22 results about "Advanced driver" patented technology

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.

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.

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.

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.

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.

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.

The in-vehicle system for distributing signals to controllers includes: an ADAS controller configured to control the vehicle's advanced driver-assistance system (ADAS); an infotainment controller configured to control the vehicle's infotainment system; a sensor device; and a switch, the switch being powered coaxially from the ADAS controller or the infotainment controller, and configured to distribute a first signal from the sensor device to either the ADAS controller or the infotainment controller.

The present disclosure relates to a method performed by a trajectory monitoring system of a vehicle for assessment of a vehicle control system of an advanced driver-assistance system, ADAS, or autonomous driving, AD, system of the vehicle. The trajectory monitoring system determines in view of a reference system a planned vehicle trajectory adapted to be executed by the vehicle control system during a predeterminable time period (T). The trajectory monitoring system furthermore determines following initiation and / or completion of the time period, an actual vehicle trajectory of the vehicle during the time period in view of the reference system. Moreover, the trajectory monitoring system determines an accuracy deviation measure indicating deviation between the actual vehicle trajectory and the planned vehicle trajectory. The trajectory monitoring system further communicates acknowledgment data indicative of the accuracy deviation measure.

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.

The present invention relates, in general, to the technical fields of autonomous driving and advanced driver assistance. More specifically, the present invention relates to preventing collisions and dangerous driving situations. The present invention may relate to a method for predicting driver behavior using at least one computing device. The method may include the steps of: receiving a plurality of motion data elements characterizing the motion of at least one vehicle in at least one specific driving situation; constructing a behavior model representing expected driver behavior in the at least one specific driving situation based on the plurality of motion data elements; and inferring the behavior model for at least one incoming motion data element to predict expected driver behavior in the specific driving situation.

The embodiment of the invention relates to the field of intelligent driving, in particular to a low-order advanced driving assistance method, system, device and product. The method comprises the following steps: setting a low-order ADAS special data calling interface in the SOC; obtaining a node result of a high-order intelligent driving algorithm in the SOC through the interface, wherein the node result of the high-order intelligent driving algorithm comprises a perception fusion layer data result, a scene decision-making layer data result and a trajectory prediction layer data result; generating a control strategy according to a node result, and performing low-order ADAS control based on the control strategy; through the control mechanism, the low-order ADAS function can maintain higher robustness and decision accuracy in a complex environment, the system perception and control performance is improved, the hardware cost and the development period are remarkably reduced, a new implementation path is provided for the integrated and modular design of the intelligent driving system, and the hardware cost is reduced.