446 results about "Lead vehicle" patented technology

The invention discloses an unmanned vehicle formation cooperative hunting method, device and equipment based on pilot following, and a medium, and relates to the technical field of multi-agent control. The method comprises the following steps: determining role division of a leading vehicle and a following vehicle, planning a path by the leading vehicle through an improved fast random exploration tree algorithm, sending the path and a control strategy to the following vehicle in stages, dynamically adjusting the position and speed by the following vehicle according to an instruction, ensuring formation consistency and task execution, and realizing information sharing between unmanned vehicles through a communication network. The pilot vehicle broadcasts a path and a task instruction in real time, and the following vehicles receive and analyze the path and the task instruction and then dynamically adjust the motion trail, thereby ensuring the cooperative consistency of the formation and the efficient stability of information transmission; each vehicle executes a hunting task according to a task instruction and adjusts a motion track and speed in real time, the following vehicle keeps a formation according to a path instruction, the pilot vehicle dynamically adjusts a global path according to environment change and a target position, the instruction is updated in real time through a communication network, and finally effective hunting and capturing of a target are realized. The method is suitable for a target hunting task in a complex environment, and reliable technical support is provided for a multi-target complex scene.

A hitch point where a receiving vehicle is coupled to a following vehicle is located relative to a leading vehicle. The trajectory or route of the following vehicle is detected and a dynamic model estimates a location of a receiving area in the receiving vehicle relative to the leading vehicle based on the trajectory or route of the following vehicle and the location of the hitch point relative to the leading vehicle.

The disclosure relates to the system and methods for generating a guidance path for automated navigation of large agricultural equipment. Vehicle path data from leading vehicles can be recorded, stored or inputted into the system for generation of guidance paths offset from the swaths traversed by the leading vehicle. Combines, planters sprayers and other vehicles utilizing guidance system can be used to record leading vehicle data for use by the following vehicle in real time or in subsequent cycles. Enterprise systems can manage multiple grain carts and / or combines or other vehicles to maximize efficiency.

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.

The invention discloses a cooperative control method for a heavy haul railway long and large combination train based on a coupler force closed loop in order to solve the problem that operation safety is threatened due to unstability and breakage of a coupler caused by overlarge longitudinal impact force in the traction braking process of the long and large marshalling train. According to the method, coupler force is introduced into a control system of a locomotive to form a unit train coupler force closed-loop control system in a marshalling train, and traditional locomotive control is changed into train control. According to the method, the purpose of minimizing coupler force of slave control locomotives and front locomotives in a group is achieved, a distributed cooperative control strategy is adopted for unit trains in the group, the purpose of equivalently decomposing the long and large heavy-load combined train into unit trains running independently is achieved with the fact that stress of front coupler of each slave control locomotive is almost zero. And automatic driving operation of the unit trains in the marshalling is realized. By recognizing the stress change trend of the couplers and adopting a coupler force closed-loop active control mode, the overlarge impact force of the train couplers is relieved, and the running safety of the whole heavy-load train is guaranteed.

The invention provides a method and a device for controlling and testing formation driving of autonomous vehicles. The method comprises the following steps: acquiring current scene features based on a sensing device configured on a road side unit and / or an autonomous vehicle; the current scene feature is matched with a preset scene library, target formation control information is obtained, the preset scene library stores a mapping relation between multiple groups of scene features and formation control information, and the target formation control information comprises pilot vehicle control information and control information corresponding to at least one following vehicle; and based on the target formation control information, controlling the leading vehicle and the following vehicle to perform formation driving. According to the method and the device, the formation scene of the automatic driving vehicles in multiple scenes such as expressways is introduced, the problems of scattered test organization and limited scene coverage in the traditional road test are solved, multi-source heterogeneous sensing fusion dynamic correction can be constructed, and the formation cooperative control of the automatic driving vehicles in the high-speed environment is realized; the problem of perception degradation caused by failure of a single sensor is solved.

The invention discloses an optimized calculation method and system for the following distance of an unmanned vehicle team during running, and a medium. The method comprises the following steps: analyzing the quantitative change characteristics of air resistance coefficients and lift coefficients of vehicles in the unmanned vehicle team; calculating the minimum following distance of the following vehicle based on the quantitative change characteristics, the nonlinear dynamic model and the prediction iteration of the time-varying state data of the vehicle; according to the minimum car following distance, a car following distance optimization interval is determined, and influence parameters between the car following distance and the braking margin, the energy consumption, the lift coefficient and the tracking capacity of the front car are analyzed; in the car following distance optimization interval, dynamically calculating the optimal car following distance of the following car according to the influence parameters; according to the method, the air resistance coefficient and lift coefficient quantitative rule of the formation vehicles can be obtained, the vehicle speed, the road resistance, the transmission efficiency and the driving / braking element response characteristics are comprehensively considered, and the optimal following distance of each following vehicle is dynamically predicted in real time and iteratively calculated.

The invention discloses an accident risk assessment method considering risk avoiding behavior heterogeneity. The method comprises the following steps: acquiring trajectory data of a vehicle and preprocessing the trajectory data to enhance trajectory quality; driver risk avoiding behavior characteristics are extracted; the method comprises the following steps: carrying out probability modeling on response time, maximum deceleration and maximum jerk of a driver by utilizing relative motion state and risk avoiding behavior information among vehicles and adopting a Monte Carlo simulation method, and calculating probability collision time PCT; and comparing the calculated PCT value with a preset risk threshold value, and performing accident risk assessment in real time. According to the method, dynamic risk avoiding behaviors of a front vehicle and a rear vehicle in a real collision scene are brought into a risk assessment model, and a collision risk curve with a confidence interval is generated through parameterized modeling of heterogeneity distribution of response time, maximum braking deceleration and jerk. Compared with a traditional time collision index, the method breaks through a homogeneous driving behavior hypothesis, and can remarkably improve the accuracy and timeliness of risk perception in a complex traffic scene.

The embodiments of the present application relate to autonomous mobile apparatuses. Provided are a multi-apparatus cooperative control method, apparatus and system, and a storage medium. The method comprises: in response to being determined as a lead vehicle of a platoon and receiving a cooperative task, planning a task trajectory on the basis of the cooperative task, wherein the platoon comprises one lead vehicle and at least one following vehicle; publishing the task trajectory and an execution starting time of the cooperative task to the at least one following vehicle, such that the at least one following vehicle plans their respective following trajectories on the basis of the task trajectory, and executes the respective following trajectories upon reaching the execution starting time of the task trajectory; and executing the task trajectory upon reaching the execution starting time of the task trajectory, such that the lead vehicle and the at least one following vehicle maintain platoon synchronization for task execution. Therefore, a cooperative operation with a plurality of apparatuses integrated into a whole is realized, thereby expanding the application scenarios of cooperative collaboration of the plurality of apparatuses.

The invention provides a virtual marshalling train interval prediction method and system based on a high-order extended Kalman smoother, and belongs to the technical field of train operation management, real-time state data of a train in marshalling and historical state data of a leading train are obtained, and the state data comprise speed data, position data and acceleration data; on the basis of the obtained real-time state data of the vehicle and the historical state data of the leading vehicle, a high-order extended Kalman filter is adopted to predict the dynamic state of the vehicle; and based on the state after smooth optimization, dynamically calculating the train spacing based on the difference value of the position states of the leading train and the following train. According to the method, the real-time dynamic tracking of the train state and the high-precision estimation of the distance between the train and the head train are realized, and through the definition of the high-order hidden variables and the pseudo-linearization representation of the high-order hidden variables, the description capability of the nonlinear characteristic of the train system is improved, the calculation complexity of high-order expansion is reduced, and the robustness and the stability of the virtual coupling train system are ensured.

The invention discloses an automatic driving processing method and device based on an intelligent automobile, a terminal and a medium, and relates to the technical field of intelligent automobile automatic driving, and the method comprises the steps: controlling real-time monitoring of surrounding environment data when it is detected that the automobile completes path planning and starts an intelligent driving mode; performing communication handshake with surrounding vehicles within a preset distance range during driving, and negotiating to enter an intelligent driving joint mode when detecting that paths are overlapped; when the vehicle agrees to enter the joint mode, the intelligent vehicle is controlled to enter a running accompanying mode, and front and rear vehicles are controlled to keep the same speed and share sensor data; in the running accompanying mode, the front and rear vehicles share sensor data and synchronously advance by keeping a safe vehicle distance together; if the current automobile detects that the abnormal situation needs to be avoided, the running-accompanying intelligent automobile is controlled to synchronously complete the avoidance action; and when the rear vehicle detects that an abnormal vehicle overspeeds and approaches, the running-accompanying intelligent vehicle is controlled to synchronously accelerate and avoid. The method has the advantages that the multi-vehicle cooperation efficiency is improved, and the road safety is enhanced.

A method for longitudinal guidance of a vehicle relative to a leading vehicle driving in front by using an electronic distance-keeping system, includes providing an environmental parameter relating to an environment of the vehicle, providing a target distance at which the vehicle is intended to follow the leading vehicle, providing acceleration and / or braking information relating to the leading vehicle, assigning the leading vehicle to one of at least two different, predefined driving behavior classes based on the environmental parameter and the acceleration and / or braking information, and determining an at least temporarily permitted deviation from the target distance based on the assigned driving behavior class. The distance-keeping system is used to at least temporarily longitudinally guide the vehicle as following the leading vehicle based on the target distance and the permitted deviation. A distance-keeping system for a vehicle is also provided.

Certain aspects of the present disclosure provide techniques for enhancing vehicle operations safety using coordinating vehicle platooning or enhancing platooning safety against location spoofing attacks. In one example, a source user equipment (UE) detects a potential spoofing event associated with location information being altered in an unauthorized manner, the source UE may transmit a request to a platoon control system (PCS) to join a vehicle platoon. In another example, a first UE associated with a lead vehicle in an existing platoon may detect a potential spoofing event associated with location information being altered in an unauthorized manner. The lead vehicle may transmit to a second UE of another vehicle in the platoon an indication of the detection and a request to exchange the respective roles in the platoon. The PCS may also monitor the conditions of the first and the second UEs, and arrange for the platoon reorganization.

The present disclosure provides a method in a data processing system that includes at least one processor and at least one memory. The at least one memory includes instructions executed by the at least one processor to implement a vehicle overtaking system. The method includes causing a vehicle control system in an ego vehicle to execute at least a portion of an input control sequence, receiving, from a first plurality of sensors coupled to the ego vehicle, lead vehicle data about a lead vehicle, estimating an intention of the lead vehicle based on the lead vehicle data, and causing the vehicle control system to perform a vehicle maneuver based on the intention of the lead vehicle.

The invention discloses a driving capability improvement control method and system for an unmanned vehicle. The method comprises the step of realizing road condition perception and tire pressure optimization through vehicle cloud cooperation. A front vehicle collects road surface data and uploads the road surface data to a cloud server after preprocessing, and the server carries out aggregation analysis and constructs and updates a road surface state map. The rear vehicle requests road condition data, verification is carried out, remote data and local data are fused after weight distribution, and road surface state estimation is generated. And calculating a road surface friction coefficient and a weather influence factor based on the fused data, processing tire temperature data, monitoring a load-carrying vehicle speed, formulating an adjusting strategy, and carrying out modeling solving to obtain the optimal tire pressure. And an adjusting strategy is generated according to the optimal tire pressure, differential tire pressure control is implemented, adjustment is executed through closed-loop and predictive control, the effect is monitored in real time, and data are uploaded to a cloud optimization system. The control stability and trafficability of the vehicle under complex road conditions are effectively improved, and driving safety and riding comfort are guaranteed.

A system and method for generating a guidance path for automated navigation of large agricultural equipment. Vehicle path data from leading vehicles can be recorded, stored or inputted into the system for generation of guidance paths offset from the swaths traversed by the leading vehicle. Combines, planters sprayers and other vehicles utilizing guidance system can be used to record leading vehicle data for use by the following vehicle in real time or in subsequent cycles. Enterprise systems can manage multiple grain carts and / or combines or other vehicles to maximize efficiency.

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.

A system may receive a video stream from a camera of a vehicle in a transportation network. The system may also receive an input indicating an acceleration and a steering angle of a simulated lead vehicle. The system may determine a pose of the simulated vehicle relative to a home pose based on the acceleration input and the steering input, and display an overlay of a representation of the simulated vehicle in the video stream at pixel coordinates based on the pose. The system may determine, from the pixel coordinates, spatial coordinates of the simulated vehicle in the transportation network, wherein the spatial coordinates are relative to at least one of the transportation network or the camera. The system may transmit the spatial coordinates to the vehicle to cause the vehicle to follow a path based on the spatial coordinates.

In response to an initiation of a lane-change, a speed of a host vehicle in a current lane can be adjusted to establish a forward longitudinal distance from a lead vehicle in a target lane. The host vehicle is then moved from a first lateral position to a second lateral position in the current lane based on determining that a rear longitudinal distance of the host vehicle from a trailing vehicle in the target lane is less than a rear distance threshold, and then the host vehicle is additionally moved from the second lateral position to a third lateral position in the current lane based on determining that the rear longitudinal distance is increasing. When the forward longitudinal distance exceeds a forward distance threshold and the rear longitudinal distance exceeds the rear distance threshold, the vehicle is moved from the current lane to the target lane.

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.

A multi-vehicle control system (MVCS) for control of a multi-vehicle system having independently powered and steered lead and trailer vehicles connected by a slidable length sensing tow bar system. The MVCS includes a controller configured to receive throttle, brake, and steering signals from the lead vehicle, receive signals from the tow bar system indicating an extension length of the tow bar system, a front angle deviation of the lead vehicle, and a rear angle deviation of the trailer vehicle. The controller is further configured to determine, based on the received signals, a trailer vehicle acceleration / deceleration output and a trailer vehicle steering output, determine a driving scenario of the multi-vehicle system, and operate the multi-vehicle system based on the determined driving scenario and the determined trailer vehicle acceleration / deceleration output and trailer vehicle steering output.

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.

The invention discloses an intelligent network connection vehicle formation distributed cooperative control method, system and device based on mode switching, a vehicle driving mode is divided into a single vehicle cruising mode and a formation following mode, and in the single vehicle cruising mode, a driving risk field model and model prediction control are utilized to enable a vehicle to move along a risk minimum path; and in the formation following mode, the safe distance between each vehicle and the front vehicle is kept through distributed model prediction control, and stable following motion is realized. Meanwhile, a state set is constructed in combination with a sensor and the Internet of Vehicles, the collision risk of adjacent lanes is detected in real time based on risk assessment of a fuzzy theory, and when the risk reaches a threshold value, the vehicle is converted from following to single vehicle cruising; after the queue is divided into two sub-queues, the front pilot car generates an attraction potential field to the rear pilot car, the two sub-queues gradually approach each other, and when the following condition is met, the rear pilot car is converted from single car cruising to formation following. And finally, based on a finite-state machine thought, a cooperative control system is constructed to flexibly adjust the driving mode.

An autonomous vehicle may include one or more sensors configured to detect at least one vehicle, memory, and a processor. The processor may be configured to set a lead vehicle, of the at least one vehicle, as a target vehicle. The lead vehicle may be traveling ahead of the vehicle. The processor may be further configured to: receive, via the one or more sensors, driving information associated with the target vehicle; determine, based on the driving information and a predetermined cut-out condition, a likelihood of the target vehicle cutting out of a lane; and control, based on the determined likelihood, the vehicle to operate in a safety control mode of a plurality of safety control modes.

Systems, methods, and other embodiments described herein relate to improving passenger compartment air quality in a vehicle. In one embodiment, a method includes, responsive to acquiring sensor data that includes at least air quality information about a surrounding environment of an ego vehicle, analyzing the sensor data to determine whether the air quality information satisfies an action threshold specifying that air around the ego vehicle is of a poor quality. The method includes identifying a source of the air having the poor quality, including whether a leading vehicle in front of the ego vehicle is the source. The method includes generating a response to the air having the poor quality according to the source. The method includes controlling the ego vehicle according to the response.

A vehicular vision system includes a front camera disposed at a windshield of a vehicle equipped with the system and a side camera disposed at a side mirror of the vehicle. An electronic control unit (ECU) includes an image processor for processing image data captured by the cameras. The vehicular vision system, responsive to processing by the image processor of image data captured by the front camera, detects a leading vehicle traveling in front of the equipped vehicle in a traffic lane the equipped vehicle is also traveling along. The vehicular vision system, responsive to determination of an intent of the equipped vehicle to change lanes and to processing by the image processor of image data captured by the side camera, detects an oncoming vehicle traveling in a traffic lane adjacent to the traffic lane the equipped vehicle is traveling along.

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.

Three-way longitudinal collision control techniques for mitigating vehicle occupant injuries include providing a controller and a set of perception sensors configured to monitor vehicles traveling both in front of and behind a host vehicle; executing a level of autonomy three or greater (LOA 3+) autonomous driving feature that includes controlling a speed of a host vehicle relative a lead vehicle in front of the host vehicle and a trailing vehicle behind the host vehicle, during the execution of the LOA 3+ autonomous driving feature, detecting an imminent three-way longitudinal collision between the host vehicle, the lead vehicle, and the trailing vehicle, and in response to detecting the imminent three-way longitudinal collision, controlling a speed of the host vehicle during the three-way longitudinal collision to mitigate injuries to one or more occupants of the host vehicle.

Autonomous traffic navigation optimization (e.g., using a computerized tool) is enabled. For example, a system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise a communication component that, using a defined vehicle communication protocol, receives leading vehicle telemetry data applicable to a leading vehicle, wherein the leading vehicle is ahead of a trailing vehicle, and an autonomous driving component that, based on the leading vehicle telemetry data, autonomously controls movement of the trailing vehicle to follow the leading vehicle while maintaining a threshold distance between the trailing vehicle and leading vehicle.