290 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.

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 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 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 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 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 traffic flow microscopic simulation method based on car following behavior, which adopts a novel cellular automaton model to perform traffic flow microscopic simulation, and comprises the following steps: 1) start setting; 2) beginning to update from the first updating car a; 3) updating the state of the car i; 4) updating the front driving space of the car at the back; 5) acquiring simulation data; and 6) judging the simulation ending conditions. The model adopted by the method adopts a small cellular size; the car characteristics and the driving behavior can be finely adjusted according to the adjusting parameters, so as to facilitate the traffic flow simulations under different conditions. The simulated flow of the model is well matched with the actual measured data, and can describe the meta-stable state and the hysteresis phenomenon of the traffic system.

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 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.