292 results about "Car following" patented technology

A platoon model allows improved prediction of preceding vehicle future state. In this context, the preceding vehicle is a vehicle immediately ahead of the host vehicle, and the dynamic state of the preceding vehicle was predicted based on data received from one or more vehicles in the platoon. The intelligent driver model (IDM) was extended to model car-following dynamics within a platoon. A parameter estimation approach may be used to estimate the model parameters, for example to adapt to different driver types. An integrated approach including both state prediction and parameter estimation was highly effective.

The invention discloses a system for controlling automatic car-following operation and a method thereof. The system comprises a piloting-car control system and following-car control systems, wherein the piloting-car control system is arranged on a piloting car, and the following-car control systems are arranged on following cars and communicate with the piloting-car control system. The piloting-car control system collects related data about the piloting car through a plurality of sensors and a GPS positioning device which are arranged on the piloting car, and sends the data to the following car through a car-car communicator. Each following-car control system receives the data based on a car-car communicator and collects data through a plurality of sensors, a GPS positioning device, a radar, a camera and the like which are arranged on the following car, and then controls automatic car-following driving of the following car according to the data. Multi-data is integrated to judge the following situation of the following car so that the safety and the reliability of automatic driving can be ensured effectively.

A method and apparatus are disclosed for reducing traffic flow instabilities and increasing vehicle throughput by monitoring the distances and velocities of motor vehicles leading and following a center vehicle and controlling the velocities of the vehicles to maintain a steady relative distance between the center vehicle and the leading and following vehicles. Using distance and speed information derived from both leading and following vehicles reduces the loop gain of feedback needed below one (1) and diminishes traffic instabilities caused by “car following.”

A method and apparatus are disclosed for reducing traffic flow instabilities and increasing vehicle throughput by monitoring the distances and velocities of motor vehicles leading and following a center vehicle and controlling the velocities of the vehicles to maintain a steady relative distance between the center vehicle and the leading and following vehicles. Using distance and speed information derived from both leading and following vehicles reduces the loop gain of feedback needed below one (1) and diminishes traffic instabilities caused by “car following.”

The invention discloses an electronic toll collection system and method. The system consists of a lane control computer and a road side unit, wherein the lane control computer is used for receiving information of the road side unit and performing corresponding treatment on the information; the road side unit comprises a wireless location module used for locating a vehicle-mounted e-tag, a toll transaction module used for communicating with the vehicle-mounted e-tag, acquiring billing and vehicle information of the vehicle-mounted e-tag and completing toll transaction, a video detection module used for detecting vehicle positions, capturing vehicle images and recognizing vehicle registration plates, and a control module used for realizing real-time position matching between position information, acquired by the wireless location module, of the vehicle-mounted e-tag and the vehicle position information detected by the video detection module and achieving corresponding process control. According to the invention, the toll transaction success rate can be effectively improved, car-following interference is reduced and toll dodging is prevented.

The invention discloses an optimization method of an intersection dynamic comprehensive evaluation and signal control system based on a real-time simulation model. Statistical analysis is performed on an intersection vehicle arrival time distribution law and a saturation time headway distribution law, simulation control is performed on vehicles monitored in real time, analytical calculation of traffic flow parameters of an intersection such as mean queue length, queue time and travel vehicle speed, and characteristics such as vehicle exhaust, oil consumption and noise, and acquired information is shared and an optimized signal control scheme is implemented by a third party control platform. The method has the advantages that a real-time data acquisition function is realized by test coils embedded in interfaces at the near end and the far end of an intersection entrance road; the simulation model adopts vehicle arrival and green light termination as triggering conditions of starting a simulation system traffic flow parameter configuration; a saturation time headway is adopted as a time series distribution basis of vehicle leaving; simulation car following and lane changing behaviors are realized by a series of algorithms; and a traffic simulation function has timeliness, accuracy and rapid response.

The invention belongs to the technical field of intelligent auxiliary driving for vehicles and vehicle safety and particularly relates to a learning cruise control system and method used for a drivingstyle and based on mode switching. By means of the system and method, self-adaption cruise control is conducted through mode switching of a special driver style and self-adaption learning for the carfollowing behavior. According to the system and method, the driving style is defined to be switch strategies of a cruise control mode, an acceleration approach mode, a steady-state car following modeand a quick braking mode of a driver under different car following conditions, the driving style of the driver is learned, and the driving characteristic of the driver is further learned through a learning method based on a continuous state under each driving mode. The system is suitable for automatic driving of an L2-level vehicle, the aim is to effectively learn the driving style characteristicof the driver, and the adaptability and acceptability of the specific driver for the self-adaption cruise system under different working conditions are improved.

The invention relates to a cooperative self-adaptive cruise control system algorithm based on traffic information. The traffic information serves as a condition capable of being obtained, prediction control is carried out through a model, and the riding comfort, the fuel economy, the safety and the car following performance serve as to-be-optimized targets; meanwhile, vehicle capacity limitation is added and respectively converted into corresponding optimal targets and system limitation, and as a cost function is built and minimized, a optimal solution sequence is obtained, and a first value is taken and applied to the system to optimize targets. According to the cooperative self-adaptive cruise control system algorithm, on the scene that signal lamps and front waiting motorcades of a CACC system exist in city working condition, more pieces of interference are treated to achieve that the speed of the vehicles is more actively adjusted, unnecessary speed keeping or large speed reduction is reduced so that the fuel economy and the riding comfort can be improved to achieve multi-objective optimization, the condition that as the vehicles follow some radical drivers, the driving behaviors of the vehicles are also radical is reduced, and optimal speed adjustment can be achieved according to the periphery environment.

The present invention relates to the field of traffic safety, in particular to a judgment and prediction method of driving behavior intention based on the implicit Markov model (HMM), aiming to overcome the defect that the existing driving behavior intention recognition and prediction technology does not take into account the dynamics and continuity of driving behavior, as well as complex behaviors such as lane changing, car following and braking and the like. The judgment and prediction method of driving behavior intention obtains time series segmentation data from cluster analysis of dynamic driving data, the linear direction HMM, lateral HMM and speed classification model are trained respectively, and the obtained identification results are regarded as the observation sequence of behavior recognition layer; Then, off-line training is performed to deal with normal or emergency braking, normal or emergency lane change, normal or dangerous driving behavior, and multi dimensional discrete HMM model; according to the model parameters and the observation sequence, the next time step driving behavior can be predicted. The judgment and prediction method of driving behavior intention takes the complexity and continuity of driving behavior into account and can dynamically judge and predict the driving behavior intention and warn the dangerous behavior, and accordingly can be applied to driving behavior evaluation and driving assistance system.

The invention discloses an ETC lane anti-jamming method based on a multi-beam antenna. The ETC lane anti-jamming method comprises the steps that signal and position detection is carried out through the multi-beam antenna on vehicle-mounted electronic tags in an antenna communication area of a lane where the multi-beam antenna exists; when the multi-beam antenna detects that the vehicle-mounted electronic tags exist in the communication area, an orientation downlink beam is emitted to each vehicle-mounted electronic tag, each downlink beam forms an independent and relatively small communication subdomain, and concurrent communication of the downlink beams with the vehicle-mounted electronic tags is achieved through a space division multiplexing mode. According to the ETC lane anti-jamming method based on the multi-beam antenna, beam forming technology is utilized, effective communication areas for ETC trading can be regulated, an OBU accurate position of an ETC lane is detected in real time, the orientation downlink beam is regulated and emitted according to positions of the electronic tags, the concurrent communication of the downlink beams with the vehicle-mounted electronic tags is achieved through the space division multiplexing mode, and therefore the lane passage speed is improved, and meanwhile vehicles on the ETC lane are prevented from car following jamming and adjacent channel interference.

The invention relates to a method for calculating the expected car following distance in driver car following behavior analysis. On the basis that drivers design multiple different driving scenes during data collection on a car surrounding simulation testing platform, driving data is collected aiming at the drivers; the car following behavior feature parameter data set of each driver is extracted from the driving data; the feature behavior parameter data sets of all the drivers are clustered into several different driving classifications, and are adopted as training data sets; then the training data sets are utilized for carrying out classification judgment on the current drivers to be classified. Different car following behaviors are classified, the efficiency of collecting car following behavior data is improved, the cost is low, and good safety is achieved. The longitudinal driving behaviors of the drivers can be simulated through the Gussian process, the individualized expected car following distance can be provided for the current drivers, and the active adaptive capacity of an auxiliary driving system to different drivers is improved.

The invention belongs to the technical field of harbor road design and relates to a cellular automata based design method of circular curve elements of bends of harbor roads. The method comprises the following steps: preliminarily determining the radius ranges and the arc length ranges of the circular curves to be designed; determining the maximum speeds and acceleration and deceleration probability of the vehicles by analyzing the safety and the comfort and treating the safety boundary conditions; establishing a simulation model of the cellular automata, determining the maximum speeds and acceleration and deceleration probability of the vehicles and designing car-following and lane-changing rules of the vehicles; and simplifying the types of the vehicles, converting the types of various vehicles into a single type, simulating the traffics in the harbor districts by changing the radii and the arc length of the circular curves of the bends of the harbor roads according to the established simulation model of the cellular automata, evaluating the simulation result and investigating whether the design scheme can meet the requirement of the traffic capacity. Compared with the traditional method, the design method of the invention has the advantage that the design scheme of the line shapes of the bends in the harbor districts is further consistent with the traffic characteristics in the harbor districts.

The invention provides a car following model generating method based on driving types and preceding vehicle acceleration speed information. By using a cluster data mining method, driving types of drivers are divided according to actual data; individual expectancy effect is imported on the basis of a full velocity different model; further the influence of preceding vehicle acceleration speed information on car following behavior is taken into consideration; thus a car following model is obtained. Compared with the conventional car following model, the car following model greatly improves the traffic flow stability and better accords with actual traffic flow status.

The invention discloses a dual-mode switch based self-adaptive cruise control method for an electric car. The method comprises steps as follows: establishing a stable car following mode; establishing a fast approaching mode; establishing a dual-mode switch rule for control moment distribution on the basis of fuzzy reasoning. According to the method, driving demands of a driver are summarized through analysis of actually measured microscopic driving NGSIM data collected by Federal Highway Administration combined with microscopic traffic simulation research scholars; the control rule of the fast approaching mode and the stable car following mode is constructed under a model prediction control frame, and a switch rule between the modes is made according to the fuzzy reasoning. A control mode of a self-adaptive cruise control system is closer to driving characteristics of a real driver and is more adaptive to complicated road conditions. The dual-mode switch based self-adaptive cruise control method for the electric car can really reflect demands for the stable car following mode, the fast approaching mode and the like of the driver in a normal driving process, and can guarantee safety, car following performance, comfort and economic efficiency of the car in a traveling process.

The invention discloses a self-adaptive cruise control method based on a safe distance model with driver car-following characteristics, which mainly comprises the following steps: 1, based on the analysis of the motion states between the cars, a safe car-following distance model conforming to the diver car-following characteristics is designed; 2, a fourth-order continuous car-following model of an ACC system is established; 3, the safe car-following distance model established in step 1 is used as the following target of the upper layer decision algorithm of the adaptive cruise system. According to the invention, aiming at the problem that the expected safe distance selected by the upper layer control algorithm of a prior adaptive cruise system does not confirm to the actual driver characteristics, the safe car-following distance model is redesigned and applied to the upper layer decision algorithm, so that the control characteristics of the adaptive cruise system is closer to the control behaviors of a skilled driver and the acceptance and utilization rate of the system are improved.

The invention relates to a vehicle formation autonomous following control system and method and belongs to the technical field of vehicle automatic driving, which solves the problem that the error rate for car-following-car after vehicle formation is high and following cannot be restored in the case of a loss of following. The vehicle formation autonomous following control system comprises a control device, a communication device and a video detecting device; the video detecting device is disposed in a following vehicle for identifying and storing the license plate and image features of the preceding vehicle; the control device is used for collecting and processing a video detection result of the video detecting device and performing path planning and decision control for the vehicle; thedecision control specifically comprises controlling the following vehicle to drive to the back of the front vehicle so that the following vehicle enters the field of view of the video detecting device; the communication device is used for implementing communication between vehicles. Autonomous following control of the vehicle formation is realized, which reduces the error rate of following of thevehicle formation and solves the problem that the vehicle cannot be re-followed after being lost.

The invention discloses a car-following simulation method based on fuzzy mathematics in the technical filed of motor vehicle driving behavior simulation. The method comprises the steps of: firstly, drawing a speed-time curve and a speed-displacement curve of a leading car and a following car respectively; acquiring driving character parameters of the following car and moving state parameters of the two cars one after the other; and then, substituting the character parameters and the moving state parameters of the following car into a fuzzy inference system, deriving a vehicle motion control rule and simulating car driving. By taking into consideration feature differences of drivers and different characteristics of vehicles, the method can better reflect drivers' decision making process, better simulate drivers' driving behavior, and reproduce real traffic scenes more effectively.

The invention provides an urban intersection mixed traffic flow collaborative optimization underlying control method, and belongs to the field of traffic engineering. The method comprises the following steps: a microcosmic car following model is determined; an initial trajectory of a car is predicted; a conflict cooperation model is established for the conflict type of an intersection; a cooperative control strategy set is drawn up; whether the car can smoothly pass through a conflict area or not is judged; if the car can smoothly pass through the conflict area, the car continues to run according to the speed of the microcosmic car following model; if the car cannot smoothly pass through the conflict area, the specific situation of the car in the process of passing through the conflict area needs to be judged, and a corresponding cooperative control strategy needs to be made; and the running track of a target car is optimized according to the cooperative control strategy, the optimization problem is summarized into an optimal control problem of discrete time state constraints, the problem is solved by the idea of dynamic programming to obtain a cooperative optimization control strategy about the target car, and the cooperative optimization control strategy is applied to the target car to control the running of the target car.

The invention discloses a speed control multi-target optimized car following algorithm of an automatic driving vehicle. According to the algorithm, a car following speed control model is provided on the basis of deep reinforced learning, and the model not only simulates human driving but also optimizes the driving safety, efficiency and comfortableness. Collision time, heat time-distance experience distribution and jerk are combined to construct a reward function reflecting the driving safety, efficiency and comfortableness, a practical driving data training model in an NGSIM (Next GenerationSimulation) project is used, a car following behavior simulated by the model is compared with a behavior observed in NGSIM experience data, test and error test of a learning intelligent agent in the simulated environment is reinforced, and safe, comfortable and efficient vehicle speed control is learned in a maximally reward accumulation way. It is proved that, the car following speed control algorithm provides a safer, more efficient and more comfortable driving capability compared with a human driver in the real world.

The invention provides a dynamic speed limit optimization control method to reduce secondary rear-end collisions in bad weather on a fast road. Traffic flow detection devices and meteorological monitoring devices are arranged on each section of the fast road to acquire traffic flow status and meteorological data. In the propagation process of congestion caused by traffic accidents in bad weather,a car-following model is used as the core algorithm to calculate the conditions of traffic accidents, so as to calculate and solve the optimal speed limit value of the upstream section. The speed limit value is issued through variable information signs on the roadside, so as to reduce secondary rear-end collisions. Compared with the prior method, the improved dynamic speed limit optimization control method provided by the invention considers the traffic flow operating status on the road and meteorological parameters such as visibility and road surface conditions at the same time; the calculated optimal speed limit value is accurate and reasonable; the climate and environmental conditions of the specific road can be considered; secondary rear-end collisions in bad weather can be effectivelyreduced; and the traffic safety of fast roads in China can be improved.

The invention provides a car following navigation method and system and a navigation system. The method comprises the steps that the navigation system obtains the first position information corresponding to a piloting first navigation device in real time and obtaining the second position information corresponding to a following second navigation device in real time at the same time; a piloting route where the first navigation device is located is determined according to at least one piece of obtained first position information; a navigation route between the piloting route and the second position information is set, so that the second navigation device conducts navigation. By means of the car following navigation method and system and the navigation system, the function of car following navigating is achieved on the second navigation device when two vehicles run at the same time.

A computer-implemented method and system for simulating traffic flow configurations. The method includes simulating vehicle traffic on a virtual roadway configuration in a simulated environment using a car-following model to provide simulated vehicle traffic at an intersection, wherein the car-following model includes vehicle parameters of at least one of a lead vehicle and a subsequent vehicle at each timestep, the lead vehicle positioned in front of the subsequent vehicle, and modifying the simulated vehicle traffic using a ghost vehicle approximation model to provide simulated traffic flow distributions while maintaining incident avoidance criteria, wherein the ghost vehicle approximation model includes setting a speed value and a gap distance value of a ghost vehicle positioned in front of the lead vehicle and / or the subsequent vehicle to modify the vehicle parameters of the lead vehicle and the subsequent vehicle at each timestep.

The invention discloses an intersection vehicle guidance method under a mixed traffic condition. At an intersection containing network-connected vehicles and ordinary vehicles, a trigonometric function speed guiding method is used to carry out speed guiding for the network-connected vehicles. The speed guiding of the ordinary vehicles is to guide the driving speed of the ordinary vehicles indirectly through the guiding speed of the network-connected vehicles according to a car-following model. In the method provided in the invention, a mixed motorcade condition is considered, the network-connected vehicles are used as head vehicles and the vehicle speeds of the ordinary vehicles are indirectly guided so that the passing efficiency of the intersection is increased and the tail gas dischargeof motor vehicles in an intersection area is reduced. In the method, an algorithm is simple, operation complexity is low and the sensitivity of the speed guiding method to time delay is satisfied.

The invention discloses an energy-saving stability motion control method for a networked car queue, and belongs to the technical field of a car driving assistance system. The method includes the stepsof first establishing a single vehicle dynamics model in the networked car queue and calculating the acceleration of each vehicle in a pulse phase and a glide phase; by establishing a car following subsystem model with the relative speed and the relative inter-vehicle distance error of a car following subsystem composed of two adjacent vehicles as state variables, determining four driving modes of the car following subsystem to obtain a host vehicle state trajectory on a [delta]v-[delta]R plane; for the pulse or glide cases of a front vehicle, obtaining host vehicle [delta]v-[delta]R state plane partition maps respectively, and determining the host vehicle acceleration of each range; and finally, synthesizing the host vehicle state plane partition maps obtained respectively for the pulseand glide cases of the front vehicle, and obtaining a switching control chart and a control law of the host vehicle to achieve energy-saving motion control of the vehicles in the networked car queue.The method in the invention has low requirements on the precision of the position control of the vehicles and has the effect of high fuel efficiency.

The invention discloses a heterogeneous vehicle queue distributed energy-saving control method based on MPC. The method includes 100, constructing a heterogeneous vehicle queue ;200, constructing a distributed heterogeneous queue energy-saving control method framework comprising an energy-saving control and a vehicle following controller; 300, designing an energy-saving controller; Step 400, determining the economic vehicle speed of each vehicle in the heterogeneous vehicle queue; 500, designing a car following control; 600, clarifying that division of labor among the vehicle controllers in the heterogeneous queue; Step 700, allowing each vehicle in the heterogeneous vehicle queue to travel according to the economic vehicle speed provided in step 400 without road gradient; and, in the caseof road gradients, allowing each vehicle in the heterogeneous vehicle queue to travel at the current economical vehicle speed provided by the following steps. The control method provided by the invention can achieve the following and stability control of the heterogeneous vehicle queue and simultaneously realize the energy-saving control of the heterogeneous queue under the condition of acquiringthe road information, and the method can effectively reduce the energy consumption of the vehicle queue.

The invention discloses a bend safe speed calculation method based on vehicle and road collaboration. The method comprises the steps as follows: firstly, the cut-in speed of a vehicle at a bend, the geometrical characteristic of the bend and other information are acquired by use of an OBU (on-board unit) and an RSU (roadside unit) in a vehicle and road collaboration system, driving styles of different drivers are reflected by replacing the bend radius with the driver running radius, and a bend speed model self-adaptive to the driving styles of the drivers is established; then, accident forms such as sideslip, rollover, rear-end collision and the like of the vehicle are considered, the optimal bend speed of an autonomous vehicle is decided in the aspects of yaw stability, sideslip stability, car following safety, driving comfort and the like, and finally, expected speed planning is provided for an active control system of the vehicle. Compared with existing bend safe speed calculation methods, the bend speed calculation method has the advantages that multiple accident forms such as sideslip, rollover, rear-end collision and the like are considered comprehensively, considered factorsare more comprehensive, and the speed planning of the autonomous vehicle can be supported.

The invention relates to an expressway mixed traffic flow confluence collaborative optimization underlying control method, and belongs to the field of traffic engineering. The method comprises the following steps: determining a microcosmic car-following model; predicting an initial trajectory of the vehicle; establishing a confluence model; drawing up a cooperative control strategy set; judging whether the vehicle can smoothly finish confluence or not; if the judgment result is that the vehicle can smoothly complete confluence, the vehicle continuing to run according to the speed of the microscopic car-following model; if the judgment result is that the vehicle cannot smoothly finish confluence, further judging the specific situation of the vehicle in the confluence process, and making a corresponding cooperative control strategy according to the specific situation; and optimizing the driving track of the target vehicle according to the made cooperative control strategy to obtain a cooperative optimization control strategy about the target vehicle, and acting the cooperative optimization control strategy on the target vehicle to control the operation of the target vehicle. By meansof the method, the vehicles can stably run at a high speed, and then the vehicle traffic capacity of the highway ramp in the confluence area is improved.

The invention provides a motorcade autonomous car following system, a portable device and a method; the system comprises the following parts: a GPS module in a data acquisition unit is used for gathering vehicle positioning information, and an inertial navigation module in the data acquisition unit is used for gathering vehicle acceleration information and angular velocity information; a fusion positioning module in a data processing unit is used for fusing the GPS information and inertial navigation information in real time, thus obtaining a self-vehicle absolute pose; a car following controlcalculation module is used for planning an absolute route according to the self-vehicle absolute pose information, front vehicle absolute pose information and a head vehicle history route, updating an absolute route table, and calculating to obtain a vehicle target velocity and direction turning angle for vehicle control. The system and method can realize combined positioning via the GPS module and the INS module, are high in positioning precision, high in data refreshing frequency, and cannot be affected by illumination and weather environment factors, thus effectively improving the autonomous car following system precision and reliability. The device is portable and easy to construct, and low in cost.