660results about "Platooning" patented technology

Systems and methods are provided herein for implementing dynamic, scalable platoon formations for traveling vehicles. Such platoons may be formed and modified on the fly by vehicles that have varying sensing or autonomy capabilities. Platoons may be negotiated among available vehicles, to designate roles including global platoon leader, local leader(s), and platoon member(s). While the platoons are in place and the vehicles traveling in coordination, platoon members may depart the platoon, and the remaining members may re-allocate roles among the remaining members.

The invention relates to the technical field of intelligent network connection, and discloses a mixed traffic flow-oriented highway ramp confluence optimization control method, which comprises the steps of selecting different control methods according to state information of vehicles entering a formation control area, indirectly guiding manual driving vehicles to form a mixed formation mode through intelligent network connection vehicles, and controlling the formation control area according to the mixed formation mode. The driving states of all vehicles in the motorcade are monitored in real time; and dynamically calculating a confluence sequence and confluence time according to the driving state of the vehicle team entering the confluence control area, and dynamically adjusting the driving path and speed of the vehicle according to the confluence sequence and the confluence time. According to the method, the passing efficiency in the confluence process is greatly improved, and particularly in a complex traffic flow environment, the problem that in a traditional method, complex dynamic interaction during vehicle team confluence cannot be processed is solved.

The invention discloses a vehicle queuing length dynamic estimation method and system based on unmanned aerial vehicle cruise, belongs to the technical field of traffic state monitoring, and is used for solving the technical problems of how to realize accurate identification and tracking of queuing vehicles and panoramic scene construction and solving the problem of vehicle position deviation caused by time difference under dynamic traffic flow. And the accuracy and stability of vehicle queuing length estimation are improved. The method comprises the following steps: acquiring cruise video data of a congested road section through an inspection unmanned aerial vehicle; performing image processing on the cruise video data, and extracting vehicle characteristic parameters of the congested road section; constructing a lane-level queuing vehicle discrimination model based on two indexes of vehicle speed and linear vehicle distance; acquiring an ordered queuing vehicle set of the congested road section through the model; determining the initial queuing length of each lane according to the ordered queuing vehicle set; and dynamically correcting the initial queuing length based on a space-time compensation correction algorithm to obtain the real-time queuing length of each lane in the congested road section.

A method by which a first device performs maneuver driving in an intelligent transportation system (ITS) according to various embodiments may comprise: receiving a vehicle-to-everything (V2X) message related to a maneuver sharing and coordinating service (MSCS) from a second device; and determining whether to perform cooperative driving with the second device on the basis of the V2X message.

The invention relates to a formation driving coordination method based on vehicle-road cooperation, and the method comprises the steps: a road side unit RSU is used for releasing a traffic constraint weight value containing the information of passing restriction, path guidance, risk distribution and the like into a formation driving region, and the weight value forms a traffic environment guidance domain with a potential gradient at a vehicle end; the vehicle autonomously completes formation aggregation and position arrangement based on the own state and the weight change trend in the local sensing environment; after the formation is completed, the vehicles dynamically form a multi-level influence circle structure according to relative positions, historical cooperative behaviors and influence factors provided by the RSU, a current leading vehicle exists in each layer of influence circle, the leading vehicle has local scheduling guidance capability, and when the state of the leading vehicle is unstable or the behavior is abnormal, the control right is transferred to the auxiliary control vehicle; according to the method, the traffic environment guidance domain with the potential gradient is constructed through the traffic constraint weight value issued by the RSU, and the vehicles can autonomously perform formation aggregation and array sorting based on the local weight change trend.

One or more driving analysis computing devices in a driving analysis system may be configured to analyze driving data, determine driving behaviors, and calculate driver scores based on driving data transmitted using vehicle-to-vehicle (V2V) communications. Driving data from multiple vehicles may be collected by vehicle sensors or other vehicle-based systems, transmitted using V2V communications, and then analyzed and compared to determine various driving behaviors by the drivers of the vehicles. Driver scores may be calculated or adjusted based on the determined driving behaviors of vehicle drivers, and also may be calculated or adjusted based on other the driver scores of nearby vehicles.

An operating method of a first device (100) in a wireless communication system is presented. The method may comprise the steps of: determining that a first device (100) interrupts walking of a pedestrian; and determining, on the basis of the determination that the first device (100) interrupts walking of the pedestrian, whether to perform a first operation for preventing the interruption.

The invention provides an automatic driving vehicle lane changing intelligent decision-making method based on multi-dimensional evaluation, and the method comprises the steps: obtaining the environment information of a passing road section of an automatic driving vehicle queue through advanced sensing equipment and technology, constructing an optimization decision-making space in an automatic driving lane changing vehicle target queue, and carrying out the optimization decision-making space; and determining a candidate set where the lane changing vehicle is inserted into the target queue. And then, selecting a sine curve and model prediction control (MPC) to carry out track planning and control on the lane-changing vehicle, and traversing in sequence to obtain tracks and control information of the lane-changing vehicle inserted into each candidate position. Then, calculating the influence of the lane changing vehicle at each candidate position on four dimension indexes of operation safety, stability, efficiency and smoothness of the original queue and the target queue of the lane changing vehicle, dynamically updating the weights of the four dimension indexes according to road environment information, and carrying out weighted calculation to obtain a comprehensive score; finally, after traversal is finished, the comprehensive scores are compared, the insertion position with the highest score and the trajectory planning and control decision of the insertion position are selected as the optimal lane changing strategy to complete lane changing, the queue safety is guaranteed, and the passing efficiency is improved.

The invention provides a vehicle formation control method fusing graph nerve and reinforcement learning, and the method comprises the steps: constructing a graph structure model between vehicles, extracting the structural interaction characteristics between a current vehicle and a neighbor vehicle through a graph attention network, and combining with the dynamic evolution characteristics of the modeling historical time steps of a long-short-term memory network, and predicting a neighbor aggregation state at a future moment. And processing the two-dimensional reflection image acquired by the vehicle sensor by using a convolutional neural network, and extracting the spatial semantic information of the surrounding environment. A vehicle state, neighbor features and environment features are fused as strategy input, joint training is performed based on a multi-agent depth deterministic strategy gradient algorithm, and end-to-end control strategy optimization is realized by adopting a centralized value network and distributed strategy network structure. According to the invention, the intelligent decision-making level of the formation system is improved, and the system has good environment adaptability and cooperative control performance.

The invention discloses an automatic driving motorcade control method, device and equipment and a storage medium, relates to the technical field of automatic driving, and discloses an automatic driving motorcade control method comprising the following steps: obtaining initial vehicle position data and initial environment data, and carrying out edge calculation on the initial vehicle position data and the initial environment data to obtain an initial feature vector; generating initial vehicle control data, and generating initial local empirical data according to the initial vehicle control data; generating noise-added local empirical data according to the Laplacian noise, encrypting the noise-added local empirical data through an encryption algorithm, uploading the noise-added local empirical data to a cloud end, and calculating an action value by the cloud end according to the noise-added local empirical data; and generating target vehicle control data according to the action value, the current vehicle position data and the current environment data, and controlling the vehicle to run according to the target vehicle control data to complete automatic driving fleet control. The technical effect that the communication efficiency is optimized while the collaborative decision-making efficiency and safety of the automatic driving motorcade are improved is achieved.

The invention discloses a formation riding danger information sharing method and system for intelligent riding helmets, and relates to the technical field of information sharing, and the method comprises the steps: carrying out the deep fusion of the adjacent beacons of all intelligent helmets, the state information of a vehicle, and the multi-source heterogeneous data of an external sensor, vehicle-road cooperation and the like; and constructing and dynamically maintaining a topological graph reflecting the space-time relationship among the formation members in real time. On the basis, intelligent source end propagation strategy decision is carried out on perceived semantic danger information, and topology-based directional relay and propagation are carried out. And finally, the decided propagable information is combined with the state of the rider, so that a highly personalized early warning instruction is generated. In this way, undifferentiated global information broadcast can be effectively converted into accurate risk announcement based on individual context, and the efficiency of dangerous information sharing in formation riding and the cooperative safety capability of the whole system are remarkably improved.

The invention is suitable for the technical field of intelligent driving regulation, and provides an intelligent traffic flow line optimized driving behavior regulation method and system, and the method comprises the steps: continuously collecting the behavior characteristic data of the following distance, lane changing frequency, accelerator stability and the like of a driver, and generating aggressive or prudent driving style DNA tags; in combination with vehicle navigation destinations and routes, a temporary motorcade containing one aggressive leader vehicle and two cautious following vehicles is established in the vehicles sharing the main road path; sending a team establishment application through the Internet of Vehicles, establishing a temporary collaborative team after confirmation of all the members, and planning an exclusive collaborative route; the leader vehicle broadcasts control instructions such as lane changing and speed changing, a vehicle team is regarded as a whole to calculate a cooperative safety space, instruction transmission intervals are dynamically set, and a unified driving strategy is generated. According to the invention, vehicle complementation and cooperation are realized, the traffic flow line is optimized, the driving behavior is adjusted, and the traffic efficiency and safety are improved.

The disclosure relates to a method (100) for coordinating a first road user (10) with a second road user (20) located in an environment of the first road user (10). Furthermore, the disclosure relates to a system (40) comprising at least two motorized vehicles (50, 60), wherein the system (40) is designed to carry out a method according to one of the preceding method claims, wherein at least one of the motorized vehicles (50) is designed to carry out the method steps in order to act as the first road user (10), and wherein at least one of the motorized vehicles (60) is designed to carry out the method steps in order to act as the second road user (20).

The invention discloses an intelligent electric vehicle queue optimization method based on congestion prediction and DRL. The method comprises the following steps: (1) modeling a queue position optimization problem of an electric vehicle queue into a mathematical model which takes energy balance as a target and is constrained by a traffic environment; and (2) solving the mathematical model in the step (1) by adopting a DRL method based on a TRPO algorithm to obtain an optimal queue adjustment strategy of the electric vehicle queue. In the step (2), congestion state information in an external traffic environment is obtained through an LSTM traffic prediction and FCM method, and the congestion state information, the remaining electric quantity of each vehicle in the motorcade and the accumulated driving distance are jointly used as state input of a TRPO algorithm; according to the TRPO algorithm, a strategy network and a value network are adopted to respectively obtain a strategy for adjusting the queue position, and the return expectation in the current state is evaluated. The TRPO algorithm dynamically adjusts the updating step length of the strategy through the KL divergence updated by the constraint strategy, and guarantees the stability and convergence in the strategy updating process.

Aspects of the disclosure provide for route variation for autonomous vehicles. For example, a plurality of routes may be identified. Each route of the plurality may be a current route on which an autonomous vehicle of a fleet of autonomous vehicles is currently traveling. Each of the autonomous vehicles may use a cost-based analysis to determine routes. That there will be a cluster of autonomous vehicles of the fleet of autonomous vehicles on a road segment may be determined. A signal may be sent to one or more of the autonomous vehicles of the cluster of autonomous vehicles a signal to adjust a cost of traversing the road segment in order to increase a likelihood of the one or more of the autonomous vehicles of the cluster of autonomous vehicles avoiding the road segment.

The invention discloses a motorcade distributed cooperative control method under dual attacks of sensors and actuators, and relates to the technical field of motorcade safe operation control. The problems that a traditional motorcade cooperative security control method is poor in environmental adaptability, insufficient in attack compensation capability and insufficient in robustness are solved. The thought of hierarchical control is adopted, and the designed hierarchical control comprises a basic cooperative controller of each vehicle, a first cooperative feedback controller of each vehicle, an advanced cooperative controller of each vehicle and a second cooperative feedback controller; the stability of the system under the sensor attack is ensured through a first cooperative feedback controller, an advanced cooperative controller is designed to counteract the influence of the attack on a control loop, and an actuator attack forms negative value compensation through a second cooperative feedback controller and is fed back to a feedforward compensation module of a vehicle. The system disturbance caused by the attack of the actuator can be resolved in real time, and the robustness of control is ensured. The method is mainly applied to the cooperative control of the motorcade.

A system for mapping road segment free spaces for use in autonomous vehicle navigation. The system includes at least one processor programmed to: receive from a first vehicle one or more location identifiers associated with a lateral region of free space adjacent to a road segment; update an autonomous vehicle road navigation model for the road segment to include a mapped representation of the lateral region of free space based on the received one or more location identifiers; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.

The invention relates to the technical field of vehicle control, in particular to a vehicle queue optimal control method and system based on a function deviation triggering mechanism, and the method comprises the steps: 1, constructing a piloting dynamics model of a piloting vehicle and a following dynamics model of a following vehicle; 2, defining a local tracking error of the ith following vehicle, defining control input of the following dynamical model as a function of the local tracking error according to an optimal control target, further defining an optimal value function, and determining optimal control input according to the optimal value function; 3, obtaining an optimal value function estimation value through an adaptive commentator neural network; 4, designing a function deviation triggering mechanism according to the optimal value function estimation value and the local tracking error so as to dynamically adjust the optimal control input of the following dynamical model; and 5, setting a communication association relationship and an initial state of the leading vehicle and the following vehicle to obtain a queue control result. According to the invention, efficient cooperative driving in a complex dynamic environment is realized.

The invention relates to the technical field of call center control, and discloses an unmanned vehicle formation control and obstacle avoidance reconstruction system based on complex road conditions. The system call data acquisition module is used for acquiring multi-source interaction data in real time; the dynamic feature extraction module is used for performing semantic intention hierarchical analysis on the voice stream text conversion data, performing event fragment segmentation on the user behavior time sequence data, and generating an operation track feature vector; the scene clustering engine module is used for performing space-time alignment on the semantic intention hierarchical analysis result and the event fragment segmentation result to generate a multi-dimensional scene clustering tag; the strategy matching core module is used for retrieving an interaction strategy set matched with the current scene from a distributed strategy library according to the multi-dimensional scene clustering label and generating a dynamic control instruction sequence; and the instruction distribution execution module is used for distributing the dynamic control instruction sequences to corresponding seat terminals and voice routing equipment after sorting the dynamic control instruction sequences according to priorities.

The invention obtains a controller and a control method capable of appropriately assisting with driving by a rider of a motorcycle in group travel.In the controller and the control method according to the invention, an execution section of a controller 20 executes driving assistance operation to assist with driving by the rider. Furthermore, in the case where the group travel in which a group made up of plural motorcycles travels in plural vehicle lines is made, an identification section of the controller 20 identifies in first identification processing whether a vehicle line identification target vehicle, which is the motorcycle constituting the group, is positioned in a first vehicle line, to which a vehicle line identification reference vehicle, which is the motorcycle constituting the group belongs, or is positioned in a second vehicle line, to which the vehicle line identification reference vehicle does not belong, on the basis of surrounding environment information of the vehicle line identification target vehicle. The execution section executes the driving assistance operation on the basis of an identification result by the identification section.

The invention discloses a heterogeneous vehicle queue control method for cooperative communication and energy consumption optimization. A three-order heterogeneous multi-vehicle queue model, a dynamic event trigger mechanism module, a network update law evaluation module and an optimal queue controller module are included. Defining a local consistency error through heterogeneous vehicle formation information, and constructing a dynamic event triggering mechanism according to the local consistency error; according to the local consistency error and an error value output by the optimal queue controller module, designing an evaluation network weight updating law; and deriving the local consistency error to obtain the state of the local consistency error, and learning an optimal cost function and an optimization controller online in combination with an integral reinforcement learning algorithm and the evaluation network weight update law, so as to construct an optimal queue controller module. According to the method, the number of communication times between vehicles is reduced through a dynamic event triggering mechanism, and communication resources are saved. And meanwhile, the queue stability is realized in the optimal queue controller module, and the problem of minimization of energy consumption is solved.

The invention relates to an information physics multi-agent cooperative control method in a mixed traffic flow environment, and belongs to the technical field of intelligent traffic, and the method comprises the following steps: S1, at the upstream section of an intersection, according to the driving direction and dynamic state of vehicles, taking a first vehicle reaching the boundary of a lane changing area as a trigger vehicle, detecting the vehicles in a preset time window, dividing the triggered vehicle and all vehicles in front of the triggered vehicle within the detection range into the same target vehicle set; s2, for the target vehicle set, constructing a 0-1 integer programming model, and optimizing an organization scheme of a hybrid queue; s3, on the basis of the optimal formation scheme, constructing a vehicle and hierarchical control framework which comprises two parts of cloud deep reinforcement learning DRL and vehicle-end distributed model prediction control DMPC; the cloud DRL outputs a reference action; and the vehicle end DMPC takes the reference action and state output by the cloud end DRL as a target, and realizes cooperative passing of the whole motorcade while satisfying local constraints.

Various embodiments relate to methods and systems of using street lights to indicate emergency or other priority routes along a route in a network of streets. In one example, a central controller receives a request to indicate a priority route, for use by one or more vehicles. The central controller queries a database of street lights to select a plurality of street lights that are located along the priority route. The central controller transmits, to respective controllers associated with the selected plurality of street lights, respective indications to operate the selected plurality of street lights in a priority route mode. Embodiments further include updating an operating mode of the selected street lights based on position updates for the one or more vehicles.

The invention provides a fleet route feedback and control system. A fleet of multiple vehicles is executed by a self-aware vehicle. A self-aware vehicle receives capability information in a fleet message via a wireless transceiver of the self-aware vehicle indicating a capability of the vehicle to cross an obstacle along a route. A performance level indicative of a relative capability of the vehicle for each type of obstacle is determined based on the capability information. One or more ranking strategies are utilized to define a vehicle ranking for crossing the obstacle along the route, the vehicle ranking based on the performance levels of the vehicles and the obstacle. And the motorcade crosses the obstacle according to the vehicle sequence.

An apparatus of a first vehicle includes a processor configured to detect a fault associated with a second vehicle. The processor is configured to obtain sensor data associated with a sensing region of the second vehicle. The processor is configured to provide an output, based on the sensor data, for controlling the second vehicle to traverse a vehicle transportation network

Provided is a driving assistance device that is safe and convenient in a situation where reverse playback cannot be performed in passing each other on narrow paths or the like. Driving operation of driver and surrounding obstacle information are stored at forward movement. At this time, the information of the front sensor (or the front camera) and the information of the rear sensor are integrated to determine and store the object type (stationary object, moving object). After the backward movement assistance is started, the surrounding obstacles of the rear sensor are collated with the stored surrounding obstacles, and execution of the backward movement assistance based on the stored driving operation is determined according to the collation result.

Provided herein is technology relating to roadway design and traffic control systems and methods for connected and automated vehicle and highway (CAVH) systems, and particularly, but not exclusively, to systems and methods for controlling switching of vehicles between automated mode and human-driven mode, systems and methods for vehicle merging, diverging, and overtaking on automated lanes of multiple lane highways, systems and methods for emergency management and roadside assistance on automated lanes, and / or systems and methods for managing automated vehicle lanes on urban major and minor expressways.

Collective action by multiple vehicles traversing a vehicle transportation network is used to improve operation of vehicles and utilization of the vehicle transportation network. Sensor data for multiple monitored vehicles traveling in a common direction along a road in a vehicle transportation network is received and used as input to a traffic estimation model to determine an estimated congestion for the road in the common direction. A cruise control setting for each of several controlled vehicles, at least some of the controlled vehicles traveling behind the monitored vehicles in the common direction, is determined from the estimated congestion. The cruise control setting is transmitted to the controlled vehicles to modify operation of the controlled vehicles using respective cruise control systems of the controlled vehicles.

The disclosure relates to a motor vehicle, to a driver assistance system, and to a method for operating the driver assistance system. A relevant slipstream region of a further motor vehicle driving in a predetermined surrounding region of the motor vehicle is detected. A current slipstream efficiency is ascertained based on whether the motor vehicle is driving in the slipstream region and, if so, based on which of the slipstream partial regions of the slipstream region the motor vehicle is driving in. An overall aerodynamic efficiency is ascertained based on a current driving speed of the motor vehicle and the current slipstream efficiency. An individual overall aerodynamic efficiency potential that characterizes an overall aerodynamic efficiency that will be achieved by the motor vehicle when same drives in the slipstream region of the further motor vehicle is assigned to the further motor vehicle based on the driving speed and slipstream region.