26 results about "Time headway" patented technology

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 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 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 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 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 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.

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 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.

The application relates to a road section vehicle group dissolving method based on real-time vehicle speed regulation, which comprises the following steps: S1, acquiring real-time traffic information of a road section; S2, when the headway between a host vehicle and a front vehicle in the same lane is less than a safe following headway and the headways between the host vehicle and front vehicles in adjacent lanes are both less than a safe lane-changing headway, the front vehicle in the same lane and the front vehicles in the adjacent lanes form a vehicle group; S3, judging the risks of vehicles in the vehicle group, and the vehicle with the maximum risk value R being the risk source of the vehicle group; S4, regulating the vehicle speed of the risk source vehicle of the vehicle group; and S5, when the host vehicle satisfies the condition that the headway with the front vehicle in the same lane is greater than or the headway with the front vehicle in the adjacent lane is greater than, the original vehicle group is dissolved. The application dissolves the vehicle group and reduces the driving risk of the road section by regulating the real-time vehicle speed of the risk source vehicle.

The invention discloses an automatic driving vehicle full-speed-domain lane changing method based on longitudinal and transverse coupling, which is suitable for acceleration and deceleration lane changing scenes of common roads and ramps and comprises the following steps: 1, collecting states of a vehicle and surrounding vehicles in real time, and constructing virtual boundary vehicles in a sparse scene; 2, constructing a longitudinal merging feasible region according to a comfortable and safe time headway, introducing a lane changing urgency factor into a ramp scene to dynamically compress a safety boundary, and determining an optimal merging point according to a minimum longitudinal error; 3, calculating a longitudinal acceleration by using a nonlinear longitudinal controller; 4, generating a transverse reference point in combination with the longitudinal merging progress; calculating transverse acceleration by adopting an improved social force model; 5, introducing a tire friction circle theory to carry out dynamic coupling correction, and dynamically cutting the transverse acceleration according to a longitudinal priority principle; and 6, repeating the steps until a lane changing target is achieved. According to the method, full-speed-range lane changing control can be unified, so that the driving stability and safety can be effectively improved.

Disclosed are a decision and control method, apparatus and device for automatic overtaking and lane changing, and a storage medium, relating to the technical field of autonomous driving planning control. The method comprises: upon detection that there is a vehicle having the vehicle speed less than a preset low speed threshold in a lane of an ego vehicle, on the basis of a set speed of the ego vehicle, the speed of a preceding vehicle, and time headway, obtaining the necessity of overtaking and lane changing; obtaining traffic efficiency on the basis of the traffic flow and the average vehicle speed in a destination lane for lane changing; acquiring an overtaking and lane changing suppression condition; if an automatic lane changing control condition is satisfied, triggering an overtaking and lane changing request on the basis of the necessity, the traffic efficiency, and the overtaking and lane changing suppression condition; and if an overtaking and lane changing termination condition is satisfied, canceling overtaking and lane changing.

This invention discloses a scenario generation method and system for testing and evaluating autonomous driving cold chain logistics vehicles. The method includes the following steps: collecting a multidimensional dataset of driving scenarios for cold chain logistics vehicles and filtering lane-changing scenarios; calculating a comprehensive cognitive risk score for each lane-changing scenario based on the headway, longitudinally corrected collision time, and lateral collision time; obtaining the risk level of each lane-changing scenario based on clustering classification; and extracting trajectory sequences of high-risk lane-changing scenarios; constructing a physical model of the cold chain logistics vehicle, calculating its acceleration, and generating the driving trajectory of the lane-changing scenario between the cold chain logistics vehicle and the target vehicle; inputting the conditional vector and the trajectory sequence of the high-risk lane-changing scenario into a conditional variational autoencoder to obtain a reconstructed trajectory sequence; and constructing an autonomous driving test scenario library based on the characteristics of cold chain logistics vehicles. This invention can achieve accurate generation of high-coverage autonomous driving test scenarios tailored to the characteristics of cold chain logistics vehicles.

The application discloses a kind of city intelligent public transport scheduling method and system based on Internet of Things, including the traffic influence coefficient of gathering city multi-source traffic information calculation, construct city public transport network time-varying diagram carries out flow propagation prediction each future state feature of public transport station, calculates the dynamic adjustment vehicle station time of vehicle head time interval, calculates the support demand index to determine the station to be supported, the support weighted demand index of corresponding neighborhood station of the station to be supported is selected to support vehicle.The method not only can improve the efficiency and accuracy of city public transport scheduling, but also has good interpretability, can be directly applied in city intelligent public transport scheduling system.

The invention discloses an automatic mining method and system for an automatic driving risk scene based on optimization search, and relates to the technical field of automatic driving simulation testing, and the method comprises the steps: obtaining a target automatic driving simulation scene template and a parameter space constraint; generating a candidate parameter group set by adopting an optimization search algorithm; instantiating the scene and driving a simulator to execute simulation; collecting motion state data, and calculating a collision time sequence and a time headway sequence; generating a comprehensive risk score through normalization and weighted combination processing; iteratively updating the parameter group by using the score feedback guide algorithm; high-risk scenes are screened and stored in a risk scene library, data in the library are output when termination conditions are met, and the system comprises a scene template module, a parameter management module and the like. Through an automatic closed-loop process and a comprehensive risk assessment mechanism, a multi-modal distributed high-risk scene is efficiently and comprehensively mined, the limitation of a single optimal solution is avoided, and the integrity and efficiency of an automatic driving simulation test are improved.

The invention relates to the technical field of intelligent traffic, discloses a bus balanced operation control method based on Internet of Vehicles coverage, and aims to solve the problem that existing bus scheduling depends on a static timetable and lacks dynamic prediction and collaborative optimization capabilities, resulting in bunching and service gaps. Comprising the steps of collecting multi-source heterogeneous data and constructing a dynamic space-time diagram; fusing the graph convolutional network and the long-short-term memory network to predict a future line state; based on a multi-agent reinforcement learning framework, taking time headway balance, energy consumption and punctuality rate as optimization targets, and generating a cooperative control strategy including cruise speed, dwell time and green wave request; and the instruction is executed through vehicle-road cooperation. The system comprises a data acquisition module, a space-time fusion module, a state prediction module, a collaborative decision-making module and an instruction execution module. The crossing of bus operation from passive response to active prediction, single vehicle control to vehicle team cooperation and single speed regulation to multi-dimensional regulation is realized, and the bus service balance, the energy efficiency and the punctuality rate are remarkably improved.

The application discloses a kind of based on the space-time calculation method and system of fusion of radar vision.The video image data is fused with radar detection data, obtains the relative space-time information of each vehicle relative to intersection, obtains the absolute space-time information of all vehicles of intersection according to relative space-time information;According to the absolute space-time information of each vehicle, the time mark is carried out to each vehicle passing through stop line in stage time, obtains the marking time of each vehicle;According to the absolute space-time information of each vehicle, the saturation head time distance of queuing vehicle team is obtained;According to saturation head time distance, the time extension is carried out to marking time, obtains the extension time period of each vehicle;According to the absolute space-time information of each vehicle, the start-up sub-loss time period and the clearing sub-loss time period in stage time are obtained;The extension time period of each vehicle in stage time, start-up sub-loss time period and clearing sub-loss time period are removed, and the emptying time period of each lane is obtained.