of uncertainty in the operation of manually driven buses, improves the uniformity of headway time, and reduces the number of times of entering node queues. The present invention provides a new idea for the efficient operation of intelligent connected bus companies, and is of great significance to promoting the large-scale operation of intelligent connected buses from specific simple roads to complex roads.

The present invention discloses a method and device for designing the length of an early right turn based on a traffic trajectory diagram. The method collects relevant traffic data, converts the total traffic volume obtained into equivalent standard car traffic volume, and calculates the saturated flow of the right through lane of the intersection based on the maximum headway. A Greenshields linear relationship model is established to obtain the relationship between the speed before entering the intersection entrance and the green light speed of the intersection entrance. Based on the traffic trajectory diagram, the relationship between the gathering wave and the dissipation wave is obtained, thereby calculating the traffic dissipation time of the right through lane of the intersection. The maximum number of vehicles queuing in the right turn lane during the straight red light period is calculated, and finally the maximum queue length of the right turn lane of the intersection entrance is obtained, which is the distance from the stop line to the early right turn line. The present invention effectively alleviates the waste of resources caused by vehicle congestion at intersections, alleviates the problem of tight urban vehicle resources, and effectively improves travel efficiency.

The application discloses a kind of platoon adaptive cruise control methods, comprising: step A: establish dynamic safety demand distance model, for describing the demand of intelligent networked driving platoon to safety spacing under networked environment;Combined with the speed, acceleration information of the vehicle in front of the queue inside, and the influence of different positions in the queue where the vehicle is located, introduce information transmission coefficient and position influence coefficient, establish dynamic safety demand distance strategy;Step B: improve cooperative adaptive cruise control model CACC based on dynamic safety demand distance model, update the headway strategy to the dynamic safety demand distance strategy;Introduce the delay of speed and spacing to simulate the time delay relationship between upper planning and bottom control;Step C: establish cooperative lane-changing control strategy based on adaptive spacing strategy, set relaxation spacing, recovery spacing, equilibrium spacing for different stages of vehicle forced lane-changing, adaptively change according to lane-changing stage indicator variable;Cooperative lane-changing optimization model is constructed to realize centralized control of platoon.

The invention belongs to the technical field of intelligent traffic and automatic driving control, and discloses a vehicle formation time headway dynamic control method, device and equipment and a medium, and the method comprises the steps: constructing a traffic time sequence feature set and a state label according to multi-source traffic data; an MAP model is adopted to communicate the time sequence feature set and the state label, and an output result is obtained and comprises vehicle arrival features, a state transition probability and a traffic state proportion; according to the member state of the automatic driving vehicle formation, dynamically adjusting the time headway by taking an output result as a constraint and adopting a vehicle longitudinal dynamics model; when the time headway is dynamically adjusted, the MPC is adopted to perform rolling optimization according to the target function, and an optimal control instruction sequence is obtained; and performing time headway dynamic control on the automatic driving vehicle formation through the optimal control instruction sequence. The method has the beneficial effect that the dynamic control efficiency and adaptability of the time headway of the vehicle formation are improved.

The invention provides a control method, device and equipment, a storage medium and a computer program product, and is applied to a control system.The method comprises the steps that under the condition that a first vehicle enters a first area, first time headway information when the first vehicle enters a second area is determined; determining a plurality of pieces of second time headway information of a plurality of second vehicles in the third area; under the condition that the first time headway information is greater than the multiple pieces of second time headway information, searching a target front vehicle and a target rear vehicle from multiple second vehicles based on the multiple pieces of second time headway information; adjusting at least one piece of speed information of the target front vehicle and / or the target rear vehicle to ensure that a first gap between the target front vehicle and the target rear vehicle meets a preset range; determining first speed information based on the first gap, and controlling the first vehicle to run by inserting the first speed information into the first gap; and the traffic efficiency in different traffic environments is improved.

The invention relates to vehicle route recommendations for avoiding collisions in hot lane change scenarios. A vehicle system includes a control module configured to determine one or more reliability scores for each of a plurality of segments of a lane, modify the one or more reliability scores for each segment based on a number of vehicles that change the lane and a time taken by the vehicle to change the lane, a road segment having the highest reliability score is selected, a lane merge feasibility is determined based on an expected time headway value, a vehicle lane change ratio of the vehicle, and a time gap for changing the lane, a modified lane merge feasibility is determined based on the number of vehicles moving laterally into and out of the lane, and a lane merge feasibility is determined based on the modified lane merge feasibility. And identifying the selected road segment as a portion to avoid when merged into the lane based on the modified lane merging feasibility. Other exemplary vehicle systems and control methods are also disclosed.

This invention discloses a dual-linkage variable speed limit and connected vehicle collaborative management strategy for mixed traffic flows. Taking the optimal operation and management of mixed flows involving intelligent connected vehicles as its starting point, it constructs a hierarchical start-trigger model theory and control strategy based on speed fluctuation theory. It proposes a motion wave elimination (JAD) strategy for variable speed control under the influence of real-time fluctuation parameters of intelligent connected vehicles. Furthermore, it proposes a collaborative control strategy based on a collaborative hybrid control algorithm to reverse the shock wave formation process, mitigate the increase in headway caused by traffic flow start-stop waves, and optimize global traffic efficiency and safety. Based on this, it achieves optimal spatiotemporal effect output based on a genetic algorithm learning framework, providing a more scientific reference scheme for traffic management under mixed traffic flows.

This invention provides a method for calculating the recommended clearance length between tunnels and interchange exits, comprising the following steps: constructing a VISSIM simulation model; obtaining the headway, speed, and instantaneous traffic flow density at different cross-sections in the model, and fitting the models to obtain headway models and speed distribution models for different road segments, constructing speed and density models and critical intercalation gap models; constructing a safe lane-changing probability model using the differential method; solving the safe lane-changing probability model based on the Monte Carlo simulation method according to various uncertainties, obtaining the target lane-changing success rate under different traffic volumes, large vehicle ratios, and clearance lengths, and providing a recommended clearance length value. This invention uses a deterministic approach to calculate the problem of large clearance values ​​required under traffic capacity, and utilizes a reliability design method to integrate traffic characteristics, driver characteristics, and traffic safety requirements of small clearance road segments, proposing a method for calculating the recommended clearance length value.

A system for directly and indirectly controlling a mixed automated vehicle receives a traffic state of a group of mixed automated vehicles traveling in the same direction, wherein the group of mixed automated vehicles includes a controlled vehicle willing to participate in a platoon formation and at least one uncontrolled vehicle, and wherein the traffic state indicates a state of each vehicle in the group, submits the traffic state to a parameterized function to produce a target headway, the parameterized function trained to convert the traffic state to a target headway for the mixed automated vehicle, and submits the target headway to a headway-based model to produce a target speed, the model configured to map the target headway to a target speed for the mixed automated vehicle. The system determines a control command based on one or a combination of the target headway and the target speed and sends the control command to the controlled vehicle.

This invention discloses a method for controlling the self-organized merging of intelligent connected vehicles into a queue, comprising: acquiring the speed and position of all target queue vehicles and merging vehicles within the communication distance; calculating the headway between each merging vehicle and the merging vehicle in front of it; determining whether each merging vehicle and the merging vehicle in front of it belong to the same local merging queue; counting the number of different local merging queues; determining whether the number of local merging queues is greater than the set maximum number of local merging queues; selecting the candidate merging position with the smallest distance as the target merging position; and considering the safety space and acceleration constraints of the local merging queues and the target queue, controlling the vehicles in the local merging queues to merge as a whole into the target merging position in the target queue. This method can iteratively organize merging vehicles into local merging queues based on their speed and spacing, and control the local merging queues to merge as a whole into the target queue, reducing the computational complexity of multi-vehicle merging queue problems.

This application provides a method and electronic device for constructing high-risk scenarios in following and side-by-side vehicle insertion scenarios. The method includes: acquiring scenario elements to be tested in the following and side-by-side vehicle insertion scenarios; using Spearman correlation test to select scenario elements with strong correlation to driving risk from the scenario elements to be tested, and constructing a dangerous scenario; calculating the headway between the selected vehicle and the vehicle in front in the dangerous scenario; simulating the motion state of the vehicle, calculating the relative distance between the vehicle and the vehicle in front in real time, and determining the relative distance between the two vehicles when both the vehicle and the vehicle in front brake to a stop when their speeds are both 0; and constructing a high-risk scenario based on the headway between the vehicle and the vehicle in front and the relative distance between the two vehicles when they brake to a stop. This solution helps improve the efficiency of autonomous driving system detection and reduce detection costs.

A server accesses vehicle data from each connected vehicle (CV) of a subset of a plurality of CVs on a roadway portion, the vehicle data comprising at least one of: a position, a velocity, or a headway. The server generates, based on the accessed vehicle data, a long-term shared world model. The server generates, using the long-term shared world model, a data structure representing predicted future velocities on the roadway portion by position and time by applying a traffic flow model to the long-term shared world model. The server transmits, to a connected autonomous vehicle (CAV), a control signal for controlling operation of the CAV based on the generated data structure.

The application provides a single-line and multi-line bus string vehicle optimization control method, which firstly carries out single-line bus string vehicle prevention and treatment, carries out intersection signal adjustment by analyzing the relationship between the bus arrival intersection time and the signal execution state, and takes the bus state of the single road section bus string vehicle, signal control and road parameters as system inputs, selects the decision type through the calculation and analysis of the headway offset between stations, designs an edge computing-based decision method to solve the decision problems of optimization measure selection and optimization measure combination, and then carries out multi-road section bus string vehicle prevention and treatment, adopts a multi-road section string vehicle prevention and treatment decision method, and designs multi-road section bus string vehicle prevention and treatment optimization targets from two aspects of bus string vehicle prevention and treatment inter-station road section quantity control and road per capita delay control through historical case retrieval, similar case extraction, similar case correction and case storage.

The present application belongs to the technical field of road facility equipment layout in traffic engineering, more particularly, relates to a method for monitoring the traffic state of a basic road section of an expressway. The method comprises the following steps: firstly, randomly setting detectors at different intervals on the expressway to obtain original vehicle trajectory data; then, pre-processing the original data; and then, calculating the running speed, speed difference, headway, headway time and vehicle density of each vehicle at each time in the detection interval based on the original data. Based on the fuzzy C-means clustering traffic state clustering analysis, the interval traffic data collected in 1 min is clustered and divided into traffic states, and each category is given a label to provide a training data set for the state discrimination algorithm. The traffic state discrimination model is trained, the precision rate is selected as the machine learning evaluation index, and the traffic state discrimination model based on the random forest is selected. The determination of the detector layout interval based on the traffic state transition.

The invention belongs to the technical field of intelligent traffic, and discloses an ETC data-based abnormal following vehicle discrimination method, which comprises the following steps of S1, formulating lane division labels, and selecting traffic flow characteristics for dividing lanes; s2, using a lane classification model based on a random forest classifier to distinguish the number of a lane where the vehicle passes through the ETC portal; s3, constructing a model including front vehicle disturbance, rear vehicle reaction, brake avoidance and collision judgment, wherein the model is used for reflecting the uncertainty of the highway time headway risk; s4, quantifying collision risks of different time headways through Monte Carlo simulation; and S5, in combination with a parameter-adaptive three-parameter Boolean distribution model, constructing a safety threshold setting method adaptive to a real-time traffic flow state for judging an abnormal following vehicle. According to the invention, abnormal following vehicles can be accurately identified from complex ETC data, and reliable support is provided for traffic management.

The application provides a kind of urban mixed traffic flow basic graph model analysis method and system under Internet of Vehicles environment, belongs to urban traffic management and networked automatic driving car application technical field, according to the type of following vehicle, different car-following model is selected, and nine different headway types of front and rear vehicle types are set;Based on Markov chain model, the transfer probability corresponding to each following mode in urban mixed traffic flow is calculated, the basic graph model of mixed traffic flow under single lane is deduced, the influence of networked automatic driving vehicle penetration rate, bus penetration rate and networked automatic driving vehicle queue intensity on road capacity is considered;Under the influence of random headway, the influence of different influencing factors on the flow stability of mixed traffic flow is analyzed.The application discloses the road capacity under different traffic density and vehicle type distribution conditions;Under the existing basic graph model framework, the influence of random headway on traffic flow stability is discussed.

A computer-implemented method of determining control signals for controlling an autonomous vehicle to implement a slowdown manoeuvre, comprising: detecting an obstacle at a distance ahead of the autonomous vehicle; comparing the distance with a threshold value and implementing a slowdown manoeuvre in dependence on the comparison, the slowdown manoeuvre selected from: a first slowdown manoeuvre carried out by a kinematic function, which is a time derivative of acceleration, in which a constraint optimisation has been applied to optimise a cost function of the slowdown manoeuvre subject to a set of hard constraints that require a final acceleration, speed and position to satisfy respective acceleration, speed and position targets, given an initial speed and acceleration of the vehicle, and impose a jerk magnitude upper limit; and a second slowdown manoeuvre implemented in an adaptive cruise control mode which aims to reach a target headway between the autonomous vehicle and the obstacle.

The application belongs to the technical field of intelligent transportation, and particularly relates to a smart rail route time-sharing multiplexing method based on interoperation, which comprises the following steps: acquiring vehicle data through a roadside device, wherein the vehicle data comprises position coordinates; processing the vehicle data based on a trajectory smoothing method to obtain a fitted vehicle trajectory; processing multiple traffic indexes based on a fuzzy evaluation algorithm, and evaluating a traffic state level according to the weight of the multiple traffic indexes and the membership degree of the corresponding index in the traffic state level; when the traffic is in a lock state level, maintaining the original state for driving; when the traffic is in a non-lock state, obtaining the length of the vehicle lane changing and occupying the smart rail and the time of occupying the smart rail according to the protection headway, the safe headway, the controlled networked vehicle ruling range duration and the average driving speed of the vehicle with priority road right.

The invention discloses a travel time reliability-based CAV special lane layout optimization method. The method comprises the following steps of: constructing an expanded road network comprising a conventional road section and a virtual road section; calculating the traffic capacity of the conventional road section and the virtual road section based on the expanded road network according to the vehicle following mode and the corresponding safe time headway; establishing a mixed traffic flow distribution model based on the traffic capacity, and solving the mixed traffic flow distribution model to obtain road network balanced flow distribution; obtaining a travel time sample set of the road sections and the paths through Monte Carlo random sampling according to the balanced flow distribution; calculating a road network buffer time index based on the travel time sample set, wherein the buffer time index represents road network travel time reliability; the layout number of the CAV special lanes on the virtual road section is taken as a decision variable, the minimum road network total travel time and the minimum road network total buffer time index are taken as optimization objectives, a CAV special lane layout optimization model is established, and the model is used for outputting a CAV special lane layout optimization scheme.