7810 results about "Autopilot" patented technology

The invention relates to an automatic driving vehicle trajectory planning method, which comprises the following steps of: extracting comprehensive spatio-temporal information representation of a driving scene from automatic driving multi-modal data by adopting an end-to-end branch, and predicting and generating a trajectory sequence predicted by a vehicle in a future set time period; a visual language model branch is adopted to embed visual features related to the text instruction into a large language model, and a plurality of different track sequences in a future set time period are obtained through prediction; and fusing the trajectory sequences output by the two branches according to the loss value and the danger situation of the current driving scene to obtain a final planning trajectory. Compared with the prior art, the method has the advantages of high safety and reliability.

The invention discloses a driving situation prediction and adaptive strategy generation system based on a cloud multi-mode large model. Comprising a multi-modal data acquisition and situation prediction module, a self-adaptive strategy generation and remote optimization module, a collaborative driving and multi-vehicle strategy coordination module, a cloud driving intelligent monitoring and feedback module and a dynamic risk assessment and path optimization module. Various input data from the vehicle and the environment are processed through the cloud, risk factors such as sudden obstacles, weather deterioration and complex traffic conditions in the automatic driving situation are predicted in advance, cloud computing provides powerful data processing capacity and storage resources, a large model deployed at the cloud can train data collected by the vehicle, and the risk factors can be predicted in advance. Therefore, the perception and prediction capabilities of the automatic driving system are improved, various coping strategies aiming at the risks are generated, and the vehicle is helped to make a safer and more intelligent driving decision in a complex scene through dynamic generation and remote optimization of an adaptive strategy.

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.

This document describes trajectory planning in a three-dimensional (3D) search space with a space-time artificial potential field. An example system includes a processor that obtains an initial pose, a goal pose, and an obstacle map for an environment. The processor uses a parking trajectory algorithm to determine a trajectory by searching in a space-time artificial potential field. The trajectory includes a series of 3D waypoints, including two-dimensional (2D) positional coordinates and time coordinates, to navigate from the initial pose towards the goal pose. Operation of the host vehicle is then controlled to maneuver along the trajectory using an assisted-driving or autonomous-driving system. In this way, path searching is performed in the entire 3D search space rather than iterating between two 2D search planes. This allows motion planning for autonomous parking, especially with static and dynamic objects present, to determine an optimal trajectory in a single iteration.

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 relates to the technical field of automatic driving, in particular to an automatic driving vehicle management method and system, electronic equipment and a storage medium. The method comprises the following steps: firstly, automatically identifying and classifying a current driving scene based on vehicle real-time data, and selecting a most suitable driving strategy from a preset strategy library; then, map data are optimized in real time to reflect the current road condition; then predicting future trajectories of surrounding dynamic objects; planning a safe bypass path according to a prediction result; and finally, generating a corresponding vehicle control instruction. The system can adapt to complex and changeable driving environments, can improve the driving efficiency on the premise of guaranteeing the safety, and remarkably improves the intelligent level and practicability of an automatic driving system.

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.

This disclosure describes techniques for autonomous vehicles to determine driving paths and associated trajectories through unstructured or off-route driving environments. When an object is detected within a driving environment, a vehicle may determine a cost-based side association for the object. Various costs may be used in different examples, including costs based on a cost plot and / or motion primitives that may vary for terminal and non-terminal desired destinations (or ending vehicle states). Using tree searches to determine estimated candidate costs, the autonomous vehicle may compare right-side and left-side driving path costs around the object to determine a side association for the object. Based on the side association, the autonomous vehicle may determine an updated planning corridor and / or a trajectory to control the vehicle from a current state to a desired ending vehicle state.

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.

A trajectory for an autonomous machine may be evaluated for safety based at least on determining whether the autonomous machine would be capable of occupying points of the trajectory in space-time while still being able to avoid a potential future collision with one or more objects in the environment through use of one or more safety procedures. To do so, a point of the trajectory may be evaluated for conflict based at least on a comparison between points in space-time that correspond to the autonomous machine executing the safety procedure(s) from the point and arrival times of the one or more objects to corresponding position(s) in the environment. A trajectory may be sampled and evaluated for conflicts at various points throughout the trajectory. Based on results of one or more evaluations, the trajectory may be scored, eliminated from consideration, or otherwise considered for control of the autonomous machine.

The invention discloses an automatic driving deviation correction control method and system, and relates to the technical field of automatic driving, and the method comprises the steps: carrying out the comparative analysis of a dynamically monitored real-time automatic driving path of a target vehicle and a predetermined automatic driving path, and obtaining a target deviation coefficient; reading a predetermined road condition feature, and extracting a first road condition feature; obtaining a first real-time characteristic parameter; extracting a first historical driving record in the historical driving vehicle log; obtaining a first prediction feature parameter; taking a mean value and recording the mean value as a first target parameter; and activating the deviation rectification control decision model to obtain a deviation rectification control decision of the target vehicle on the target road section. The technical problems that in the prior art, an automatic driving vehicle is difficult to deal with driving path deviation caused by dynamic road condition changes, then the driving track cannot be corrected in real time, and the automatic driving path control precision and stability are insufficient are solved, and the technical effect of improving the path control precision and stability of the automatic driving vehicle is achieved.

The invention discloses a laser radar mapping method based on loopback detection and a visual-inertial odometer. The visual-inertial odometer (VIO) and laser radar data are combined with loopback detection. Through comprehensive utilization of rich perception of VIO to the environment, high-precision distance measurement provided by the laser radar and a correction function of loopback detection, the method can significantly improve the accuracy and robustness of the autonomous navigation system. In a dynamic and uncertain environment, the method can effectively solve the technical problems of sensor error accumulation, environment change, positioning errors caused by dynamic obstacles and the like, and provides a more reliable and effective solution for navigation of an automatic driving vehicle and an advanced robot in a complex environment.

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.