42 results about "Headway" patented technology

Methods and apparatus for extracting headway information from LiDAR trajectory data received from a roadside LiDAR sensor system. In an embodiment, a LiDAR data processor pre-processes raw roadside LiDAR sensor data, extracts headway data from the pre-processed raw roadside LiDAR sensor data and extracts critical headway data from the extracted headway data. In some implementations, the pre-processing may include filtering background information from the raw roadside LiDAR sensor data, clustering objects in the filtered LiDAR sensor data, and classifying the clustered objects, wherein the clustered objects represent road users. In addition, in some embodiments the LiDAR data processor may track movement of the clustered objects in real-time to extract trajectory and speed data, and then map the extracted trajectory and speed data such that the clustered objects are georeferenced.

Autonomous vehicle systems provide improved safety in comparison to human driven vehicles by reducing crashes. However, in future scenarios, the autonomous vehicles have to share the road with human-driven vehicles. But human drivers make decision errors during maneuvers which lead to crashes. Autonomous vehicles therefore have to predict human-initiated safe and unsafe maneuvers to react appropriately for the safety and comfort of its passengers. The present disclosure addresses unresolved problems of the conventional autonomous vehicle systems by providing a stacked autoencoder based singular deep neural network that uses sensor data to predict if any vehicle within its field of view would perform safe or unsafe lane maneuvers into its headway or into any other vehicle's headway. The method of the present disclosure also helps surrounding vehicles to know intention of a target vehicle with the help of vehicle-to-vehicle communication systems in a road driving environment.

The invention provides a traffic data visualization method and system based on the Internet of Things, and relates to the technical field of traffic, and the method comprises the steps: collecting the traffic data of a target intersection based on the Internet of Things; image segmentation processing is carried out on the traffic data, an image segmentation result is obtained, and the image segmentation result comprises a plurality of vehicle images; according to the image segmentation result, analyzing and obtaining vehicle number data of the target intersection, and according to the vehicle data number, analyzing and obtaining first congestion degree information of the target intersection; according to an image segmentation result, identifying and obtaining a plurality of vehicle head distances of a plurality of vehicles in the target intersection, and calculating and obtaining second congestion degree information; and based on the first congestion degree information and the second congestion degree information, carrying out congestion identification and classification on the target intersection to obtain a comprehensive congestion degree and corresponding congestion identification information, and carrying out visual identification on the target intersection. According to the invention, the technical effect of traffic data visualization processing is achieved.

The invention discloses a short-time traffic jam prediction method fusing FCM clustering and GA-Bi-GRU, relates to the technical field of traffic safety, and aims to solve the problem of low traffic jam prediction accuracy due to the fact that time sequence characteristics of traffic data are not fully considered in the prior art. And then, establishing a traffic flow prediction model, and inputting predicted traffic flow parameters into an FCM model to realize traffic congestion level prediction. According to the prediction model, forward and backward GRU time recurrent neural networks are designed on the basis of a gating recurrent unit (GRU) so as to fully mine data features, and in order to further capture the influence of different time steps, a time attention mechanism is introduced into the model, the time features of data are deeply mined, and optimization is carried out in combination with a genetic algorithm (GA).

The present invention belongs to the field of safe driving of intelligent vehicles, and discloses an automatic emergency obstacle avoidance control method (Consensus Behavior Migration Algorithm, CBMA) for unmanned vehicles, which includes the following steps: first, judging whether a collision can be avoided by braking in the lane; if it is judged that the self-vehicle and the front vehicle can avoid collision, automatic braking is adopted; if it is judged that the self-vehicle cannot avoid collision with the front vehicle after braking, the critical headway of the self-vehicle to the obstacle vehicle in front is adopted, and the front collision warning coefficient is calculated to make an obstacle avoidance strategy, and the self-vehicle and the surrounding vehicles are circularly clustered, and the consensus behavior migration algorithm is introduced to calculate the fitness function based on the planned safe obstacle avoidance trajectory point, and automatic emergency steering is performed to avoid obstacles. The CBMA algorithm solves the problem of mobile trajectory point planning for automatic emergency obstacle avoidance of unmanned vehicles, and reduces the risk of collision with the front vehicle.

The invention provides a motorcade active-disturbance-rejection control method and system with a variable time interval strategy. The method comprises the following steps: acquiring state information of each vehicle in a vehicle queue system; the leader vehicle and the following vehicle carry out information transmission; under the condition of considering external disturbance, a vehicle dynamics model referring to the Newton second law is established, and a variable time interval strategy is constructed; compensating unmodeled dynamic and external disturbances in the vehicle dynamics model by using a time-varying gain expansion state observer in an active disturbance rejection control technology, and constructing a virtual controller and an actual controller based on a backstepping method; and according to the constructed virtual controller and the actual controller, realizing position and speed synchronization of the following vehicle and the leading vehicle in the queue under the condition of considering unmodeled dynamics and external disturbance. According to the method, the non-smooth problem at the saturation threshold is solved, the fluctuation influence can be effectively reduced, the safety is guaranteed, and the passing efficiency is improved; and the problem of gain attenuation is solved while the high gain characteristic is reserved.

The invention discloses an urban rail transit peak period train departure interval optimization method based on multi-line cooperative scheduling, and relates to the technical field of rail transit operation management. The method comprises the following steps: firstly, constructing a network topology containing key transfer stations and a passenger flow OD matrix; establishing a passenger travel space-time network model to accurately describe a transfer waiting process; further constructing an optimization model with the goal of minimizing the total cost of the system, wherein the model comprehensively considers the passenger waiting time, the in-vehicle crowding degree, the operation cost and the transfer collaboration degree; and finally, designing a mixed intelligent algorithm combining Lagrangian relaxation and a meta-heuristic algorithm for solving, and generating a multi-line cooperative departure interval scheme. According to the method, the limitation of single-line independent scheduling is broken through, resource allocation can be optimized from the overall perspective of a line network, the total travel time of passengers and transfer station congestion are effectively reduced, and the overall transportation efficiency and service quality of a rail transit system in peak periods are remarkably improved.

The present invention relates to an apparatus and method for predicting traffic information. The traffic information prediction apparatus can include a data calculation device for deriving inter-vehicle distance, headway, and vehicle density using a plurality of sensors installed on a vehicle, and a prediction device for deriving driving speed data corresponding to the vehicle density and predicting traffic information.

A device may obtain video data associated with a driving event involving a first vehicle. The device may determine a vanishing point associated with the video data and may construct a cone of impact of the first vehicle based on the vanishing point. The device may detect a second vehicle within the cone of impact and may analyze the subset of video frames to determine a distance between the first vehicle and the second vehicle. The device may determine a speed of the first vehicle during a time period associated with a subset of video frames. The device may determine a headway score, representative of a severity associated with the first vehicle being within a proximity threshold of the second vehicle during the time period, based on the distance and the speed. The device may determine an occurrence of a tailgating event based on the headway score.

Described herein is a rail-based system (1000) for communication between a plurality of railway vehicles. The system (1000) comprises a contact (100) adapted for providing an electrical connection between at least one of the plurality of railway vehicles (2000) and at least one rail track (3000); a transceiver (102) associated with at least one of the plurality of railway vehicles (2000). The transceiver (2000) is adapted to transmit and / or receive an electrical signal by way of the contact (100) from the at least one of the plurality of railway vehicles (2000) to and / or from the at least one rail track (3000). The electrical signal is transmitted and / or received over the at least one rail track (3000) to communicate between the plurality of railway vehicles (2000). The transceiver (102) is adapted to adjust a power output of the electrical signal to determine a distance between the plurality of railway vehicles (2000) and the power output of the electrical signal is derived based on a function of one or both of a headway, and / or power input.

With respect to an adaptive cruise control method for autonomously adapting the speed of an ego vehicle (300) to maintain a target headway, headway being distance from the ego vehicle to a forward vehicle (302), the ego vehicle equipped with a perception system (100) for measuring a current headway and a current speed and acceleration of the forward vehicle relative to ego vehicle, the method may comprise: in response to detecting that the current headway is below the target headway, determining and implementing a deceleration strategy for increasing to the target headway; wherein the deceleration strategy is determined so as to selectively optimize for comfort in dependence on a predicted headway, the predicted headway computed for a future time instant based on the current speed and acceleration of the forward vehicle relative to the ego vehicle.

The invention relates to an adaptive cruise control system and method. A vehicle longitudinal movement control device for controlling longitudinal movement of a vehicle. A vehicle longitudinal motion control apparatus includes at least one sensor configured to sense longitudinal motion of another vehicle in front of the vehicle, and a controller having at least one control module in communication with the at least one sensor. The at least one control module is configured to determine longitudinal motion characteristics of the two vehicles, and a headway between the first vehicle and the second vehicle. The at least one control module is further configured to apply a coasting maneuver to the vehicle based on the determined distance-at-head to reduce the distance between the vehicles and improve the energy efficiency of the vehicle.

A smart light truck path planning method includes: building multiple road obstacle avoidance scenarios for light trucks, obtaining the coordinates of the starting point, end point, and obstacle locations in the road obstacle avoidance scenarios; introducing a target distance evaluation function, a headway evaluation function, and a curvature evaluation function into a dynamic window algorithm to obtain a primary-improved dynamic window algorithm; dynamically adjusting the weights of the primary-improved dynamic window algorithm using a global adaptive weight value adjustment strategy to obtain a secondary-improved dynamic window algorithm; optimizing the path variance in the secondary-improved dynamic window algorithm using an improved artificial lemming optimization algorithm to obtain a tertiary-improved dynamic window algorithm; and performing path planning for the light truck based on the coordinates of the starting point, end point, and obstacle locations using the tertiary-improved dynamic window algorithm to obtain an obstacle avoidance path. The present invention can improve the smoothness of the planned trajectory and enhance path planning performance.

A vehicle longitudinal motion control apparatus for controlling a longitudinal motion of a vehicle. The vehicle longitudinal motion control apparatus includes at least one sensor configured to sense a longitudinal motion of another vehicle which precedes the vehicle and a controller having at least one control module in communication with the at least one sensor. The at least one control module is configured to determine a longitudinal motion characteristic of both vehicles and a headway distance between the first vehicle and the second vehicle. The at least one control module is further configured to apply a coasting maneuver to the vehicle based upon the determined headway distance to reduce the distance between the vehicles and increase energy efficiency of the vehicle.

A vehicle includes at least one sensor in communication with a controller. The controller includes a ranging module configured to determine a headway between the vehicle and a lead vehicle traveling ahead of the vehicle. The controller also includes a headway learning module and an adaptive cruise control module. The headway learning module is configured to learn a set of personalized headways while the adaptive cruise control module is not engaged. The adaptive cruise control module is configured to maintain the vehicle at a personalized headway from the lead vehicle when the active cruise control module is engaged.

A cruise control method includes determining that a host vehicle is traveling downhill and determining an axle torque command to maintain a predetermined headway distance from the host vehicle to a lead vehicle while the host vehicle is traveling downhill. The method further includes determining that a brake of the host vehicle is actuated to provide the axle torque command previously determined and comparing the axle torque command with a brake exit threshold to determine whether the axle torque command is greater than the brake exit threshold. Further, the method includes comparing the headway distance between the host vehicle and the lead vehicle with a headway exit threshold to determine whether the headway distance between the host vehicle and the lead vehicle is greater than the headway exit threshold. Also, the method includes commanding the brake of the host vehicle to disengage to terminate braking.

A refined calibration method for a simplified car-following model based on multi-source data belongs to the field of intelligent transportation technology. In order to improve the accuracy of the simulation results of the simplified car-following model, the present invention includes obtaining road network data, geomagnetic detection equipment data, geomagnetic detection data, floating vehicle GPS Data is collected; roads are divided into different grades and roads are interrupted and merged; data is processed to remove missing or abnormal data; the average vehicle length of different types of vehicles is calibrated; the average headway between different types of vehicles at rest is calibrated through intersection surveys; the average effective vehicle length of different types of vehicles at rest is calculated; the free flow speed of different grades of roads is calibrated; the capacity of different grades of roads and the corresponding average effective vehicle length are calibrated based on processed geomagnetic detection data; and the driver reaction time coefficient of different grades of roads is calculated. This invention improves the accuracy of the simulation results of the simplified car-following model.

The application discloses a method and system for optimizing skylight and train diagram considering fluctuation demand, and relates to the technical field of skylight and train diagram optimization. The method comprises the following steps: obtaining the working state of track equipment, passenger and freight transportation demand, maintenance operation demand, passenger and freight train operation parameters, and maintenance operation cancellation cost parameters; constructing a skylight and train diagram optimization model considering fluctuation demand according to the above information; decomposing the initial model into a series of minimum cost sub-models by using a high-order term dimension reduction method and an alternating direction multiplier decomposition method; solving the minimum cost sub-models by using an improved heuristic algorithm based on an alternating direction multiplier framework, and outputting an optimal skylight scheme and train diagram under specific passenger and freight transportation demand. The application simultaneously considers passenger and freight train operation cost and maintenance operation cancellation cost, adjusts skylight length by changing the number of daily skylight maintenance operations, and flexibly matches passenger and freight transportation demand, so that the service level of trains is improved while the safety of railway transportation organization is ensured.

The invention discloses a road actual traffic capacity calculation method under an intelligent network connection vehicle formation, comprising the following steps: analyzing the intelligent network connection vehicle formation condition running in a road section, and calculating the actual traffic capacity of the road according to historical monitoring data; counting the formation number # imgabs0 # average vehicle speed vi corresponding to the vehicle formation length under different time ti in the line, the vehicle head spacing # imgabs1 # in the formation and the non-formation vehicle head spacing # imgabs2 #, and estimating the vehicle distribution # imgabs5 # of the formation length j under the given time # imgabs4 # according to the historical vehicle formation data set # imgabs3 #; grouping the historical vehicle formation data by using a density clustering OPTICS algorithm; establishing a prediction model for each group of formed clustering data, and predicting vehicle formation length distribution at given time; according to the historical vehicle formation data set # imgabs6 #, estimating the vehicle head distance under the superspeed condition; and based on the average inter-vehicle head # imgabs7 # and the average vehicle speed, calculating the actual road traffic capacity of the vehicle formation under the ultra-high-speed condition.

The present invention discloses a vehicle formation design method considering data packet loss in C‑V2X autonomous mode, belonging to the technical field of cellular vehicle networking and intelligent connected vehicles; comprising: firstly, a theoretical analysis model of data packet loss in cellular vehicle networking autonomous mode is proposed, and the relationship between the probability of successful data packet delivery and the vehicle formation vehicle spacing is modeled. Next, the influence of data packet loss on the stability of the vehicle formation queue in the C‑V2X autonomous mode is analyzed, and the threshold expression of the minimum headway time under the condition of ensuring the stability of the vehicle formation queue is derived. Finally, the present invention provides a design strategy for efficient vehicle formations, that is, by increasing the number of leading vehicles communicating with the formation member vehicles and improving the probability of successful data packet delivery, the vehicle formation can reduce the headway time and increase the road capacity.

The present invention provides a group train headway determining method and system. The method comprises: obtaining a group train in-section headway, a group train station departure interval, and a group train station arrival interval; and determining a group train headway on the basis of the maximum one of the group train in-section headway, the group train station departure interval, and the group train station arrival interval. The average headway between group cargo trains can be minimized to 3 min, and the maximum transport capacities of heavy-load train group operation control systems are improved.

A method in a vehicle, includes: providing, via an HMI, a request to be followed indication responsive to receipt of a request to be followed from a following vehicle; providing, via the HMI, a request to enter a tether operating mode; entering the tether operating mode and selecting a tether goal between the lead vehicle and the following vehicle; providing, via the HMI, a tether mode indication that indicates that the lead vehicle is in the tether operating mode; monitoring a headway between the lead vehicle and following vehicle; adjusting a preplanned trajectory calculated by a driver assistance system to facilitate the following vehicle obtaining and maintaining the tether goal; modifying driver assistance system operation based on a level at which the headway is greater than the tether goal; and generating control signals for vehicle actuators to control the lead vehicle to facilitate the following vehicle maintaining the tether goal.

The server accesses vehicle data of each CV in a subset of multiple connected vehicles (CVs) on a road section, where the vehicle data includes at least one of position, speed, and headway. The server generates a long-term shared world model based on the accessed vehicle data. The server generates a data structure that represents predicted future speeds on the road section by position and time by applying a traffic flow model to the long-term shared world model and using the long-term shared world model. The server transmits a control signal for controlling the operation of the connected autonomous vehicle (CAV) based on the generated data structure to the CAV.

A vehicle system includes a control module and a display module. The control module is configured to: identify a lane in which the vehicle should be located based on a current driving route of the vehicle, a change to the current driving route, and a real-time traffic condition to provide smooth navigation; determining lane merging feasibility based on the expected time headway value and a time slot to merge into the identified lane; determining a modified lane merging feasibility based on the lane merging feasibility and the lane speed data; and identifying portions of the identified lane for incorporation based on the modified lane merging feasibility. A display module is configured to output a visual representation of the highlighted identified portion to indicate which lane the vehicle should be in and a recommendation where it is incorporated into that lane. Other example vehicle systems and vehicle control methods are also disclosed.

The present application relates to the technical field of traffic control, and in particular to a method, system, device, and medium for detecting and evaluating highway traffic flow. The present application first utilizes video detectors, radar detectors, and coil detectors to collaboratively collect parameters such as vehicle type distribution, vehicle speed distribution, and headway spacing. Based on these data, the application then calculates a traffic flow heterogeneity index, which includes a large vehicle impact coefficient, a vehicle speed difference coefficient, and a following safety factor. Furthermore, the application analyzes the traffic flow operating status to obtain the traffic capacity attenuation characteristics, ultimately achieving an evaluation of the traffic efficiency of different road sections. This allows for a more comprehensive and accurate reflection of the operating quality of mixed traffic flows.

The invention discloses a refined calibration method of a simplified car-following model based on multi-source data, and belongs to the technical field of intelligent traffic. In order to improve the accuracy of a simulation result of a simplified car-following model, the method comprises the following steps: acquiring road network data, geomagnetic detection equipment data, geomagnetic detection data and floating car GPS data; road grades are divided, and the roads are broken and combined; carrying out data processing, and removing missing data or abnormal data; calibrating the average vehicle length of different types of vehicles; calibrating an average headstock distance when different types of vehicles are static through intersection investigation; calculating the average effective vehicle length when different types of vehicles are static; calibrating free flow velocities of different grades of roads; on the basis of the processed geomagnetic detection data, calibrating the traffic capacity of different grades of roads and the corresponding average effective vehicle length; and calculating driver response time coefficients of different grades of roads. According to the invention, the accuracy of the simulation result of the simplified car-following model is improved.

The present application relates to a kind of confluence area vehicle cooperative control method, device, electronic equipment and storage medium, its method includes: based on the preset distance demarcation confluence area from confluence point to intersection;According to the headway of adjacent vehicle, the vehicle in confluence area is grouped, and multiple passing sequences are set in group unit;Based on the time that vehicle reaches confluence point, construct cost function, and utilize the cost function to obtain the passing time cost of each passing sequence, to determine optimal passing sequence.The present application is by the vehicle queue that the vehicle with headway less than set threshold is formed, reduces vehicle passing sequence decision algorithm calculation time, improves confluence area passing efficiency.

A cruise control method includes determining that a host vehicle is traveling downhill and determining an axle torque command to maintain a predetermined headway from the host vehicle to a preceding vehicle while the host vehicle is traveling downhill. The method also includes determining that a brake of the host vehicle is actuated to provide a previously determined axle torque command, and comparing the axle torque command to a brake exit threshold to determine if the axle torque command is greater than the brake exit threshold. Further, the method includes comparing the headway between the host vehicle and the preceding vehicle to a headway exit threshold to determine if the headway between the host vehicle and the preceding vehicle is greater than the headway exit threshold. In addition, the method includes commanding a brake of the host vehicle to release to terminate braking.

The invention discloses a road actual traffic capacity calculation method under an intelligent network connection vehicle formation, and the method comprises the steps: carrying out the statistics of the formation number and average vehicle speed corresponding to the vehicle formation length at different times in a designated line according to historical monitoring data; according to the historical vehicle formation data set, estimating the vehicle distribution of the formation length at a given time and the headstock under the superspeed condition according to the average peak period and the peak period; and calculating the actual road traffic capacity of the vehicle formation under the ultra-high-speed condition based on the average vehicle head distance and the average vehicle speed.

The application discloses a kind of dynamic short-time passage regulation and control methods of vehicle turning under network environment, comprising:1, whether target vehicle is located in the turnable headway lane;2, whether there is vehicle travel in destination area;3, when there is no vehicle travel in destination area, short-time passage is constructed to make target vehicle left turn across two opposite lanes to enter destination;4, when there is vehicle travel in destination area, whether opposite lane meets the condition of being able to insert vehicle, short-time passage is constructed to make target vehicle U-turn into opposite lane and enter destination.The dynamic regulation and control method proposed in the application can realize information interaction between network vehicles and roadside facilities, network vehicles and network vehicles under network environment, fully utilize road resources under the premise of ensuring traffic safety, improve the efficiency of vehicle turning or U-turn into destination, reduce vehicle delay and improve road traffic capacity.