208 results about "Platoon" patented technology

The invention relates to the technical field of intelligent traffic control, and discloses a self-balancing two-wheeled vehicle queue formation control system which comprises a cloud scheduling platform, a formation control unit and a vehicle end execution unit. And the cloud scheduling platform generates a global avoidance path based on the accumulated gravitational potential energy change and the pavement flatness coefficient, and executes dynamic position complementing. The formation control unit constructs a communication topology and executes formation switching based on environmental perception; the cooperative control module adopts an attitude position coupling algorithm, an expected acceleration is corrected through a gain function attenuating along with an attitude angle index, and the attitude balance is maintained preferentially when a tracking error is eliminated. The vehicle end execution unit is provided with an environment sensing and composite actuating module, and a hub motor and a control moment gyroscope are used for responding to longitudinal driving and transverse balance instructions respectively, so that dynamic decoupling is achieved. According to the invention, the control conflict between two-wheeled vehicle formation trajectory tracking and attitude stability is effectively solved, and the driving safety of the system is improved.

The embodiment of the invention relates to the technical field of intelligent driving, and discloses a motorcade passing control method, device and equipment based on global planning, and the method comprises the steps: obtaining the driving related information of vehicles in a motorcade; the motorcade comprises a plurality of vehicles, and the driving related information comprises at least one of road condition information, vehicle driving information and map navigation information; according to the driving related information of at least one vehicle in the motorcade, performing global planning by taking the vehicles in the motorcade not to fall behind or the fallen vehicles to get up to the motorcade as a control target, and determining a target state of a target vehicle in the motorcade; the target state indicates driving information needing to be executed by a target vehicle, and the target vehicle is any vehicle in a motorcade; and sending the target state of the target vehicle to the target vehicle to control the target vehicle to be switched from the current state to the target state. By applying the technical scheme of the invention, the current problems that the formation vehicles are easy to fall behind and are difficult to recover because of being cut off in the complex urban road can be solved.

The invention discloses an unmanned vehicle queue control method based on physical information reinforcement learning, and the method comprises the steps: building a reinforcement learning environment of an unmanned vehicle queue, and enabling a queue following vehicle to plan the acceleration according to the acceleration change of a leading vehicle; state information of each vehicle is obtained, a TD3 algorithm is used for carrying out training iteration on the model, in the training iteration process, the expected acceleration obtained through calculation of the CACC model is embedded into a loss function of a strategy network Actor to serve as physical information constraint, and a strategy is guided to generate an action conforming to vehicle dynamics; and after training iteration is finished, deploying a control strategy of the strategy network Actor in an unmanned motorcade simulation environment to realize cooperative control of the motorcade. According to the method, physical information constraints are introduced, so that the model training speed is increased, the stability and interpretability of a reinforcement learning control strategy are improved, and the queue operation efficiency and stability are enhanced.

The invention provides a multi-pilot vehicle formation control method based on formation graph connectivity judgment, and the method comprises the steps: determining the motion state of each target vehicle in a vehicle formation when the formation operation mode of the vehicle formation is a single-pilot mode; if it is determined that a target vehicle in the vehicle formation is abnormal according to the motion state, splitting the vehicle formation into a plurality of sub-formations in a multi-navigation mode; and when each sub-formation satisfies a preset formation fusion condition, recombining the sub-formations into a vehicle formation in the single navigation mode. The method is used for overcoming the defect that in the prior art, the whole formation loses efficacy when the vehicles cannot continue to follow stably in the vehicle formation in a single-pilot mode.

The invention discloses a fleet safety reinforcement learning robust formation control method fusing deep heterogeneous features. The method comprises the following steps: step 1, establishing a heterogeneous motorcade dynamic model; step 2, isomerism feature extraction based on LSTM (Long Short Term Memory); step 3, designing a Lyapunov safety reinforcement learning vehicle following strategy; and step 4, carrying out Lyapunov-PPO training optimization and convergence guarantee. According to the invention, the dynamic time-varying features of each vehicle in the heterogeneous motorcade are accurately extracted and tracked through the LSTM network, and adaptive control of different types of vehicles is realized; a Lyapunov safety constraint mechanism is introduced, collision-free guarantee is embedded in the reinforcement learning decision process, and the driving safety is improved; robustness of the system to external disturbance and internal dynamic change is enhanced, and it is ensured that a fleet can still keep stable formation in a complex driving environment.

The invention discloses an intelligent truck formation transportation control system based on cloud side-end cooperation, and the system achieves the cooperative control through a three-stage architecture of a cloud large model cognition center, an edge-side intelligent control center and an intelligent terminal with a body. Comprising a cloud large model cognition center, an edge side intelligent control center and an emergency response module driven by integrated fuzzy logic of an intelligent terminal system with a body. The invention also discloses an intelligent truck formation transportation control method based on cloud side-end cooperation, and the method comprises the steps: firstly deploying a large model at a cloud end, achieving the domain adaptation through the LoRA fine tuning, analyzing task semantics, and outputting feature parameters; secondly, a vehicle end needs to integrate a multi-mode sensor and a fuzzy controller, a sensing-control local closed loop is established, and it is guaranteed that when communication is interrupted, local safety takeover is achieved through the fuzzy controller; and finally, cloud-edge-end strategy alignment is realized, and autonomous operation in a network disconnection scene is supported. The problems of cognitive decision separation, high communication dependency, insufficient security redundancy and the like in a traditional method are solved.

The invention relates to an airport snow sweeper cooperative formation control method, electronic equipment and a medium, and the method comprises the steps: building a vehicle transverse and longitudinal coupling kinematic model in an airport snow removal environment based on environmental parameters, the vehicle transverse and longitudinal coupling kinematic model comprises transverse and longitudinal physical quantity cross terms, enabling the longitudinal speed to affect the transverse posture change, and enabling the longitudinal speed to affect the transverse posture change; the transverse path characteristics and the control input restrict the longitudinal motion; constructing a formation constraint model based on the vehicle transverse and longitudinal coupling kinematics model; constructing a distributed communication topology model based on a directed graph and an adjacent matrix; constructing a single vehicle local optimization control problem model; and iteratively solving the single vehicle local optimization control problem model based on a path guiding type distributed model predictive control algorithm to obtain the optimal control input of each vehicle. According to the method, control input can be optimized while accurate path tracking of the snow sweeper is ensured, energy consumption and cost are reduced, and collaboration, safety and efficiency of snow removal operation of an airport can be improved.

The invention provides a method for determining a motorcade driving strategy and electronic equipment, and relates to the technical field of path planning, and the method comprises the steps: firstly, determining a comprehensive operation index of a motorcade based on cooperation data and a basic operation index of the motorcade; secondly, the collaborative data and interaction data and prior data of the motorcade are fused to obtain fused data, the interaction data are data of the current running state of the vehicle and the road condition of the environment where the vehicle is located, and the prior data are used for representing historical driving data matched with the current scene; and finally, based on the fusion data, the comprehensive operation index and the path planning data of the motorcade, determining a target driving strategy of the motorcade, the path planning data being a constraint for supporting path planning. The technical problem that in the prior art, cooperative requirements of distance control, path conflict avoidance and the like of the motorcade in an off-road environment are not considered, so that the overall passing efficiency and safety of the motorcade are insufficient is solved.

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

The invention provides a forward collision risk processing method and device, equipment and a medium, and relates to the technical field of auxiliary driving, and the method comprises the steps: forming a forward target sequence through synchronously obtaining the speeds, accelerations and vehicle distances of a vehicle and a plurality of front vehicles, and constructing a relative speed, relative distance and longitudinal acceleration time sequence according to a time dimension; calculating nominal collision time and a time margin based on the motion relation between the two vehicles, and introducing a motorcade compression index and a deceleration wave intensity index to correct the nominal collision time so as to obtain effective collision time; collision hazards are quantified according to a collision relative speed, a compression index and a deceleration wave intensity index, a controllability score is constructed in combination with effective collision time and a time margin, then a severity score and the controllability score are mapped into discrete risk levels, and differential active safety measures such as transverse avoidance are triggered according to different risk levels. The method can simultaneously consider the longitudinal compression trend of the motorcade and the deceleration wave cumulative effect to trigger the evasion strategy.

The invention provides a wireless communication method based on a cellular Internet of Vehicles technology, a communication terminal and a vehicle, and relates to the technical field of vehicle communication. The method comprises the following steps: adding a first vehicle-mounted communication terminal and a second vehicle-mounted communication terminal of a target following vehicle in preset formation vehicles to a communication network of a target multicast group, and generating a target multicast routing table, so that a leading vehicle adopts a multicast communication mode, the multicast data can be sent to the second vehicle-mounted communication terminal of each target following vehicle in a targeted manner through the first vehicle-mounted communication terminal, so that the data is prevented from being sent to other vehicles. Due to the adoption of the multicast communication mode, the vehicle-mounted communication terminal of any vehicle in the target multicast group sends the multicast data, and the vehicle-mounted communication terminals of the other vehicles can receive the multicast data, so that unnecessary repeated transmission is avoided, the risk of network congestion is remarkably reduced, the information spreading efficiency is improved, and the user experience is improved. And meanwhile, all target following vehicles can receive the latest instruction in time, and synchronization and stability of a motorcade are maintained.

The invention discloses a networked vehicle queue safe cruise method for coping with hybrid network attacks, the vehicle queue comprises a head vehicle and a following vehicle, the following vehicle collects the running state data of the vehicle, obtains the running state data of other vehicles through a communication network, continuously detects the state of the communication network, and sends the detected state to the vehicle queue. Detecting whether a network attack is currently suffered or not and the type of the suffered attack, and calculating the minimum characteristic value of the topological matrix in a normal communication state, a spoofing attack state or a replay attack state according to the identified attack type; and then solving the constructed linear matrix inequality which enables the vehicle closed-loop system to be stable to obtain a feedback gain, and generating a control input signal according to the feedback gain obtained by calculation to realize accurate control of the vehicle. The feedback gain is dynamically adjusted according to the real-time state of the vehicle, so that the safety cruise control of the vehicle queue system is more effectively realized.

The invention provides an automatic driving formation hardware-in-the-loop simulation platform based on a satellite position simulation platform and a medium, and relates to the technical field of automatic driving and car networking simulation. The platform comprises a satellite position simulation module, a traffic simulation platform, a hardware verification platform, a simulation bus and a clock synchronization module. The satellite position simulation module generates satellite data with a timestamp; the traffic simulation platform constructs a formation driving scene and operates a formation control algorithm; the hardware verification platform comprises a real phased-array antenna module and a PC5 communication device. The modules exchange data through a simulation bus, and the clock synchronization module unifies the time reference to form a closed-loop verification environment of simulation hardware simulation. The problems that space-based data and ground simulation are separated and real communication characteristics are difficult to quantitatively evaluate in the prior art are solved, the satellite-ground cooperation and formation control strategy can be efficiently and reliably verified in a laboratory environment, and the real vehicle test cost and risk are remarkably reduced.

This invention discloses a system and method for lane-changing and clustering of connected autonomous driving fleets in bottleneck areas. It generates corresponding lane-changing and clustering schemes based on the type of vehicle ahead, calculates the lateral offset of the vehicle ahead, and utilizes a cooperative search intelligent optimization algorithm to improve the accuracy of the lateral offset. The impact values ​​of collision factors and the safety thresholds of collision impact factors are input into a collision risk assessment model to determine whether there is a collision risk, thereby controlling the connected autonomous driving fleet. Finally, based on an information matrix, the types of lagging vehicles in the target lane are discussed to complete the lane-changing and clustering process. This invention can improve traffic efficiency in bottleneck areas while ensuring driving safety.

A method for automated vehicle driving control in a platoon includes obtaining first operational parameters of a first vehicle, including at least a first measured braking performance of the first vehicle, obtaining second operational parameters of a second vehicle, including at least a first measured braking performance of the second vehicle, determining a platoon sequence according to the operational parameters by assigning whichever of the first vehicle and the second vehicle that has a lesser measured braking performance as a lead vehicle in the platoon sequence, and assigning the other of the first vehicle and the second vehicle as a follower vehicle in the platoon sequence, initiating a platoon sequence driving route, and controlling automated driving of the follower vehicle according to tracking of the lead vehicle, wherein the automated driving includes automated steering control, automated acceleration and automated braking based on tracked movement of the lead vehicle.

The application discloses a kind of anti-network attack's multi-vehicle safety platoon control method and system, the method first establishes multi-vehicle queue dynamics model;Second, design fuzzy state observer to estimate vehicle state and unknown nonlinear dynamics;Then, introduce funnel function to build time-varying performance boundary, and the double constraints of vehicle anti-collision and keep communication are converted into funnel constraints of spacing tracking error;Then, based on backstepping method and funnel performance error, design virtual controller and adaptive law, and obtain adaptive safety controller to obtain control input;Finally, through Lyapunov stability analysis, the tolerance threshold condition that system can still maintain final consistent bounded stable after suffering replay attack is quantitatively obtained.The application simplifies design through funnel control, avoids complex barrier lyapunov function, and for the first time clearly provides quantified anti-interference ability to replay attack, enhances the network security and practicality of multi-vehicle queue system.

The embodiment of the invention discloses a vehicle control method and device based on laser communication, equipment and a storage medium, belongs to the technical field of vehicle control, is applied between communication nodes including a vehicle-mounted terminal, and is characterized in that a wavelength division multiplexing-based multi-channel MIMO communication module is configured between the communication nodes, and the method comprises the following steps: obtaining driving information of an ith vehicle; determining decision information for the i-th vehicle based on the driving information; and sending related information of the decision information to two vehicles adjacent to the i-th vehicle in the motorcade through the laser array in the i-th vehicle. Communication is achieved through the multi-channel MIMO communication module, the problems of communication interruption and high time delay caused by electromagnetic interference, high-concurrency channel blocking and the like in communication can be effectively solved, the communication stability is improved, the time delay is reduced, stable links and low bit error rate can be kept in a complex traffic environment, and the communication efficiency is improved. Decision information of the vehicle is expressed to the adjacent vehicle in time, and consumption of hardware computing power is effectively reduced.

This invention relates to the field of intelligent transportation and road engineering technology, specifically disclosing a method and system for splitting and assembling modular vehicle platoons on mountainous highways. First, it acquires real-time road element parameters, platoon perception characteristic parameters, and operational status parameters. Then, it calculates the available line-of-sight (CASD) for platoon cooperation, establishing a multi-factor nonlinear platoon drivability index (CDI) model with CASD as the core constraint to complete the spatiotemporal partitioning of road segment risks. Subsequently, it constructs a marginal benefit decision-making model to achieve forward-looking decision-making for splitting and assembling. Finally, it formulates a splitting strategy matching the cooperative perception boundary and a drivability recovery-oriented assembly strategy to achieve dynamic adaptation of the platoon structure to the traffic conditions of mountainous roads. This invention solves the shortcomings of existing technologies, such as poor scenario adaptability, delayed passive response, and failure to consider platoon perception characteristics. It can be directly applied to the engineering practice of autonomous driving freight platooning on mountainous highways, balancing driving safety and traffic economy.

A message transmission method performed by an edge computing device includes receiving vehicle communication messages transmitted by a core network user plane device in a core network. Each of the vehicle communication messages is transmitted through a communication network to the core network by a vehicle-mounted terminal mounted at one of platooning vehicles in one or more vehicle platoons. The method further includes performing message scheduling on the vehicle communication messages, and transmitting the vehicle communication messages subjected to the message scheduling to the core network user plane device, to enable the core network user plane device to forward the vehicle communication messages through the communication network. Each of the vehicle communication messages is forwarded to one or more vehicle-mounted terminals other than the vehicle-mounted terminal that transmits the vehicle communication message.

The application relates to the technical field of automobile auxiliary intelligent driving systems, in particular to a control method and device for automobile platooning, a vehicle and a storage medium, which comprise the following steps: identifying the platooning type of automobile platooning; if the platooning type is a driving type, when it is identified that the vehicle has a position conversion requirement, generating a position conversion action according to the platooning position information and the expected platooning position of surrounding vehicles, and controlling the vehicle to convert to the expected platooning position based on the position conversion action; if the platooning type is a charging type, broadcasting the charging requirement information of the vehicle to the automobile platooning, calculating the charging requirement degree of each vehicle according to the charging requirement information of all the vehicles in the automobile platooning, generating a charging matrix based on the charging requirement degree, and controlling the vehicles in the automobile platooning to charge when the charging station is idle. Therefore, the problems that the automobile cluster driving process and energy supplement cannot be uniformly assisted, managed and controlled in platooning, the user's driving safety is reduced, and the user's experience is reduced are solved.

A system includes sensors that obtain occupant data of an occupant within the ego vehicle. The ego vehicle is part of a platoon of vehicles. The system includes one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations. The operations include determining, based on the occupant data, an impairment status of the occupant; and in response to determining the impairment status, selectively implementing an action to address the impairment status. The action includes communicating, using one or more coded status messages, at least a portion of the impairment status to one or more other vehicles in the platoon, and implementing a navigation action of the ego vehicle.

The application discloses a road friction estimation method and device combined with vehicle platoon and extended Kalman filter, and the method comprises the following steps: designing state equation and measurement equation of road friction estimation; all the vehicles in the vehicle platoon except the tail vehicle send the estimation information of the road adhesion coefficient to the following vehicle through vehicle-to-vehicle communication; all the vehicles in the vehicle platoon except the head vehicle determine whether to use the estimation information of the front vehicle according to the estimation information of the road adhesion coefficient; all the vehicles in the vehicle platoon estimate the current road adhesion coefficient by using the extended Kalman filter, and the estimation process is to solve the state equation and the measurement equation based on the estimation information of the road adhesion coefficient at the last moment. The application designs the extended Kalman filter with a judgment module, and the extended Kalman filters of the platoon members are combined through vehicle-to-vehicle communication of the vehicle platoon, so that the estimation speed and precision of the following vehicle for the road adhesion coefficient are improved.

The invention provides a formation anti-jam method and system based on intelligent network connection, a vehicle and an electronic device, and the method comprises the steps: obtaining the driving state information of an automatic driving formation and a side lane parallel vehicle, judging whether the side vehicle has a jam state or not according to the driving state information, and when the side lane parallel vehicle has the jam state, stopping the jam. When the distance between the vehicle head and the traffic jam adding lane line is within a preset range, executing at least one of the following steps: sending warning information to parallel vehicles on a side lane; a protection device at the rear end of a front vehicle in the automatic driving formation is controlled to stretch out; and controlling a protection device at the front end of a rear vehicle in the automatic driving formation to extend. According to the method, the driving state information of the parallel vehicles on the side lane is monitored in real time, the plugging intention is accurately judged, measures can be taken in time at the initial stage of the plugging behavior, the stable driving state of the automatic driving formation vehicles is effectively maintained, and formation interruption or disordered driving order caused by the plugging behavior is reduced.

The application belongs to the technical field of vehicle control, and provides a multi-vehicle longitudinal formation robust control method and system based on knowledge transfer, which comprises the following steps: constructing a generalized nonlinear multi-vehicle longitudinal system model of heterogeneous vehicles; mapping an original constrained tracking error to an unconstrained tracking error based on a dynamic design of a segmented performance function of the original constrained tracking error; constructing a coupled sliding mode surface based on the unconstrained tracking error, designing a multi-vehicle longitudinal formation robust controller and an uncertain parameter adaptive law; collecting input data and output data of a first following vehicle under the multi-vehicle longitudinal formation robust controller, and introducing historical brake measurements as additional input data; training a long short-term memory network and performing iterative optimization; and transferring the knowledge in the trained long short-term memory network to other following vehicle control systems. The application solves the problems of low transient response rate, high design complexity, and a large number of parameter configurations in the traditional method.

A control method of mixed traffic flow on freeway ramp based on controllable connected and autonomous vehicles (CAVs) is provided. A ramp is divided into a normal driving section, a vehicle platoon formation section and an accelerating and merging section. A vehicle platoon is formed by a leading CAV and human-driven vehicles (HDVs). Time interval [tmin, tmax] for the vehicle platoon to completely reach the merging point S is calculated. CAVs on the main lane and ramp are cooperatively controlled, and a merging gap is reserved for the ramp vehicle platoon. The vehicle platoon is allowed to accelerate and merge into the main lane. By means of the Internet-of-Vehicle (IoV) technology, the traffic situation on the main lane and downstream merging zone can be obtained in advance, and speeds of the CAVs are cooperatively controlled to lead the ramp vehicles to safely merge into the main lane.

This invention discloses a method for calculating the fuel consumption saving rate of a vehicle platoon based on topology and dynamic characteristics. The method includes establishing a vehicle dynamics model, an external flow field model, and a mathematical analysis model of the fuel consumption saving rate of the vehicle platoon. The proposed method conducts simulation experiments for different parameters such as longitudinal vehicle spacing, lateral vehicle offset distance, and vehicle speed. By analyzing the influence of longitudinal vehicle spacing, lateral vehicle offset distance, and vehicle speed on the vehicle's aerodynamic drag coefficient, the functional relationship between the vehicle platoon's aerodynamic drag coefficient and the above parameters is determined. Furthermore, the functional relationship between the aerodynamic drag coefficient and the fuel consumption saving rate is determined to calculate the vehicle platoon's fuel consumption saving rate.

The application belongs to the technical field of industrial process control and vehicle networking cooperative control, and discloses a vehicle networking cooperative vehicle fleet self-triggered formation control method under denial of service attack conditions, which comprises the following steps: constructing a state space equation of cooperative vehicles, and obtaining expected formation states and actual states of a leading vehicle and a following vehicle; step 2, modeling possible denial of service attacks in vehicle networking; step 3, designing a self-triggered control update strategy for each following vehicle in a time interval in which communication is normal and the communication topology of the fleet remains connected; step 4, based on steps 1, 2 and 3, constructing a fleet consistency controller, and under the condition that the denial of service attack satisfies the frequency and duration constraint conditions, the fleet system meets the control requirements. The application reduces the communication and calculation burden of the fleet, improves the safety and robustness of the system under the conditions of communication attack, limited bandwidth and unstable network, and is suitable for cooperative fleet formation control scenes under various vehicle networking topological structures.

The application provides a vehicle formation control method and device, electronic equipment and medium. The vehicle formation includes a head vehicle and at least one following vehicle. The method is applied to the head vehicle. The method includes: obtaining real-time performance parameters of each following vehicle to calculate a following weight of the following vehicle according to the real-time performance parameters; receiving a target safety distance reported by each following vehicle, and generating a control instruction for coordinating formation driving according to the following weight and the target safety distance of the following vehicle, and sending the control instruction to the following vehicle; when a following vehicle in a formation reorganization scene is identified, generating a formation reorganization instruction according to a scene type corresponding to the formation reorganization scene and the following weight of the following vehicle in the formation reorganization scene, and sending the formation reorganization instruction to the corresponding following vehicle to adjust the formation shape. The application improves the overall coordination of the vehicle formation.

Platoons of vehicles are operated remotely by a teleoperator. A lead vehicle of a platoon establishes communications with a teleoperator station, which receives drive state data from the lead vehicle and generates reference instructions for the lead vehicle. When a lead vehicle is under control of a teleoperator, and a follower vehicle joins a platoon with the lead vehicle, the follower vehicle receives drive state data from the lead vehicle and reference instructions for the lead vehicle from the teleoperator station. The follower vehicle captures data regarding its own drive state, and generates instructions based on the drive state data of the lead vehicle and the reference instructions for the lead vehicle. Drive state data of a lead vehicle of a platoon may be estimated, augmented or refined for accuracy based on drive state data of other vehicles of the platoon.