63 results about "Vehicle platooning" patented technology

The invention provides a vehicle formation control system and method, and the system comprises a formation formation and management module which initiates or responds to a formation request when a preset condition is satisfied, and builds / maintains a formation pattern and a member relationship; the inter-vehicle communication module is used for establishing a direct communication link between the formation vehicles and broadcasting and receiving vehicle state information and a control instruction; the target vehicle sensing module fuses vehicle-mounted multi-sensor sensing data and the front target vehicle state information provided by the inter-vehicle communication module; the following track planning module is used for generating a following driving track of the vehicle based on the motion state information of the front target vehicle and the road constraint; the cooperative longitudinal and transverse control module performs cooperative control on driving, braking and steering of the vehicle according to the following driving track; and the event / safety management module is used for performing protection and recovery operation on formation control based on collision risk measurement and operation health criteria. According to the invention, the safety and continuity of the formation can be improved.

The invention relates to a safety constraint vehicle formation distributed sliding mode control method based on a preset performance function. Compared with the prior art, the defect that vehicle formation control is difficult to meet safety constraint, preset performance and distributed cooperative control at the same time is overcome. The method comprises the following steps: establishing a vehicle formation three-order state space model; generating a following vehicle smooth reference trajectory; a preset performance constraint of the following vehicle is designed; calculating the expected speed of the following vehicle; calculating the expected torque of the following vehicle; a following vehicle driving system is controlled; and formation cooperative control is realized. According to the invention, through a reference generation mechanism of constraint perception, a preset performance function with a self-adaptive boundary and a topological structured reaching law, high-performance tracking of the track of the pilot vehicle is realized on the premise of ensuring that all following vehicles meet the time-varying safety boundary.

Systems and methods for coordinating and controlling vehicles, for example heavy trucks, to follow closely behind each other, or linking to form a platoon. In one aspect, on-board controllers in each vehicle interact with vehicular sensors to monitor and control, for example, relative distance, relative acceleration or deceleration, and speed. In some aspects, vehicle onboard systems supply various data (breadcrumbs) to a Network Operations Center (NOC), which in turn provides data (authorization data) to the vehicles to facilitate platooning. The NOC suggests vehicles for platooning based on, for example, travel forecasts and analysis of relevant roadways to identify platoonable roadway segments. The NOC also can provide traffic, roadway, weather, or system updates, as well as various instructions. In some aspects, a mesh network ensures improved communication among vehicles and with the NOC. In some aspects, a vehicle onboard system may provide the authorization data.

The invention discloses a vehicle formation data-driven prediction cloud control method under time-varying topology, and the method comprises the steps: carrying out the modeling of a vehicle formation under a linear parametric system, enabling a model to describe the change of a topological structure of the vehicle formation, and carrying out the control through a data-driven prediction control algorithm, therefore, the topology change caused by vehicle departure and the like can be effectively dealt with. Besides, by deploying a control algorithm in a cloud server, sufficient computing resources of the cloud can be utilized to solve the optimization problem, the real-time performance of the control process is ensured, and therefore an effective solution can be provided when the vehicle-mounted hardware is difficult to meet the control requirement due to the increase of the vehicle formation scale.

This invention discloses a privacy-preserving dynamic vehicle platooning management method and system, comprising: a lead vehicle broadcasting a vehicle platooning recruitment message and transmitting the message to cloud server A; simultaneously, member vehicles generating request messages containing location information and digital signatures, which are forwarded to cloud server A via roadside units (RSUs); cloud server A, with the assistance of cloud server B, calculating the encrypted spatial distance between the lead vehicle and the requesting vehicle using the encrypted Manhattan distance calculation method, and finally outputting a trusted vehicle platooning decision; after the platooning trip is completed, member vehicles generating feedback scores and digital signatures, which are sent to cloud server A via roadside units (RSUs); cloud server A sending aggregated reputation encrypted messages to a trusted authority; and calculating the new reputation value of the lead vehicle from the aggregated reputation encrypted messages received from cloud server A. Using the technical solution of this invention, dynamic platoon disbanding in platooning management is achieved, while effectively protecting privacy and resisting various attacks.

This invention belongs to the field of automotive control technology and discloses a vehicle platooning lateral and longitudinal coordinated control method with external disturbances, input delays, and communication delays, comprising: S1. Establishing a vehicle platooning model: the vehicle platooning model includes a longitudinal vehicle model considering longitudinal input delays and communication delays, and a lateral vehicle model considering lateral input delays; S2. Designing a platooning lateral and longitudinal coordinated controller: the lateral and longitudinal coordinated controller includes a longitudinal controller designed based on the vehicle longitudinal model and a lateral controller designed based on the vehicle lateral model; S3. Controlling vehicle lateral and longitudinal movement through the lateral and longitudinal coordinated controller: the longitudinal controller ensures that the target following vehicle maintains the desired vehicle spacing and tracks the speed of the lead vehicle; the lateral controller controls the target following vehicle to move along the road centerline.

The application provides a control method and device for automatic driving vehicle formation driving, the method comprising: obtaining current scene characteristics based on a roadside unit and / or a perception device configured by an automatic driving vehicle; matching the current scene characteristics with a preset scene library to obtain target formation control information, wherein the preset scene library stores a mapping relationship between multiple groups of scene characteristics and formation control information, and the target formation control information comprises navigation vehicle control information and control information corresponding to at least one following vehicle; and controlling the navigation vehicle and the following vehicle to drive in formation based on the target formation control information. Through the application, the formation scene of the automatic driving vehicle in multiple scenes such as highways is introduced, the problems of scattered test organization and limited scene coverage in traditional road testing are solved, a multi-source heterogeneous perception fusion dynamic correction can be constructed, the formation cooperative control of the automatic driving vehicle in a high-speed environment is realized, and the problem of perception degradation caused by failure of a single sensor is solved.

This application provides a method for processing travel activities, a method for recommending travel activities, a method for vehicle platooning, an apparatus, an equipment, a vehicle, and a storage medium, relating to the field of automotive technology. The method includes: responding to a user's trigger operation on target travel information displayed in a first display interface, displaying travel details and a travel trigger command in a second display interface; after detecting a user's trigger operation on a platooning travel command, determining travel activity information matching the platooning travel command; if it is determined that the current time matches the travel start time in the travel activity information, generating and displaying travel navigation information; the travel navigation information is used to guide the user's vehicle platoon to complete the travel activity route corresponding to the travel activity information. The method of this application can improve the diversity and enjoyment of user travel, thereby enhancing the user's travel experience.

The present invention discloses a method, system and medium for optimizing the following distance calculation of unmanned vehicle platooning. The method comprises the following steps: analyzing the quantitative variation characteristics of the air drag coefficient and lift coefficient of vehicles in the unmanned vehicle platoon; calculating the minimum following distance of the following vehicle based on the quantitative variation characteristics, a nonlinear dynamic model and the prediction iteration of the vehicle time-varying state data; determining the following distance optimization interval according to the minimum following distance, analyzing the influencing parameters between the following distance and the braking margin, energy consumption, lift coefficient and the tracking ability of the leading vehicle; dynamically calculating the optimal following distance of the following vehicle according to the influencing parameters within the following distance optimization interval; the present invention can obtain the quantitative law of the air drag coefficient and lift coefficient of the platoon vehicles, comprehensively consider the vehicle speed, road resistance, transmission efficiency and the response characteristics of the driving / braking elements, and dynamically predict and iteratively calculate the optimal following distance of each following vehicle in real time.

The invention discloses a lightweight privacy protection vehicle formation building method, which adopts a multi-level encryption mechanism from pilot vehicle selection and member attribute matching in a formation forming stage to feedback aggregation and reputation updating in a formation disintegration stage, and ensures the confidentiality and integrity of sensitive data (such as reputation values, attributes and feedback reports). Through dynamic bucket mapping, random vector perturbation and lightweight ciphertext calculation, the method significantly reduces calculation and communication overhead while guaranteeing privacy, and solves the core problems that a traditional scheme depends on a trusted third party, is susceptible to association attacks, is insufficient in dynamic adaptability and the like. The technical framework can be widely applied to logistics fleet collaborative transportation, urban intelligent bus formation scheduling, automatic driving vehicle group collaboration and other scenes, and safe and reliable technical support is provided for large-scale deployment of an intelligent traffic system.

This invention provides a connected vehicle cooperative control method based on adaptive feedback technology, comprising the following steps: establishing a simplified vehicle queuing control model; acquiring the actual control input of each following vehicle while considering controller gain variations; adding an adaptive feedback controller; optimizing the adaptive feedback controller to calculate tracking error while considering packet loss in inter-vehicle communication data; and transforming the vehicle queuing control model to achieve vehicle queuing control. This invention proposes a distributed adaptive feedback controller for vehicle queuing that guarantees both internal stability and L2 queue stability, wherein the feedback gain and parameter adaptive law of each following vehicle are dynamically determined based on its own tracking error. This avoids the packet loss matrix mentioned earlier, significantly reducing computational complexity.

The present invention discloses a formation decision-making method for connected intelligent vehicle formations based on nested graph reinforcement learning, belonging to the technical fields of vehicle networking and autonomous driving. The method includes: S1, collecting the vehicle state information within the formation and processing the state information to obtain an inter-formation nested graph and an intra-vehicle nested graph; S2, using a feature extraction network to extract features from the inter-formation nested graph and the intra-vehicle nested graph, and obtaining the actions of each intelligent vehicle based on the extracted features; the feature extraction network includes: a graph attention layer, a fully connected layer, and an activation layer, and a multi-head attention mechanism is introduced to improve the feature extraction ability; S3, optimizing the actions using a reward function to obtain an intelligent vehicle decision-making method. The present invention can achieve fully integrated formation decision-making control under high-efficiency communication in a highway environment with high dynamics, high complexity, and strong randomness.

This invention relates to a method for platooning autonomous driving vehicles in the slow lane of a curved section of a highway. By acquiring basic attribute information of the autonomous driving vehicle and its interoperating vehicles, preliminary information on the autonomous driving vehicle platooning scheme, highway alignment design and environmental information, and weather information as input information, the method analyzes scenarios based on risk scenario types, categorizing them into autonomous driving vehicle line-of-sight (LOS) failure and interoperating vehicle LOS failure. A virtual testing method is used to obtain the available LOS of the lead vehicle and interoperating vehicles in the analyzed scenarios, constructing a database linking the input information and available LOS. Furthermore, the optimal autonomous driving vehicle platooning scheme is determined based on the principles of LOS safety and speed coordination. This invention comprehensively considers road environmental conditions on curved sections of highways and factors related to the level of autonomous driving, effectively proposing a platooning scheme that meets the safe driving requirements of autonomous driving vehicles, providing a reliable technical means for their actual safe operation.

The invention provides a vehicle formation communication and emergency management system and method, and belongs to the technical field of intelligent traffic and Internet of Vehicles. The invention aims to solve the problems that high-bandwidth data transmission and low-delay instruction response cannot be considered at the same time and an autonomous emergency safety mechanism is lacked when communication is interrupted in the prior art. According to the system and the method, a master control vehicle-mounted unit and a slave vehicle-mounted unit jointly construct a vehicle-mounted self-organizing Wi-Fi Mesh network; based on a preset priority, concurrently transmitting a first data stream for remote monitoring and a second data stream for vehicle control, the priority of the second data stream being higher than that of the first data stream; the slave vehicle-mounted unit monitors the state of a communication link between the slave vehicle-mounted unit and the master control vehicle-mounted unit, and when the communication state is lower than a preset threshold value, preset safety operation including safety braking is triggered autonomously. According to the invention, both high bandwidth and low delay can be considered, autonomous fault protection is provided, and the real-time performance and safety of remote takeover are greatly improved.

The present invention provides a distributed optimization control method for intelligent connected vehicles based on a reinforcement learning strategy. The method has a hierarchical structure and includes the following steps: establishing a vehicle longitudinal dynamics model and setting upper and lower control objectives; designing the upper layer, namely the trajectory optimization control layer; a trajectory planning controller based on a distributed convex optimization algorithm combined with the internal model principle, wherein the controller designed in combination with the internal model principle removes external interference from the upper layer trajectory planning process; designing the lower layer, namely the tracking control layer; an optimal trajectory tracking controller based on the reinforcement learning actor-critic framework; analyzing the stability and convergence of the tracking control system using the Lyapunov stability criterion to ensure accurate tracking of the reference trajectory; and verifying the feasibility of the proposed algorithm through simulation experiments. The present invention studies the trajectory planning and trajectory tracking control problems in the control of connected vehicle platoons, breaking away from the reliance on precise vehicle dynamics equations.

The embodiment of the invention relates to the technical field of vehicle interaction, and discloses an in-motorcade interaction method, device and equipment and a storage medium, and the method comprises the steps: enabling a cabin function corresponding to a target interaction option when a pilot vehicle user selects the target interaction option from a plurality of motorcade interaction options provided by a pilot vehicle; generating a first social interaction message based on a first social interaction operation performed by a pilot vehicle user in a pilot vehicle cabin; the first social interaction message is sent to a following vehicle in the motorcade, and a second social interaction message generated by the following vehicle based on the first social interaction message is received, so that social interaction between motorcade members is realized, and the second social interaction message is generated by the following vehicle after the following vehicle notifies a following vehicle user of the first social interaction message. The method is generated based on a second social interaction operation performed by the following vehicle user in the following vehicle cabin. By applying the technical scheme of the invention, the problem of insufficient information sharing in the existing vehicle formation technology can be solved.

1. Name of the product in this design: Graphical User Interface for Group Travel Management of Electronic Devices. 2. Application of this design: This design is intended for use in an electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. The product carrier of this design is a conventional design, and other views are omitted. 6. Purpose of the graphical user interface: The interface is mainly used for the interactive interface of vehicle platooning management. The main view interface is an initial interface for testing various capabilities of the team. After the test is completed, the interface changes as shown in Interface Change State Figure 1. After a period of time, the interface changes to Interface Change State Figure 2, which displays the ranking of team members. You can query and set relevant information by clicking on the relevant modules and buttons in the interface.

The present invention discloses a vehicle formation driving control method for defending against eavesdropping attacks, belonging to the field of vehicle network security technology. During the wireless transmission process of the formation vehicles, a designed fuzzy vector is introduced to effectively protect the state privacy and complete the formation control task at the same time. The method comprises the following steps: determining the lead vehicle and the following vehicle, and then determining the network topology; the lead vehicle randomly generates a fuzzy vector that meets the conditions, and transmits the false state to the following vehicle according to the formation control communication protocol; the following vehicle designs a noise vector and receives the fuzzy vector transmitted by the lead vehicle, and when communicating with the front and rear adjacent vehicles, transmits its own false state according to the designed formation control communication protocol; constructs a second-order dynamic model of the lead vehicle and the following vehicle, designs the control input based on the formation control communication protocol, and completes the formation control. The present invention has the advantages of simple operation, low computational complexity, and strong real-time performance.

The invention discloses a distributed model prediction anti-interference control method for a vehicle queue. The method comprises the following steps: obtaining a distributed model prediction controller objective function; obtaining a nominal expected acceleration control law of the distributed model prediction controller based on a KKT condition method; obtaining an uncertain disturbance value caused by unmodeled characteristics of the vehicle queue control system; calculating an expected acceleration; and carrying out distributed model prediction anti-interference control. Based on a following vehicle longitudinal dynamic model containing an uncertain disturbance term, a time lag dynamic model considering communication network time lag under an asynchronous sampling mechanism is established, and a designed compensation controller can estimate uncertain disturbance of a following vehicle caused by unmodeled characteristics of a system in real time. And the anti-interference capability of the vehicle queue controller is improved, so that the control precision, stability and robustness are improved.

Systems and methods for increasing the efficiency of vehicle platooning systems are described. In one aspect, drivers are more likely to enjoy a system if it begins platooning as desired and does not accidently end platoons. When a certain amount of data packets sent between vehicles are dropped, systems typically will either not engage in a platoon or end a current platoon. When a platoon has a very small gap between vehicles, the platoon should end—or not start, when a certain amount of packets are dropped. However, if a gap is large enough to provide a driver with more time to react, a system may accept a greater amount of dropped packets before it refuses to start a platoon or causes the end of a platoon.

Systems and methods for coordinating and controlling vehicles, for example heavy trucks, to follow closely behind each other, or linking to form a platoon. In one aspect, on-board controllers in each vehicle interact with vehicular sensors to monitor and control, for example, relative distance, relative acceleration or deceleration, and speed. In some aspects, vehicle onboard systems supply various data (breadcrumbs) to a Network Operations Center (NOC), which in turn provides data (authorization data) to the vehicles to facilitate platooning. The NOC suggests vehicles for platooning based on, for example, travel forecasts and analysis of relevant roadways to identify platoonable roadway segments. The NOC also can provide traffic, roadway, weather, or system updates, as well as various instructions. In some aspects, a mesh network ensures improved communication among vehicles and with the NOC. In some aspects, a vehicle onboard system may provide the authorization data.

Systems and methods for increasing the efficiency of vehicle platooning systems are described. In one aspect, drivers are more likely to enjoy a system if it begins platooning as desired and does not accidently end platoons. When a certain amount of data packets sent between vehicles are dropped, systems typically will either not engage in a platoon or end a current platoon. When a platoon has a very small gap between vehicles, the platoon should end—or not start, when a certain amount of packets are dropped. However, if a gap is large enough to provide a driver with more time to react, a system may accept a greater amount of dropped packets before it refuses to start a platoon or causes the end of a platoon.

To perform the row traveling of vehicles while sufficiently satisfying the taste of an occupant relating to vehicle traveling.SOLUTION: The travel support device supports convoy travel of vehicles. The driving assistance device includes one or more processors. The one or more processors are configured to acquire, from a target vehicle, traveling preference information including traveling pattern preference information related to a desired traveling pattern, the traveling pattern preference information being information indicating an occupant's preference related to vehicle traveling, and execute a vehicle-queue formation support process for supporting formation of a vehicle queue for performing platooning based on the acquired traveling preference information. The platoon formation support process includes presenting the platoon to the occupant of the target vehicle via the HMI device of the target vehicle as a candidate for performing the platooning in cooperation with the target vehicle when a matching degree of the traveling preference information between the platoon and the target vehicle is higher than a first threshold value.SELECTED DRAWING: Figure 4

The invention provides a vehicle formation control method, device and equipment and a storage medium, and relates to the technical field of automatic driving, in particular to the technical fields of vehicle formation, vehicle cloud collaboration, workshop collaboration and the like. According to the specific implementation scheme, formation control information of a target vehicle is obtained according to formation expectation information and communication topology information of a vehicle set participating in formation; and according to the formation control information of the target vehicle, controlling the target vehicle to run in the vehicle set according to the formation control target.

A vehicle row follow system may include a vehicle comprising at least one sensor to output signals serving as a basis for a three-dimensional (3D) point cloud and to output signals corresponding to a two-dimensional (2D) image of a region forward the vehicle. The system may further include a non-transitory computer readable medium containing instructions to direct a processor to: determine plan row lines in a 2D image; determine a yaw of the vehicle based upon a slope of a heading line relative to a centerline between the plant row lines in the 2D image; determine a lateral offset of the vehicle from the centerline between the consecutive plant rows based upon an identity of the consecutive plant rows in the 3D point cloud; and output steering control signals based upon the determined yaw and lateral offset of the vehicle.

A deep reinforcement learning-based intelligent vehicle platooning control method under communication topology switching involves intelligent vehicle safety and autonomous driving. 1) Real-time acquisition of driving motion information from the ego vehicle, other following vehicles, and the pilot vehicle. 2) Analysis of the required vehicle control algorithms and algorithm optimization. Using graph theory to describe the information exchange between intelligent electric vehicles, an objective function for achieving platooning control is established. 3) Considering the system's communication failure scenario, a data fusion mechanism is established. In the event of a complete communication failure, an AKF is used to reduce perception errors and achieve communication topology switching for the multi-vehicle system. 4) Addressing the numerous drawbacks of traditional platooning system control, a deep reinforcement learning process is designed to iterate the platooning control strategy and obtain the optimal control strategy. This method achieves data-driven and communication topology switching for the platooning system, improving the controller's applicability and the overall vehicle performance, and addressing traffic congestion and safety concerns.

The invention discloses a vehicle formation dynamic following time interval control method and device in an autonomous traffic environment, and relates to the field of intelligent driving data processing, and the method comprises the steps: obtaining the operation state and road condition information of a vehicle formation, and constructing a formation feature vector according to the operation state and road condition information of the vehicle formation; wherein the formation feature vector comprises formation overall riding comfort, formation overall fuel consumption, formation overall road utilization rate and formation overall safety; then, taking the formation feature vector as input, and utilizing a car following time interval prediction model to predict and obtain a comprehensive car following time interval suggested value; and sending the comprehensive vehicle following time interval suggested value to each vehicle in the vehicle formation. According to the method, the vehicle formation driving state can be dynamically optimized in real time, the riding comfort and fuel economy of vehicles in an autonomous traffic system are remarkably improved, the road resource utilization efficiency is improved, the rear-end collision risk is reduced, and the safety and efficiency of vehicle formation operation in a hybrid driving environment are improved.

The application discloses a kind of multi-dimension collaborative control method and system of heterogeneous vehicle platoon following, including the highest driving speed control of queue based on power assembly peak output power and vehicle state.The highest driving acceleration control of queue based on power assembly peak output torque and vehicle state.Heterogeneous vehicle brake pressure control based on brake system performance difference and vehicle state.Steering dynamic speed control based on the difference of heterogeneous vehicle yaw, roll stability and road curvature.Based on the above control, the driving dynamic performance of the preceding vehicle is limited, and the following vehicle will control the driving, braking and steering system of the vehicle according to the control instruction of the preceding vehicle, and make real-time adjustment according to the road condition information and vehicle distance.The application solves the limitation of the early vehicle platoon following technology for homogeneous vehicles, and coordinates heterogeneous vehicles according to vehicle state and system performance, ensuring the consistency of the queue, driving safety and turning stability during the platoon following process of heterogeneous vehicles.