37 results about "Advanced driver" patented technology

An advanced driver assist system (ADAS) includes a processing circuit and a memory storing instructions executable by the processing circuit. The processing circuit executes the instructions to cause the ADAS to: obtain, from a vehicle, a video sequence including a plurality of frames captured while driving the vehicle, where each of the frames corresponds to a stereo image including a first viewpoint image and a second viewpoint image; determine depth information in the stereo image based on reflected signals received while driving the vehicle; fuse the stereo image and the depth information to generated fused information, and detect at least one object included in the stereo image based on the fused information.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent network connection vehicle driving risk assessment method and a visualization system based on multi-source data fusion. According to the method, multi-source data such as vehicle perception, communication and maps are fused, an artificial potential field theory is combined, a gravitational force risk potential field function, a repulsive force risk potential field function and a road boundary repulsive force potential field function are constructed respectively, and a unified total risk potential field TPF is formed by a gravitational force risk potential field, a repulsive force risk potential field and a road boundary repulsive force potential field; according to the method, the driving risk of the intelligent networked vehicle is obtained, risk grade division and real-time visual display are realized through clustering analysis, the accuracy and dynamic response capability of risk assessment are improved, the method is suitable for safety decision support of advanced driving assistance and automatic driving systems, real-time assessment of the driving risk can be realized more accurately and efficiently, and the risk assessment efficiency is improved. And the risk assessment precision and the dynamic response capability are improved.

Systems and methods are disclosed for driver assistance. In one example a method for an advanced driver alert system for a vehicle comprises monitoring, via a user-facing camera, facial movement of a user over time. The method includes determining a plurality of facial movement metrics based on the facial movement over an adjustable time window, transforming the plurality of facial movement metrics into a machine readable representation of the adjustable time window, and determining, via a code reading model, one or more user requests based on the machine readable representation of the facial movement metrics. The method includes executing one or more user assistance operations based on the determined user requests.

Embodiments of systems and methods for generating personalized Advanced Driver Assistant Systems (ADAS) include one or more processors, one or more action engines, and one or more communication devices. The processors are operable to filter current driving data of a vehicle including environmental data and one or more driver states associated with a current driver, label the filtered driving data based on reaction time parameters and anomaly detection, and train, using the labeled driving data, a machine-learning (ML) algorithm to generate one or more personalized ML models and a driver reaction time mapping. The one or more action engines are operable to generate personalized ADAS parameters based on the driver reaction time mapping. The one or more communication devices are operable to transmit the one or more personalized ML models and the personalized ADAS parameters to the vehicle for personalized real-time interference.

The present disclosure is directed at an Advanced Driver Assistant System, ADAS, for vehicles configured to detect a potentially dangerous object inside a vehicle, the system comprising at least one sensor unit, a processing unit, and an output unit, wherein the sensor unit is configured to monitor at least a part of the interior of the vehicle and to transmit acquired sensor data to the processing unit, and wherein the processing unit is configured to analyze the transmitted sensor data, detect, based on the analyzed sensor data, an object inside the vehicle, classify the detected object as a potentially dangerous object, and trigger the output unit to issue a warning in case the detected object is classified as a potentially dangerous object.

An advanced driver-assistance system (ADAS) comprises: a sensor; a behavior planner that performs multi-policy lane change assistance for a vehicle by evaluating multiple scenarios based on an output of the sensor using a cost-based architecture, the cost-based architecture including a Markov decision process (MDP) with a discounted horizon approach applied to pre-chosen open-loop optimistic policies that are time based, wherein the behavior planner uses the MDP for choosing among the pre-chosen open-loop optimistic policies based on respective costs associated with the pre-chosen open-loop optimistic policies, the costs determined by performing a rollout for at least one gap in a fixed time horizon; a motion planner receiving an output of the behavior planner based on the MDP; and a controller receiving an output of the motion planner and determining vehicle dynamics of the vehicle for a next timestep.

This invention relates to the field of automotive steering control technology, specifically a method for controlling a steering system in various advanced driver assistance scenarios. Compared with existing technologies, this invention meets the usage scenarios of assisted automatic parking, driver-in-the-loop, driver-hands-free, and emergency avoidance; it receives the target rack position requested by the vehicle's host computer, and the electronic power steering system calculates the required motor torque through its internal controller to achieve lateral control response; the safety function of limiting the maximum rack speed of the steering system can meet the functional safety target of preventing oversteering from being violated; when the driver assistance function is in operation, it can simultaneously meet the requirements of angle following, smooth switching, and comfortable hand feel control in human-machine co-driving scenarios.

The present invention provides a method and system of historical emotion based driver advanced assistance. In this method, a combination of external environment to a vehicle on which the advanced driver assistance system (ADAS) is mounted fetched by forward looking cameras is combined with rear looking camera for internal environment or driver state, is generated. The generated combination is utilized to analyze is there is any critical situation that is upcoming. For providing feedback for such situation, the ADAS fetches a historical combination situation similar to the current situation combination. The intensity of the feedback is varied as per the driver reaction to the feedback provided to the driver at such historical combination situation.

The present application relates to a method, system, computer storage medium and computer device for testing advanced driver assistance functions. According to one aspect of the present application, a method for testing advanced driver assistance functions is proposed, the advanced driver assistance functions including warning type functions and control type functions, the method comprising the following steps: testing the warning type functions through a first test channel, and releasing the warning type functions to a third test mode in response to the warning type functions passing the test in a first test mode and a second test mode; and testing the control type functions through a second test channel, and pushing the control type functions to the third test mode in the first test channel to be tested simultaneously with the released warning type functions while entering the second test mode in response to the control type functions passing the test in the first test mode.

The present disclosure relates to a method and a device for controlling an advanced driver-assistance system (ADAS) based on the load and the slope of a driving vehicle. The method may include calculating information on the load of the driving vehicle; transmitting a required acceleration while controlling an adaptive cruise control of the driving vehicle; comparing the time required to reach the required acceleration with the time required to reach an actual acceleration of the driving vehicle; transmitting a warning signal about overload to a driver based on the reaching time; calculating information on the slope on which the driving vehicle is traveling; and controlling the adaptive cruise control based on the information on the slope of the driving vehicle.

A method for adjusting a region of interest (ROI) in a dynamic image is implemented by an in-vehicle electronic device installed on a vehicle. The method includes: for each of a first predetermined number of successive image frames, detecting a pair of lane line segments and an intersection point; using the first predetermined number of pairs of lane line segments and the first predetermined number of intersection points, determining a first vanishing point and a temporary ROI; for each of a second predetermined number of successive image frames, calculating a pair of slopes associated with the pair of lane line segments within the temporary ROI; obtaining a second vanishing point based on the second predetermined number of pairs of slopes; and in a case where the second vanishing point and the first vanishing point correspond with one another, storing the temporary ROI as an updated ROI.

A system for automated vehicle identification and detection. The system may utilize native, in-vehicle advanced driver-assistance (ADAS) systems. Native, in-vehicle sensors may include sensors installed by vehicle manufacturers as part of the building of the vehicle for the purposes of assisting drivers in driving and parking functions (i.e., as part of an ADAS system). The system may process data from the native sensors and cameras to obtain features indicative of vehicles in the surroundings proximate to the vehicle on which the ADAS system is installed.

This application relates to an advanced driver assistance system, comprising: a first acquisition module for acquiring and outputting signals of the driving environment of the vehicle; a second acquisition module activated when the driver operates the vehicle to acquire and output signals of the driver's driving behavior; a training data generation module for acquiring the output driving environment and the output driving behavior of the driver, and generating training data; an information processing module for training and storing a driving style model for the driver, the driving style model describing the correspondence between the driving environment and the driving behavior of a single driver, wherein the information processing center trains and / or updates the driving style model upon obtaining the training data; and a decision module for downloading the driving style model corresponding to the driver of the current vehicle from the information processing center, and outputting driving behavior corresponding to the driving environment based on the driving environment output by the first acquisition module and the driving style model.

A system for automated vehicle identification and detection. The system may utilize native, in-vehicle advanced driver-assistance (ADAS) systems. Native, in-vehicle sensors may include sensors installed by vehicle manufacturers as part of the building of the vehicle for the purposes of assisting drivers in driving and parking functions (i.e., as part of an ADAS system). The system may process data from the native sensors and cameras to obtain features indicative of vehicles in the surroundings proximate to the vehicle on which the ADAS system is installed.

A computer-implemented method that, when executed by data processing hardware of a vehicle, causes the data processing hardware to perform operations is provided. The operations include gathering sensor data, calculating one or more groove detection confidence values with the sensor data using one or more groove detection modules, evaluating the one or more groove detection confidence values using a road groove detection fusion module, determining whether a road groove is detected, communicating road groove data to an advanced driver-assistance system, and adapting trajectory and steering controls of the vehicle.

The present invention provides a vehicle lane change behavior warning method based on a hierarchical reasoning model, which can be applied to the technical fields of advanced driver assistance and autonomous driving. The method includes: calculating the autocorrelation motion characteristics and cross-correlation motion characteristics of the target vehicle through a hierarchical reasoning model, wherein the hierarchical reasoning model includes a first module and a second module; according to the autocorrelation motion characteristics and cross-correlation motion characteristics of the target vehicle, calculating the lane change probability of the target vehicle through the attention weight generation network and lane change probability prediction network of the first module; according to the lane change probability of the target vehicle, determining the current state of the target vehicle through the Gaussian hidden Markov model of the second module; according to the current state of the target vehicle, deciding whether to issue an alarm signal or implement deceleration through the hierarchical reasoning model. The above method provided by the present invention can help the driver or the autonomous driving vehicle to react in advance, prevent collisions with lane-changing vehicles, and improve the safety of the traffic system.

The present disclosure provides a system (102) and a method (500) for enabling real-time warnings for sensory-impaired persons (402-1) in a vehicle (100). The system (102) comprises a control unit (104) that receives input data from one or more sensors (112) corresponding to the vehicle's (100) environment. The control unit (104) integrates the input data with one or more advanced / assisted driver (AD) functions to enable one or more advanced driver responses (AD responses). The control unit (104) generates one or more real-time warnings based on the one or more AD responses and the input data.The control unit (104) delivers one or more real-time warnings to the one or more sensorily impaired person(s) (402-1) using a preferred response mode which includes at least one of the following components: an interface unit (106), an audio unit (108) and a haptic feedback unit (110) of the vehicle (100).

This application provides an information transmission method and apparatus, relates to interference processing of a cooperative radar, and is applicable to internet of vehicles scenarios, such as a vehicle-to-everything (V2X) scenario, a long term evolution-vehicle (LTE-V) scenario, and a vehicle-to-vehicle (V2V) scenario. Therefore, information related to a transmit power can be transmitted through an interface of a radar detection apparatus, and a capability of an automated driving system or an advanced driver assistant system (ADAS) is improved, to adapt to a variable driving environment. The information transmission method includes: A first detection apparatus receives indication information, where the indication information is used to indicate the first detection apparatus to determine a first transmit power used for transmitting a radar signal; and the first detection apparatus determines the first transmit power based on the indication information.

Disclosed are a vehicle control system and a driving method of a vehicle using the vehicle control system. The vehicle control system includes a vehicle controller that controls driving of a vehicle and one or more processors that process data related to the driving of the vehicle, receive information about a longitudinal gradient and a transverse gradient of the vehicle from a server, and control a driving speed, and an Advanced Driver Assistant System (ADAS), and / or set a landmark, based on the information on the longitudinal gradient and the transverse gradient.

This invention provides an anti-fraud solution for distributing voice content to smart devices, relating to the field of voice content distribution and security anti-fraud technology. It includes S1, device initialization: After the smart device is powered on and connected to the network, it initiates a token acquisition request carrying the device identifier to the hardware server. The hardware server forwards the request to the content platform server. The content platform server generates an encrypted, unique token with an expiration date and binds it to the device identifier. The hardware server caches the token and pushes it to the device. The device caches the token. This significantly reduces the integration technology threshold for smart device manufacturers, eliminating the need for advanced driver layer development capabilities. SDK embedding can be completed with simple configuration. It also avoids resource conflicts between risk control functions and the device's host system, shortening the new device adaptation and integration cycle by more than 80% and significantly improving product launch efficiency.

Method of adapting a multi-task machine learning model of an advanced driver assistance system (ADAS) 16 to an updated set of tasks, comprising: providing a pre-trained machine learning model defined

An image display method includes obtaining motion information of a vehicle, where the motion information includes first information or an upper-layer advanced driver assistant system (ADAS) instruction, where the first information includes one or two of displacement information of a brake pedal and displacement information of an acceleration pedal, and the ADAS instruction is used to instruct the vehicle to travel at an accelerated speed or at a decelerated speed; and zooming in or zooming out a first image based on the motion information of the vehicle, where the first image is an image displayed on a vehicle-mounted display of the vehicle.

A method for calibrating an advanced driver-assistance system (ADAS) for a vehicle comprises: receiving first information by a mobile device having a camera and a light detection and ranging (LiDAR) device, the first information including three-dimensional (3D) specifications of the physical dimensions of the vehicle having the ADAS; scanning physical targets for the vehicle and ADAS by the mobile device, the scan being performed using the camera and LiDAR device after receiving the first information, and the scan generating frame data; tracking the vehicle in the frame data using the first information by the mobile device; generating an output by the mobile device indicating whether the physical targets are positioned at the target location for the ADAS; and starting the ADAS calibration process using the physical targets after the output has been generated.

In an advanced driver-assistance system (ADAS), RAW sensor image processing for a machine vision (MV) application is important. Due to different color, e.g., red / green / blue (RGB), color components, being focused by the lens at different locations in image plane, the lateral chromatic aberration phenomenon may sometimes be observed, which causes false color around edges in the final image output, especially for high contrast edges, which can impede MV applications. Disclosed herein are low-latency, efficient, optimized designs for chromatic aberration correction (CAC) components. An in-pipeline CAC design may be used to perform on-the-fly CAC without any out-of-pipeline memory traffic; enable use of wide dynamic range (WDR) sensors; uses bicubic interpolation; support vertical and horizontal chromatic aberration color channel offsets, reduce CAC line memory requirements, and support flexible look-up table (LUT) down-sampling factors to improve the spatial precision of correction and accommodate popular image sensor resolutions.

A vehicle system includes: at least one of (a) an advanced driving system and (b) an advanced driving feature, the advanced driving system being configured to assist a vehicle driver in performing at least one driving maneuver based on input from at least one of sensors and cameras; and a usage module configured to: when at least one of (a) the advanced driving system and (b) the advanced driving feature is off, determine whether at least one of (a) the advanced driving system and (b) the advanced driving feature can be turned on and used based on at least one of the following: at least one current vehicle operating parameter; at least one current road parameter; at least one current traffic parameter; and at least one current environmental parameter.

Vehicle advanced driver assistance (ADAS) and autonomous driving feature simulation and verification systems and method receive a driving log file and map data corresponding to an on-road driving scenario by a vehicle and overlay the driving log file and the map data to generate combined data that localizes a position of the vehicle. A whitelist comprising a set of included objects for an ADAS / autonomous driving feature simulation and (ii) a blacklist comprising a set of excluded objects for the ADAS / autonomous driving feature simulation and a template for the ADAS / autonomous driving feature simulation are then obtained. Finally, an ADAS / autonomous driving feature simulation scenario is generated in a desired format using the white and blacklists and the template and executed using a corresponding simulation tool and a result of the executed simulation of the ADAS / autonomous driving feature simulation scenario is verified.

The invention relates to an autonomous vehicle navigation system utilizing infrastructure-mounted sensors (100), such as cameras, LiDAR, radar, thermal, and infrared sensors, to monitor road conditions. These sensors (100) are mounted on street poles (101) and ceilings (102), transmitting real-time environmental data to a centralized AI server (108). The AI server (108) processes the data using machine learning algorithms to make navigation decisions and provide autonomous vehicles with continuous updates. The system includes a V2X connectivity box in each vehicle that enables communication with infrastructure via GSM, Wi-Fi, 5G, 6G and satellite networks, reducing the need for onboard sensors and computational load. The present invention is compatible with Advanced Driver-Assistance Systems (ADAS) technologies. The present invention enhances vehicle safety, reduces manufacturing costs, and enables real-time hazard detection and adaptive routing. The system is scalable across urban and rural environments, contributing to smarter traffic management and sustainability.

1. The name of the design product: vehicle navigation advanced auxiliary driving information display graphical user interface of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. The use of the graphical user interface: for vehicle navigation and advanced auxiliary driving information display. 6. Other circumstances that need to be explained: specify the basic design: design 1.

A vehicle including an integrated advanced driver-assistance system (ADAS) vehicle controller, a plurality of sensors, and a drone is provided. The vehicle controller includes a memory configured to store program instructions, and a processor configured to execute the program instructions. The plurality of sensors include at least a first sensor configured to transmit data to the vehicle controller. The drone is removably coupled to the vehicle. The drone includes at least a second sensor configured to transmit data to the processor. The vehicle controller is programmed to determine whether to launch the drone from the vehicle when the vehicle approaches a pre-defined operating environment, and to selectively change operation of the vehicle at least partially based on data received from the drone.