5517 results about "Autopilot" patented technology

System and methods are disclosed for navigating a host vehicle. In one implementation, at least one processing device may be programmed to receive an image representative of an environment of the host vehicle, identify, based on the image analysis, a target vehicle, select a safe navigation mode among a plurality of available safe navigation, each of the safe navigation mode being associated with a corresponding safe navigation distance, determine a planned navigational action for the host vehicle that if taken would result in a next-state distance between the host vehicle and the target vehicle greater than or equal to the selected safe navigation mode safe navigation distance, and implement the planned navigational action by controlling one or more actuators associated with the host vehicle. The at least one processing device may be further programmed to select a safe navigation mode based on a detected location or condition of the host vehicle.

The present invention relates to an automated driving system for switching between automated driving mode and manual driving mode in an automated driving situation, and a fallback method for an automated driving failure situation in the automated driving system. According to the present invention, the method comprises the steps of predicting whether, on a route a vehicle is traveling, the vehicle will deviate from an operational design domain (ODD) in which automated driving is possible; and when deviation is predicted, performing a minimal risk maneuver (MRM) process for controlling the vehicle to respond to an automated driving failure situation at a deviation point. According to the present invention, the stability of the automated driving system in emergency situations may be increased, and by proposing a stable mode-switching method for the automated driving system, the best response method may be provided.

The invention relates to the technical field of automatic driving, and discloses an automatic driving lane changing trajectory planning method based on deep learning, and the method comprises the steps: carrying out the data collection and preprocessing of a multi-modal sensor; performing spatial feature extraction and time sequence modeling on the preprocessed multi-modal data by adopting a CNN-LSTM hybrid architecture, performing feature fusion through an attention mechanism, and outputting a first feature extraction vector; taking the detected vehicles as graph nodes to construct a traffic graph, learning an interaction relationship between the vehicles through a graph attention network and a message passing mechanism, and calculating a scene urgency score and a safety score; generating a lane changing decision based on the deep Q network and the strategy gradient; and generating a trajectory based on the generative adversarial network. The technical problems that an existing lane changing track planning method cannot adapt to the dynamic traffic environment, lacks the ability of understanding complex multi-vehicle interaction and is difficult to balance safety and urgent conflict requirements are solved, and intelligent, safe and efficient automatic driving lane changing track planning is achieved.

The invention provides a cloud intelligent driving control system based on a multi-modal large model, and belongs to the technical field of automatic driving. The system comprises a cloud multi-modal large model analysis and control module, a real-time remote driving takeover module, an advanced prediction and risk avoidance module, a cloud intelligent driving collaboration and optimization module, a multi-modal fault diagnosis and remote repair module and a real-time communication and interaction enhancement module. The system realizes cloud remote monitoring and control of the autonomous vehicle through interaction and cooperation among different modules, and assists the autonomous vehicle through remote control, so that the autonomous vehicle runs more safely; the early prediction and risk avoidance module of the system sets the prediction of the sight line of the vehicle and the prediction of the sight line of the vehicle, and sets corresponding prediction methods for the vehicles in two different environments for prediction in a mode of extracting different matrix samples, so that the prediction accuracy of the cloud system and the safety of the automatic driving vehicle are improved.

The invention provides an automatic driving vehicle scheduling method and device, equipment and a storage medium, and the method comprises the steps: carrying out the multi-dimensional collection and preprocessing of an automatic driving vehicle, and generating a comprehensive data set; and based on the comprehensive data set, carrying out dynamic allocation and fleet collaborative planning on loading and unloading tasks to generate a task allocation scheme. And through a hierarchical search algorithm and a multi-objective optimization algorithm, the path of the vehicle is planned and optimized in combination with the task allocation scheme and the comprehensive data set, and an optimized path scheme is generated. And according to the task allocation scheme, the optimized path scheme, the integrated data set and the vehicle real-time state data, utilizing a predictive analysis model to evaluate and deal with potential risks in the transportation process, and generating and executing a real-time scheduling instruction. According to the invention, the scheduling method of multi-dimensional data acquisition and processing, dynamic task allocation, path optimization and risk prediction is combined, the scheduling efficiency and safety of the autonomous vehicle are comprehensively improved, and efficient logistics management is realized.

The invention discloses an automatic driving method and system based on an end-to-end and multi-modal large model. The method comprises the following steps: determining image information, vehicle historical information, navigation information and text information collected by a plurality of sensors of a target vehicle; based on an encoder corresponding to each type of information, respectively encoding the image information, the vehicle historical information, the navigation information and the text information to obtain a plurality of encoding tokens; respectively inputting the plurality of coding tokens into a trained multi-modal large language model for prediction to obtain a plurality of decoding tokens; decoding the plurality of decoding tokens based on a trajectory decoder and a text decoder, and outputting a planned trajectory and control information of the target vehicle in parallel; and based on the planned trajectory and the control information, controlling the target vehicle to automatically drive according to the planned trajectory, and performing text interpretation on the current automatic driving strategy. Therefore, the scheme is high in integration level and construction efficiency, high in generalization, high in iteration and high in adaptability, and the automatic driving landing and service efficiency is improved.

The invention discloses a sky-ground integrated automatic driving intelligent decision-making system and method driven by a large language model, and the system comprises a satellite layer, a vehicle end layer and a cloud end layer, and achieves the precise, intelligent and efficient automatic driving decision-making through efficient data interaction and cooperative processing. The satellite layer comprises a low-orbit satellite constellation, transmits high-precision positioning information to a vehicle in real time and provides a space-time reference; the vehicle end layer is an executor, collects environment data in real time through a sensor, generates a preliminary driving decision by using high-precision positioning data and an end-to-end rapid decision model built in the vehicle end layer, and uploads driving data, environment sensing data and decision requirements to a cloud end; the cloud end carries out remote monitoring and management on the system, evaluates the vehicle state and carries out early warning, when the vehicle runs safely, the vehicle end is adopted for decision making, when potential risks or abnormal conditions exist, the large language model is adopted for decision making, optimization instructions are pushed to the vehicle end, model parameters or emergency disposal schemes are updated, and the vehicle end is assisted to correct the decision.

The invention provides an end-to-end automatic driving decision control method and system, and belongs to the technical field of automatic driving. The invention relates to an end-to-end automatic driving decision control method based on multi-modal perception and hierarchical trajectory optimization, and the method comprises the steps: constructing an end-to-end decision closed loop through combining the zero sample migration capability of a vision-language-action (VLA) model with a hierarchical optimization architecture: analyzing multi-modal input (vision, language and point cloud) by using a pre-trained VLA model to generate path points; the vehicle pose is dynamically adjusted through upper-layer optimization to expand a feasible solution space, a smooth track meeting dynamics and collision avoidance constraints is solved in real time through lower-layer optimization, and finally a vehicle control instruction is output. According to the method, a multi-modal sensing and hierarchical trajectory optimization mechanism is fused, and the sensing generalization ability, the path planning feasibility and the control execution robustness of the system in a complex traffic environment are effectively improved.

The present application relates to the technical field of autonomous driving functions, and in particular to a control method and device integrating predictive cruise and lane changing decision-making. The method comprises: receiving the accelerations, speeds and positions of a controlled autonomous driving vehicle and surrounding traffic vehicles at a current moment so as to construct an IDM microscopic following model to predict surrounding traffic vehicle states in a preset future long time domain; on the basis of the surrounding traffic vehicle states in the preset future long time domain, constructing an optimization cost function, so as to plan a lane keeping strategy or lane changing strategy; on the basis of the lane keeping strategy or lane changing strategy, using a quintic polynomial to solve for a reference path; on the basis of the reference path, establishing an optimal control problem cost function for continuous intersection predictive cruise control, so as to solve for the optimal cruise speed within a traveling lane; and sending the reference path and the optimal cruise speed within the traveling lane to the controlled autonomous driving vehicle for control. Thus, the problems that existing lane changing decision-making only considers longitudinal motion and fails to pay attention to a development trend of the surrounding environment, possible dangers and the like are solved.

Aspects of the present invention relate to an apparatus for providing driver assistance of a vehicle, an autonomous system, a vehicle, a method, a controller and a non-transitory computer readable medium. The apparatus comprises a first sensor and a second sensor mounted on a vehicle. The first sensor is configured to transmit and receive electromagnetic radiation to detect the presence of an object in a first area, and the second sensor is configured to transmit and receive electromagnetic radiation to detect the presence of an object in a second area. The first area and the second area overlap to define an overlapping area forward of the vehicle. The first area extends from the overlapping area to a first extreme direction having a rearward component and a leftward component, and the second area extends from the overlapping area to a second extreme direction having a rearward component and a rightward component.

The invention discloses an automatic driving path optimization control method and device fusing environmental perception and prediction. The method comprises the following steps: acquiring environmental perception data of an automatic driving vehicle through a radar and a camera; the initial posture, the target posture and the environment perception data of the automatic driving vehicle serve as road constraint conditions, and a preliminary driving path of the automatic driving vehicle from the current position to the target position is planned through a path planning algorithm; state estimation and prediction are carried out on surrounding vehicles and obstacles through a multi-target tracking algorithm; according to future motion trails of surrounding vehicles and obstacles, road constraint conditions and physical restrictions of the autonomous vehicle, performing path optimization on the initial path to obtain an optimized driving path; and converting the optimized driving path into a control instruction of the automatic driving vehicle, and driving the automatic driving vehicle to stably drive along the optimized driving path through the control instruction. According to the invention, the safety and the driving efficiency of the automatic driving vehicle are further improved.

Aspects presented herein may enable a UE (e.g., a vehicle, an autonomous vehicle, an on-board unit (OBU) of a vehicle, an advanced driver assistance systems (ADAS) of a vehicle) to calculate and output safety margins for objects around the UE or a vehicle. In one aspect, a UE receives or transmits a request to provide safety margin visualization assistance. The UE obtains, in response to the request, an indication of a safety margin associated with an object based on a distance between the UE and the object and at least one of: a size of the object, a speed of the object, a location of the object, a type of the object, a direction of the object, or a movement pattern of the object. The UE outputs the safety margin associated with the object.

The invention provides a self-adaptive control method and system for an automatic driving truck, and the method comprises the steps: carrying out the filtering and structural processing of multi-dimensional data collected by a vehicle-mounted sensor network, and generating a standardized data package containing vehicle state parameters and environment information; and according to the standardized data packet, retrieving a deep learning neural network model library, selecting a currently applicable basic model, and dynamically adjusting parameters of the basic model to generate an optimized adaptive control model. And inputting the standardized data packet into an adaptive control model for operation, generating a candidate control instruction, and generating an optimal control strategy through rapid simulation and multi-objective optimization calculation. And finally, the optimal control strategy is executed by the vehicle controller, vehicle response is monitored in real time, and controller parameters are adaptively adjusted based on deviation. According to the method, the dynamic adaptability and strategy optimization of the control model are realized, and the safety and efficiency of the automatic driving truck in a variable environment are improved.

The invention provides an automatic driving-oriented kinematics priori guided vehicle trajectory generation method, and belongs to the technical field of trajectory generation and motion control in automatic driving. Comprising the following steps: constructing a vehicle kinematics differential equation, performing nonlinear compensation and control correction based on a double-flow mechanism to obtain a vehicle explicit physical state, and converting the vehicle explicit physical state into a vehicle implicit physical state; feature extraction, target detection and multi-mode fusion are carried out on the collected point cloud data and the vehicle surrounding image data, and environment information of a scene where the vehicle is located is provided; anchoring Gaussian distribution to simulate a feasible noise track of a vehicle in a current scene, generating a noise track candidate sample through sampling and noise adding, performing reverse denoising reasoning on the noise track candidate sample, generating a track anchor point, and generating a reasoning noise track around the track anchor point; environment information of a scene where the vehicle is located, a reasoning noise track and an implicit physical state are input into a diffusion decoder for iterative training, and kinematic prior guides generation of a future track of the vehicle in the denoising process.

The invention provides a high-precision perception-driven automatic driving positioning method and system, and relates to the technical field of automatic driving, and the method comprises the steps: collecting vehicle environment data in real time through a multi-mode sensor array, and generating a multi-source perception data set; performing time-space synchronization processing; performing motion distortion correction on the fused sensing data stream to generate dynamic compensation sensing data; inputting a multi-source fusion positioning model, and performing three-dimensional space matching in combination with high-precision map data to obtain real-time positioning coordinates; and performing iterative optimization on the real-time positioning coordinates through an adaptive error correction module to generate a vehicle positioning result. According to the method and the device, the technical problem that the positioning precision is affected due to data distortion caused by the influence of the vehicle motion on the sensor data is solved, the distortion caused by the vehicle motion is eliminated through motion distortion correction and compensation, and the positioning precision and stability are improved.

The invention relates to the technical field of automatic driving, and particularly provides an automatic driving vehicle track re-planning method, which comprises the following steps of: firstly, evaluating stability risk and defining three states of vehicle stability through real-time vehicle dynamic parameters, and designing intervention and exit criteria of track re-planning according to the three states; when the vehicle driving is evaluated to be in an unstable state, generating a virtual obstacle by using prediction information of a path tracking module, and converting a dynamic stability boundary into a spatial constraint; performing trajectory re-planning by adopting nonlinear model predictive control, and adjusting the weight between the path tracking precision and the obstacle avoidance demand according to the relative position of the virtual obstacle in combination with a dynamic weight adjustment mechanism; and finally, generating an optimized trajectory containing stability constraint, and realizing high-precision tracking through linear MPC. The method can effectively balance the path tracking precision and the vehicle stability under a complex working condition, and prevents the vehicle from entering a dynamic unstable region.

Trailer detection is provided. An autonomous vehicle can receive, from a time of flight sensor, an indication of a position of a wheel assembly of a trailer. The vehicle can determine a rotation of a rotatable coupler. The vehicle can determine a centerline distance from the rotatable coupler to the wheel assembly.

The invention provides a multi-modal automatic driving scene generation method based on autoregressive closed-loop prediction. The method comprises the following steps: encoding an automatic driving scene video, scene understanding prediction questions and answers and trajectory planning data into a unified multi-modal discrete code by using a pre-trained discrete encoder; multi-modal discrete codes are integrated into a coding sequence, a mask strategy is adopted to cover trajectory codes, original codes without masks serve as a supervision target, a model is generated through autoregressive normal form training, and fine tuning is carried out on scene understanding, scene prediction and trajectory planning data sets; during reasoning, an initial scene image collected by a vehicle camera and a user prompt word are input, the model generates a future scene discrete coding sequence according to initial information, and closed-loop prediction is realized through mask track information; the generated codes are decoded into scene images, text questions and answers, and vehicle trajectories using a symmetric decoder. According to the invention, the problems of poor universality and low efficiency of the existing end-to-end automatic driving world model can be solved.

The embodiment of the invention provides an automatic driving method and device based on a multi-dimensional reward function, and the method and device achieve the control of a driving motion through the combination of an imitation learning framework and a reinforcement learning framework, collection of environment information through a plurality of cameras, and construction of a strategy generation network and a discriminator network. A multi-dimensional reward function model is designed, behaviors such as red light running, line pressing, lane departure and collision are detected and evaluated in real time, and a driving behavior reward and punishment matrix is constructed. Based on an actor evaluation network architecture, environment information and a navigation instruction are input into an actor network to generate an optimal driving action, and the action value is evaluated through the evaluation network to realize dynamic parameter optimization. According to the method, the defects of the traditional technology in the aspects of driving behavior evaluation, action value judgment and the like are effectively overcome, and the safety and reliability of the automatic driving system are remarkably improved.

Provided are a vehicle control method and apparatus, and a vehicle-mounted device, a vehicle and a storage medium. The vehicle control method comprises: on the basis of environmental perception data, position data of a vehicle, and map data, determining behavior trajectory information of a target object in an environment where the vehicle is located; sending the behavior trajectory information of the target object and ego-vehicle trajectory information of the vehicle to a server, so that the server processes the behavior trajectory information of the target object and the ego-vehicle trajectory information on the basis of a target model to generate planning and decision-making information, wherein the target model is a generative pretrained transformer and is obtained by means of performing training on the basis of a plurality of types of training sample data; receiving the planning and decision-making information fed back by the server, and on the basis of the behavior trajectory information of the target object and the planning and decision-making information, generating planning trajectory information and driving strategy information for the vehicle; and on the basis of the planning trajectory information and the driving strategy information, controlling the vehicle to travel. The safety of autonomous driving and the driving and riding experience of users can be improved.

An example method includes (a) obtaining reference decision data describing a reference decision associated with navigating a driving scenario, wherein the reference decision data comprises a target action and a corresponding object identifier associated with the target action; and label data that comprises a validity interval associated with the reference decision, the validity interval indicating a time period of the driving scenario during which the reference decision is valid; (b) simulating a performance of a system under test (SUT) in the driving scenario to generate SUT decision data describing one or more SUT decisions associated with controlling an autonomous vehicle to navigate the driving scenario; and (c) determining, based on a comparison of the reference decision data and the SUT decision data, a validation state for the SUT.

The present disclosure provides an example method for obtaining labeled trajectories. The example method can include obtaining log data describing a trajectory of a vehicle traveling through an environment. The example method can include determining a suboptimal condition associated with the trajectory. The example method can include generating label data that characterizes the suboptimal condition along one or more constraint dimensions of a motion planner of the autonomous vehicle control system. The example method can include generating a training example for training the one or more machine-learned models of the autonomous vehicle control system to decrease a probability of the autonomous vehicle control system inducing the suboptimal condition.

The invention discloses a unified framework for solving automatic driving track prediction and planning consistency based on a world model. According to the method, through cooperative work of the automatic driving domain controller and the vehicle-mounted sensing system, end-to-end joint optimization of track prediction and planning in a complex traffic scene is realized, time sequence dependence and interaction dynamics among intelligent agents are accurately captured, and the prediction capability and robustness of a model are remarkably improved. The method comprises the following specific steps: firstly, constructing a generative world model, and generating potential future state representation by utilizing a behavior conditional and backtracking expansion technology; secondly, in combination with global modeling and a local convolutional network, multi-scale features are extracted, adaptive fusion is carried out, and a multi-modal prediction trajectory is generated; then, a multi-target planning model is adopted to integrate various driving indexes, and a track with the minimum loss function is generated; finally, path planning parameters are dynamically optimized through real-time environment perception and decision feedback, and the problems of prediction uncertainty and planning consistency of the automatic driving track are effectively solved.

The invention provides a following vehicle locking man-machine interaction method and device, electronic equipment, a vehicle and a medium, and the method comprises the steps: obtaining the real-time sensing data of the surrounding environment of the vehicle, and generating and displaying the other vehicle identifier corresponding to the other vehicle in the surrounding environment on the intelligent driving sensing picture of the own vehicle according to the real-time sensing data; the other car identification comprises a locking car following touch control identification used for indicating the self car to lock the car following relative to the other car; touch operation received by the locking and following touch identification is detected in real time, the other vehicle corresponding to the touched locking and following touch identification is determined as a target vehicle, and a control instruction of locking and following of the own vehicle relative to the target vehicle is generated; and according to the vehicle attribute of the target vehicle and the real-time vehicle information, controlling whether the vehicle is locked according to the control instruction to follow the target vehicle to run. According to the human-computer interaction method for locking the following car, the driving safety is ensured while the individuation degree of the intelligent driving system is improved, so that the user experience of automatic driving is improved.

The invention discloses an improved hybrid A star path planning method for an automatic driving vehicle, and relates to the technical field of automatic driving, laser radar sensing and unmanned vehicle navigation. According to the method, on the basis of inheriting the advantages of high global search efficiency, strong kinematics feasibility and the like of a hybrid A star algorithm, a multi-factor cost function is designed, a trajectory smooth constraint is introduced, and the feasibility and execution quality of a path are improved from two aspects of cost evaluation and path construction. In order to further enhance the smoothness and the dynamic controllability of the local path, the initial path is optimized and adjusted by fusing a time elastic band (TEB) algorithm in the method, so that the continuity and the stability of the path in a complex dynamic environment are improved. The method is suitable for a high-precision path planning task in an automatic driving system, and the comprehensive requirements of a path generation result in the aspects of reliability and environmental adaptability can be well met.

An example method includes (a) obtaining a pull-over command indicating the autonomous vehicle is to pull-over to a side of a travel way; (b) in response to the pull-over command, obtaining map data indicative of a plurality of pull-over locations for the autonomous vehicle; (c) determining one or more candidate pull-over locations for the autonomous vehicle based on the map data and a route of the autonomous vehicle; (d) determining a ranking of the candidate pull-over locations based on a feasibility of the autonomous vehicle completing a stop at each respective candidate pull-over location and a quality of each respective candidate pull-over location; (e) based on the ranking of the candidate pull-over locations, determining a selected pull-over location for the autonomous vehicle; and (f) controlling a motion of the autonomous vehicle based on the selected pull-over location.

The invention relates to an automatic driving vehicle steering and suspension cooperative control method based on reinforcement learning, and the method specifically comprises the following steps: 1, building a dynamic model of a vehicle steering system and a suspension system, and obtaining transverse and vertical vehicle motion states through controlling a steering angle and a suspension acting force; 2, constructing a control action and reward function of a vehicle steering system and a suspension system, and forming a reinforcement learning exploration strategy of vehicle steering and suspension cooperative control; 3, constructing intelligent agents of a steering system and a suspension system; and 4, constructing a reinforcement learning environment, and realizing cooperative control of the steering system and the suspension system through a multi-agent reinforcement learning method. According to the method, different vertical and transverse plane environments and different learning tasks are comprehensively considered, the data input dimension is reduced, the optimal motion control strategies under different working conditions are considered, and on the premise that accurate path tracking is guaranteed, the transverse roll stability and the vertical smoothness and comfort are improved through cooperative steering system and suspension system control.

The invention relates to a memory enhanced vision-language-action submerged space dynamic fusion automatic driving method. Comprising the following steps: generating a bird's-eye view feature map; extracting scene, agent and map marks from the aerial view feature map, and fusing the mark at the current moment and the previous i historical marks to generate memory enhanced visual marks; the memory enhanced visual mark and the vehicle state information are converted into a submerged space, text input of a driver is marked and unified into the submerged space, the submerged space is represented and fused, and fusion representation is generated; an automatic driving instruction data set is introduced for adjustment, and a large language model adapting to an automatic driving task is obtained; according to the fusion representation, track planning is carried out in an autoregression mode by using a large language model, and path point coordinates are obtained; and designing a transverse controller and a longitudinal controller based on PID (Proportion Integration Differentiation) to track and control the coordinates of the path points. According to the invention, the visual representation capability of end-to-end driving is improved, and the visual-language-action fusion effect is improved.

The invention provides an automatic driving vehicle track prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring scene information and initial noise of a target vehicle; the scene information comprises surrounding vehicle information, road information, static object information and navigation information; predicting a future trajectory of the target vehicle according to the scene information and the initial noise based on a pre-trained automatic driving trajectory prediction model; the automatic driving track prediction model comprises an adaptive layer normalization sub-module and a scale adaptive self-attention sub-module; the automatic driving track prediction model is obtained by training according to a scene information sample and a real self-vehicle track sample added with noise, and the automatic driving track prediction model can predict a conditional velocity field and an unconditional velocity field at the same time during training. According to the method, efficient space-time fusion of multi-source input information is realized, and a multi-vehicle game / interaction behavior in a high-interaction scene is effectively simulated.

The invention discloses an automatic driving planning method, device and equipment for a two-wheeled mobile robot and a storage medium, and the method comprises the steps: collecting environment data and vehicle body posture information through a multi-source sensor, and constructing a space-time joint map under a dynamic aerial view coordinate system; a hierarchical trajectory planning architecture is designed based on the four-dimensional state space, an initial path is generated by adopting an improved algorithm, and space-time joint optimization is performed through quadratic programming; a smooth trajectory meeting dynamic constraints is generated in combination with a differential flatness parameterization method, and accurate execution of attitude and motion instructions is realized based on feedforward-feedback cooperative control. According to the method, the problem that the trajectory is not feasible due to neglect of the inclination angle in the traditional automatic driving planning of the two-wheeled vehicle is solved, and the motion stability and safety in a dynamic scene are improved.