576 results about "Autopilot" patented technology

An AI-connected-electrified (ACE) heavy truck system equipped with an intelligent multi-mode hybrid (iMMH) powertrain system and a vehicle supervision control strategy based on machine learning (ML) or reinforcement learning (RL) paradigm are presented. This system ensures industry-leading power and braking performance of the ACE heavy truck while automatically optimizes both energy saving and emission reduction based on the vehicle's dynamic driving data and 3D electronic map information of roads for any transport event. In this application, the conventional analog electronic control (AEC) method is replaced by a novel digital pulse control (DPC) method on the instantaneous power function of the engine. The DPC method converts the complex surface working conditions of the AEC engine of the hybrid vehicles into simpler pre-defined working condition lines of the DPC engine. Consequently, the multi-variable nonlinear technical problem of simultaneously optimizing real driving environment (RDE) fuel consumption and pollutant emissions of the ACE heavy truck is simplified into two decoupled quasi-liner optimization problems, and ensures the reduction of on-vehicle computing resources for real-time AI inference computation and the improvement of the optimal performance, the convergent rate, and the robustness of the corresponding fuel-saving algorithm for the trained ML model or the learned RL model. Ultimately, the ACE truck achieves in the engineering sense the global minimum RDE fuel consumption and pollutant emissions meeting the standard consistently at high performance to cost ratio for any transport event and the RDE fuel consumption is decoupled from the vehicle configuration parameters and the human driver.

In examples, autonomous vehicles are enabled to negotiate yield scenarios in a safe and predictable manner. In response to detecting a yield scenario, a wait element data structure is generated that encodes geometries of an ego path, a contender path that includes at least one contention point with the ego path, as well as a state of contention associated with the at least on contention point. Geometry of yield scenario context may also be encoded, such as inside ground of an intersection, entry or exit lines, etc. The wait element data structure is passed to a yield planner of the autonomous vehicle. The yield planner determines a yielding behavior for the autonomous vehicle based at least on the wait element data structure. A control system of the autonomous vehicle may operate the autonomous vehicle in accordance with the yield behavior, such that the autonomous vehicle safely negotiates the yield scenario.

This application belongs to the field of autonomous driving technology, specifically relating to a multimodal data processing method and apparatus for end-to-end autonomous driving. The method includes: extracting multi-scale features from camera images and LiDAR bird's-eye view images through multiple feature extraction stages of an encoder; performing a feature fusion operation on the image features and LiDAR features of each feature extraction stage after each stage; after the final stage, segmenting at least a first output stream for trajectory prediction from the split LiDAR features; performing a feature enhancement operation on the first output stream and vehicle state features to generate enhanced features; and decoding the enhanced features to generate a sequence of future trajectory points for the vehicle. This application significantly improves inference efficiency and enhances environmental robustness while maintaining perception depth.

The technology involves pullover maneuvers for vehicles operating in an autonomous driving mode. A vehicle operating in an autonomous driving mode is able to identify parking locations that may be occluded or otherwise not readily visible. A best case estimation for any potential parking locations, including partially or fully occluded areas, is compared against predictions for identified visible parking locations. This can include calculating a cost for each potential parking location and comparing that cost to a baseline cost. Upon determining that an occluded location would provide a more viable option than any visible parking locations, the vehicle is able to initiate a driving adjustment (e.g., slow down) prior to arriving at the occluded location. This enables the vehicle to minimize passenger discomfort while also providing notice to other road users of an intent to pull over.

The application discloses a trajectory planning method and device, equipment and storage medium, and belongs to the technical field of vehicle navigation auxiliary driving or automatic driving. The application obtains a sampling trajectory cluster, determines actual motion trajectories of vehicles corresponding to each sampling trajectory in the sampling trajectory cluster, and screens a target trajectory from the sampling trajectory cluster according to the actual motion trajectories, so that the optimal trajectory is screened from the sampling trajectory cluster again based on the actual motion trajectories of the vehicles after the actual motion trajectories of the vehicles corresponding to each sampling trajectory are determined. In the above manner, it can be ensured that the optimal cost trajectory selected is the optimal selection when the vehicle is actually executed, and the safety and comfort of the navigation auxiliary driving system are improved.

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

A remote assistance apparatus records, in an assistance content recording unit, assistance contents for remote assistance for an autonomous driving vehicle in response to the autonomous driving vehicle stopping as a result of an occurrence of a traveling abnormality, and then resumes autonomous traveling as a result of remote assistance for the autonomous driving vehicle being provided by an operator. The assistance contents include an assistance location and operator instruction contents for remote assistance instructed by the operator. The remote assistance apparatus detects an autonomous driving vehicle that is expected to pass through a predetermined assistance location included in predetermined assistance contents recorded in the assistance content recording unit. The remote assistance apparatus determines an assistance method of the remote assistance for the autonomous driving vehicle that is detected by the assisted vehicle detecting unit, based on the predetermined assistance contents that are recorded in the assistance content recording unit.

Distance calculations for autonomous vehicle (AV) or advanced driver assistance system (ADAS) navigation may include calculating a distance between two points within an image captured by a vehicle camera. To calculate distance in ADAS and AV systems, the vectors and other values may be represented as floating-point (FP) values. The use of FP values may provide improved performance of these calculations in ADAS, AV systems, and other systems that seek to reduce or minimize computation speed. When the input values are represented as FP values, the distance may be calculated as a square root of a sum of squares of the two FP values. Improved systems and methods are provided for floating-point calculation related to a square root, such as for determining distance calculations.

The invention relates to a method for navigating a motor vehicle (10) with an at least partially automated driving function (56). The method comprises the following steps: providing an obstacle map (24) of a predefined environment (22); generating at least one proposed trajectory arc (26, 28, 30); checking each of the at least one proposed trajectory arcs (26, 28, 30) against at least one kinematic feasibility criterion (36, 38); calculating at least one cost value (46, 48, 50); selecting one of the at least one proposed trajectory arcs (26, 28, 30) as the planned trajectory (28) depending on the at least one cost value (46, 48, 50); and operating the driving function (56) according to the planned trajectory (28). The invention further relates to a computer program product, a non-volatile, computer-readable storage medium, and an electronic computing device.

An automated driving system for deciding to change a travel state of a host vehicle using a machine learning model in an automated driving control process for the host vehicle includes an automated driving control unit configured to decide to change the travel state of the host vehicle using the machine learning model on the basis of a preset target route and position information of the host vehicle and map information, or a detection result of an external sensor of the host vehicle, and an information presentation unit configured to present a travel state change factor, which is a main cause of the change in the travel state, and the change in the travel state of the host vehicle to a user of the host vehicle when the automated driving control unit decides to change the travel state of the host vehicle.

This invention provides a 4D radar odometry method for high-dynamic scenarios, relating to the field of autonomous driving perception and sensor fusion technology. The method includes: acquiring raw point cloud data using a 4D millimeter-wave radar-based perception system; extracting corresponding clustering parameters from the raw point cloud data, wherein the clustering parameters include search radii in different directions and minimum neighborhood density; clustering the raw point cloud data using the DBSCAN clustering method based on the clustering parameters to obtain different point cloud clusters; extracting static points from the different point cloud clusters; and calculating the vehicle speed based on the static points using an angle-distance weighted least squares method. This technical solution effectively improves the accuracy of vehicle speed measurement.

The present application relates to the technical field of vehicle automatic driving, in particular to a kind of intelligent Agent trajectory planning control method and system based on HDMap;The method mainly includes four steps of the initialization of vehicle Agent, formal path query, entry mode confirmation and automatic driving according to path;High-precision semantic map and the idea of intelligent Agent are used in the present scheme, and a kind of vehicle Agent implementation scheme for flexible operation along lane is presented;In the entry mode of vehicle Agent, the present scheme designs a kind of ingenious vehicle Agent entry mode from the angle of time and space;The present scheme utilizes lane center line segment information in map, regulates the motion trajectory of vehicle, and provides flexible acceleration setting interface, so that the speed of vehicle Agent can change in real time according to the demand of user or traffic condition while automatically driving along lane, which reserves interface for the access of subsequent vehicle anti-collision strategy.

To effectively alleviate lack of exercise and stress in cats. [Solution] The system comprises an unmanned aerial vehicle that flies by autopilot, a camera mounted on the unmanned aerial vehicle, and a holding unit mounted on the unmanned aerial vehicle that holds the target object 602. Based on the image captured by the camera, a flying robot 101 flies around the target object, the cat 601, to attract the cat's attention and, specifically for cats 601 that need exercise, to create an opportunity for the cat 601 to move. This effectively alleviates the cat's lack of exercise and stress.

This invention relates to an autonomous driving control system and a vehicle. The autonomous driving control system includes an autonomous driving intelligent controller and a vehicle controller, which are communicatively connected. The system also includes a drive-by-wire controller and a safety redundancy controller. The drive-by-wire controller is communicatively connected to both the autonomous driving intelligent controller and the vehicle controller, and the safety redundancy controller is also communicatively connected to both. The drive-by-wire controller sends a braking command to the vehicle controller when it detects an anomaly in the autonomous driving intelligent controller, and the safety redundancy controller sends a braking command to the vehicle controller when it detects an anomaly in the drive-by-wire controller. Based on this invention, the autonomous driving control system can solve the vehicle safety problem caused by the lack of effective detection of anomalies in the autonomous driving intelligent controller in existing autonomous vehicles.

This application discloses a control method, device, equipment, and storage medium for an autonomous vehicle, relating to the technical field of autonomous driving. The method of this application includes: acquiring vehicle driving monitoring data of the autonomous vehicle; determining the driving action to be performed based on the vehicle driving monitoring data, wherein the vehicle driving monitoring data is obtained by fusing and stitching together several video data collected by multiple cameras; if the driving action to be performed is a first driving action, receiving a driving operation instruction from the autonomous vehicle and performing autonomous driving operation of the autonomous vehicle according to the driving operation instruction, wherein the first driving action is a precise driving action; if the driving action to be performed is a second driving action, receiving a driving operation instruction sent by a remote control platform and performing remote driving control operation of the autonomous vehicle according to the driving operation instruction, wherein the second driving action is a non-precise driving action.

The invention relates to a method for operating an assistance system (2) of a vehicle (1) comprising the steps of: issuing an offer to a driver (14) of the vehicle (1) to perform an automated driving maneuver, wherein a steering system (9) for automated lateral guidance of the vehicle (1) is controlled during the execution of the automated driving maneuver; checking whether the driver (14) has accepted the offer, wherein acceptance describes a turning of the gaze direction (31) and / or the head orientation towards a predetermined area (22, 23, 25, 26); detecting an operating action and / or a body posture of the driver (14) during the acceptance check; judging the acceptance as plausible if the operating action corresponds to a predetermined operating action and / or if the body posture corresponds to a predetermined body posture; and issuing a control signal to perform the automated driving maneuver.if the assumption is deemed plausible.

The invention provides an intelligent learning evaluation method and system for a driving strategy of an automatic driving automobile. Screening safety-critical scene data in the automatic driving big data, constructing a safety-critical scene set, and obtaining a safety-critical scene set explicit function in combination with explicit function operation; carrying out automatic differential calculation on a basic mean square error loss function, and obtaining a safety-critical scene loss function in combination with a safety-critical scene set representation function; according to the safety-critical scene loss function, through driving strategy probability distribution training, forming a driving strategy probability distribution intelligent learning model; verifying the effectiveness and accuracy of the driving strategy probability distribution intelligent learning model, and evaluating the safety precision of the driving strategy probability distribution intelligent learning model; and the efficiency and the accuracy of intelligent learning evaluation of the driving strategy of the autonomous vehicle are improved.

The application provides a push stream control method and device and electronic equipment, and relates to the technical field of automatic driving. The method comprises the following steps: subscribing to a push stream switch topic published by a remote control platform based on a preconfigured robot operating system (ROS) node; obtaining push stream control information issued by the remote control platform from the push stream switch topic; the push stream control information comprises a push stream total switch and a target address; the push stream total switch is used to represent a push stream on state or a push stream off state; updating a current push stream parameter in the ROS node according to the push stream control information to generate a new push stream parameter; and controlling camera push stream to a target server according to the new push stream parameter; the target server is a device corresponding to the target address. The method can realize dynamic updating of the push stream parameter in the ROS node, thereby adapting to the dynamic scheduling requirements of multiple vehicles and multiple tasks in a vehicle-mounted scene.

This invention discloses a motion risk assessment system and method for autonomous vehicles based on multi-mode trajectory prediction, comprising four sensors, a time synchronization module, a verification module, a local anomaly voting and fault detection counter, a trajectory correction and feature enhancement module, and an LSTM network. The four sensors include an INS (Inertial Navigation System) combined inertial navigation sensor, an inertial navigation IMU (Inertial Measurement Unit) sensor, a drive-by-wire chassis sensor, and a visual recognition sensor. The INS combined inertial navigation sensor includes GPS and an IMU. The verification module includes a χ² (X-ray) sensor. 2 The system includes checks for consistency in motion patterns and residual consistency; the LSTM network comprises an LSTM encoder, a classification decoder, and an LSTM decoder. This invention proposes a complete "detection-evaluation-decision" closed-loop solution. By constructing a multi-mode prediction network incorporating fault characteristics, not only is fault identification achieved, but the impact of faults on driving safety can also be accurately assessed, providing comprehensive safety decision-making support for autonomous driving systems.

Autonomous driving control device (1) that can be installed in a vehicle comprising: at least one wide-angle camera (32w) and one narrow-angle camera (32n) designed to capture at least one wide-angle image and one narrow-angle image of at least one directed wide-angle area and one directed narrow-angle area of ​​the vehicle, which are at least partially different from each other;and a vehicle control unit (24, 26, 28) designed to control a driving state of the vehicle, wherein the wide-angle camera has a predetermined wide-angle field of view, wherein the narrow-angle camera has a predetermined narrow-angle field of view that is narrower than the wide-angle field of view, wherein the directed narrow-angle area of ​​the narrow-angle camera is further away from the vehicle than the directed wide-angle area of ​​the wide-angle camera, wherein the autonomous driving control device comprises: a drive control unit (6) designed to determine a value of at least one controlled variable for autonomous driving of the vehicle based on the wide-angle and narrow-angle images and an autonomous driving condition; and to output the value of the at least one controlled variable to the vehicle control unit in order to cause the vehicle control unit to perform a task for autonomous driving of the vehicle;and a camera monitoring unit (8) designed to determine whether a camera from the wide-angle camera and the narrow-angle camera is malfunctioning;and if it is determined that the wide-angle camera is malfunctioning, to limit the autonomous driving condition on the basis of the directed wide-angle area corresponding to the malfunctioning wide-angle camera, wherein the drive control unit is designed to determine the value of the at least one controlled variable according to the limited autonomous driving condition in order to cause the vehicle control unit to continuously perform the task of autonomously driving the vehicle according to the determined value of the at least one controlled variable, wherein the camera monitoring unit is designed to perform at least one of the following tasks: a first task to cause a driver of the vehicle to interrupt the task of autonomously driving the vehicle when it is determined that the narrow-angle camera is malfunctioning;and a second task to force the interruption of the autonomous driving task of the vehicle if it is determined that the narrow-angle camera is malfunctioning.