80 results about "Advanced driver assistance systems" patented technology

Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing alerts to the driver of a vehicle, based on anomalies detected between driver behavior and environment captured by the outward facing camera. Various aspects of the driver, which may include his direction of sight, point of focus, posture, gaze, is determined by image processing of the upper visible body of the driver, by a driver facing camera in the vehicle. Other aspects of environment around the vehicle captured by the multitude of cameras in the vehicle are used to correlate driver behavior and actions with what is happening outside to detect and warn on anomalies, prevent accidents, provide feedback to the driver, and in general provide a safer driver experience.

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.

An Augmented Reality (AR) display device interoperating with a vehicle and a method for operating the same are disclosed. An AR display device according to the present disclosure can provide an intuitive AR route to a parking available area or charging available area in a parking lot or charging station based on Advanced Driver Assistance Systems (ADAS) sensing data of a vehicle and / or control data of a parking lot / charging station. It is recognized that the vehicle has arrived in from of a selected parking space or charger, and an AR graphic interface can be changed in real time to sequentially guide forward driving, a change point to reverse driving, and reverse driving to the vehicle in correspondence to a current driving state of the vehicle, such that the vehicle can be parked accurately in the parking space or in front of the charger.

A method for detecting anomalies in image data captured by a vehicle sensor includes receiving first and second image frames, and first and second observation clouds generated based on LiDAR data capt

The disclosed technology teaches a system and method for providing driver assistance alerts to a driver using an end-to-end artificial intelligence advanced driver assistance system. The disclosed techniques further include receiving a series of environmental data of a driving state including at least video from a camera, return data from an optical sensor, and location data from a GNSS receiver, where the camera, the optical sensor, and the GNSS receiver are coupled to a processor carried by a vehicle; processing the environmental data as input to an end-to-end neural network, wherein the end-to-end neural network is trained to generate specified steering and speed control actions in response to a current driving state; analyzing hidden layer data and output data from the end-to-end neural network to estimate collision avoidance data; and presenting to the driver a user interface including a driver assistance alert based on the collision avoidance data.

A user equipment (UE) may obtain, from at least one memory, an indication of a first gaze pattern of a driver of a vehicle. The UE may obtain a second gaze pattern of the driver of the vehicle based on a first set of sensor data from a first set of sensors associated with the vehicle during a first time period. The UE may adjust a sensitivity of an advanced driver assistance system (ADAS) associated with the vehicle in response to a difference in visual engagement in a visual region of the driver between the second gaze mode and the first gaze mode greater than or equal to a threshold amount.

The application discloses a kind of integrated multifunctional advanced automatic driving auxiliary system, including five kinds of vehicle driving modes of constant speed cruise mode (CC), low performance adaptive cruise mode (LACC), high performance adaptive cruise mode (HACC), autonomous lane changing mode (ALC), automatic emergency braking mode (AEB). Vehicle detects the state information of surrounding vehicles and environment through sensing device, calculates characteristic quantity and correlation function in real time through extension control strategy, determines the extension set and measure mode of belonging, and further determines the driving mode of vehicle. When the state information of surrounding vehicles and environment changes, vehicle can switch between different driving modes. When the speed of current vehicle continues to drop and the surrounding environment meets the lane changing condition, vehicle can automatically switch to autonomous lane changing mode (ALC), switch the following object, and improve driving efficiency. When the current vehicle stops due to failure or suddenly appears obstacle in front, vehicle can automatically switch to automatic emergency braking (AEB) mode, shorten the reaction time, and improve driving safety.

This invention provides an advanced driver assistance system (ADAS) automatic lane changing method and system, relating to the field of vehicle control technology. The method includes: when a vehicle is preparing to change lanes from its current lane to a target lane, selecting an optimal lane gap from among target vehicles in the target lane; calculating a first passable speed based on the traffic flow speed of the target lane; and, in response to the first passable speed, the target distance required for the vehicle to cross the lane's dashed line when entering the optimal lane gap, and the target distance between target vehicles after entering the optimal lane gap all satisfying preset lane change activation conditions, initiating a lane change time counter; and, in response to the lane change time counter exceeding a counting threshold, generating a vehicle lane change request for the vehicle to change lanes from the optimal lane gap to the target lane; and, in response to the vehicle lane change request, controlling the vehicle to enter the optimal lane gap to complete the lane change. This invention addresses the problems of overly rule-based lane change decisions and a lack of monitoring for real-time risks and changes in lane change conditions during advanced driver assistance systems (ADAS) automatic lane changing.

A method for controlling an actuatable safety device for protecting an occupant of a vehicle includes utilizing an advanced driver assistance system (ADAS) to determine that a vehicle collision is impending. In response to determining the existence of an impending vehicle collision, a mass spring damper model uses sensed vehicle longitudinal acceleration (IMU_X) to estimate the occupant movement that will result from the impending vehicle collision. The estimated occupant movement is used to determine how to control deployment of an actuatable safety device in response to the crash.

Provided is a chassis-integrated controller. A receiver configured to receive state information from each of individual chassis controllers provided in a vehicle and receive route information of the vehicle from an advanced driver assistance system. A control signal generator configured to determine a target vehicle motion for the vehicle to move according to the route information and generate control signals for the individual chassis controllers, to control the vehicle to move according to the target vehicle motion. A motion limiter configured to generate maximum curvature information of a curvature at which the vehicle is able to maneuver based on at least one of the state information or road surface information. A transmitter configured to transmit the maximum curvature information to the advanced driver assistance system and transmit the control signals to the individual chassis controllers.

Method for the automated calibration of functions of a driver assistance system (Advanced Driver Assistance System ADAS / Automated Driving System, ADS) for a variety of scenarios and vehicle configurations, comprising: - Training (S10) a learning reinforcement agent (410) of an optimization module (400) with a training data set (240), wherein the optimization module (400) comprises at least the learning reinforcement agent (410), an action module (420), an environment module (430), a state module (440) and a reward module (450), and wherein the training data set (240) contains calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) of an ADAS / ADS system (10); - generating (S20) a strategy of the learning reinforcement agent (410) for varying calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) in the form of actions (A i) for optimising calibration data sets (15) of ADAS / ADS systems (10); - Entering (S30) a calibration data set (15) of the ADAS / ADS system (10) with calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) via a user interface (220) of an input module (200) or from a database (300); - Selecting (S40) a large number of test cases (T i ) from the database (300) and generating simulation environments with simulation scenarios from the test cases (T i ), where a test case (T i ) to select a parameterized scenario (SZp i ), where both scenario parameters (P i ) as well as associated scenario parameter values ​​(PV i ) not all are fixed, concrete scenario parameters (Pc i ) and concrete scenario parameter values ​​(PVc i) and further information for simulating the behavior of the ADAS / ADS system (10) in a specific driving situation and for managing a specific driving task; carrying out (S50) simulations of the behavior of the ADAS / ADS system (10) in the simulation scenarios, namely in concrete scenarios (SZc i ), in which the concrete scenario parameters (Pc i ) and the corresponding concrete scenario parameter values ​​(PVc i ) or value ranges of the concrete scenario parameter values ​​(PVc i ) and generating simulation results, wherein the simulation results reflect the system behavior of the ADAS / ADS system (10) in the simulation scenarios; - Evaluate (S60) the simulation results in the form of key performance indicators (KPI i ) which describe the behavior of the ADAS / ADS system (10) in the numerous test cases (T i ) defined simulated concrete scenarios (SZc i) and performance values; - Determining (S70) states (S i ) from the key performance indicators (KPI i ) and rewards (R i ) from the performance values; - Calculate (S80) modeled calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) to achieve improved system behavior of the ADAS / ADS system (10) in the simulation scenarios by the trained learning reinforcement agent (410) in several iterations until an optimized calibration data set (17) has been generated; - generating and outputting (S90) output results (550) by an output module (500), wherein the output results (550) contain the optimized calibration data set (17).

A navigation system for assisting a host vehicle with respect to a road segment based on real-world scenarios. The system includes a processor comprising circuitry and memory. Onboard the host vehicle, a camera captures images of the road segment and the vehicle's surroundings. The captured images are used to create graphical representations that illustrate the semantic relationships between the objects depicted. The GNN-based system uses nodes to identify objects and edges to identify relationships from the captured images. The GNN output predicts the probability of a predetermined event type occurring. The host vehicle then executes a navigation response based on the predicted probability of the event occurring.The result is an autonomous and advanced driver assistance system that accurately predicts the behavior of pedestrians, vehicles and other road users to ensure the safest possible host vehicle navigation.

Systems and methods for detecting potentially hazardous objects within a vehicle are provided. The present disclosure is directed to an Advanced Driver Assistance System (ADAS) for a vehicle, the system configured to detect potentially hazardous objects inside the vehicle, the system comprising at least one sensor unit, a processing unit, and an output unit, the sensor unit is configured to monitor at least a portion of the interior of the vehicle and to transmit the acquired sensor data to the processing unit, and wherein the processing unit is configured to analyze the transmitted sensor data, detect objects within the vehicle based on the analyzed sensor data, classify the detected objects as potentially hazardous objects, and transmit the potentially hazardous objects to the vehicle. And when the detected object is classified as a potential dangerous object, the output unit is triggered to give an alarm.

The present application discloses a method for preventing the rear section from rolling over during the path tracking process of a two-section vehicle. The method takes into account the vehicle's dynamic performance and prevents the vehicle from rolling over during the path tracking process. The method comprises: establishing a front section model and a rear section model of a multi-section vehicle to ensure effective tracking of the front section and accurate tracking of the rear section; determining the driving condition through an advanced driver assistance system module based on road information acquired by a visual sensor, and determining whether the vehicle has deviated; obtaining the actual vehicle body roll angle through an unscented Kalman filter estimation module; inputting the theoretical vehicle body roll angle into a rollover condition determination module, and outputting the wheel braking force to achieve the effect of differential braking; the present invention designs a rear section trajectory error model based on a front section preview error model, and designs a rollover warning system logic flow based on a dynamic model prediction, effectively improving the vehicle driving stability and safety during the path tracking process of the two-section vehicle.

A vehicular driving assist system includes (i) a forward-viewing camera disposed at a windshield of a vehicle viewing forward of the vehicle through the windshield and (ii) an in-cabin camera disposed at an interior rearview mirror assembly in an interior cabin of the vehicle and viewing at least a steering wheel of the vehicle. The forward-viewing camera and the in-cabin camera are operable to capture image data. Image data captured by the forward-viewing camera is processed for at least one advanced driver assistance system and to determine a hazardous condition ahead of the vehicle. Based at least in part on processing of image data captured by the in-cabin camera, at least one hand of a driver of the vehicle is determined to not be on the steering wheel of the vehicle during approach of the vehicle to the determined hazardous condition.

1. Name of the product in this design: Automotive Camera (ADAS). 2. Purpose of this design: To be installed on a vehicle to capture images of the vehicle's interior or exterior. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

A method of operating an advanced driver assistance system (ADAS) can include performing a map data reload operation on a first map data buffer containing map data obtained from a first source. A switching operation can be performed to switch from the first map data buffer to a second map data buffer containing map data obtained from a second source. An electronic horizon can be generated based on the map data contained in the second map data buffer and used to perform a first ADAS operation. Another switching operation involves switching back from the second map data buffer to the first map data buffer, which now contains map data obtained from the first source during the map data reload operation. Then, another electronic horizon can be generated based on the map data contained in the first map data buffer and used to perform one or more ADAS operations.

A method for controlling an actuatable safety device for protecting an occupant of a vehicle includes utilizing an advanced driver assistance system (ADAS) to determine that a vehicle collision is imminent. In response to determining that an imminent vehicle collision exists, a mass-spring-damper model uses a sensed vehicle longitudinal acceleration (IMU_X) to estimate occupant movement that would result from the imminent vehicle collision. The estimated occupant movement is used to determine how to control deployment of the actuatable safety device in response to the collision.

The invention relates to a calibration equipment pose calibration method, device and system, a medium, equipment and a vehicle. The calibration device is used for calibrating the installation position of a device to be calibrated in the vehicle advanced auxiliary driving system. The method comprises the steps that a measurement value obtained by measuring a preset point on the calibration device through a measurement device is acquired; converting the measurement value into a conversion value under a vehicle coordinate system according to a conversion matrix between a measurement coordinate system of the measurement equipment and the vehicle coordinate system; determining a current pose parameter value of the calibration equipment based on the conversion value; according to the current pose parameter value and a target pose parameter value, the pose compensation value of the calibration device is determined, and the target pose parameter value is the parameter value of the target pose of the calibration device under the vehicle coordinate system.

The present disclosure relates to a lane-related advanced driver assistance system and a control method, and a vehicle having the same. A system according to an embodiment of the present disclosure may include a memory for storing an image captured by a camera of a vehicle; and a processor for controlling a lane-related Advanced Driver Assistance System (ADAS) in the vehicle using information stored in the memory, wherein the processor is configured to: identify a driver's intention in a rear region of a target section, which is a divergence point section without an indication line, using the image; and determine whether to perform the lane-related ADAS function in the target section according to the identified driver's intention.

An advanced driver assistance system, method and vehicle for assisting a driver of a vehicle is provided. The system includes a sensor unit, a processing unit and a display unit. The sensor unit is configured to sense an environment of the vehicle. The processing unit is configured to determine at least one feature of the environment based on the sensing output. The processing unit is configured to determine a risk zone for the feature for a current time by estimating a respective risk with respect to the feature at each of two or more virtual positions of the vehicle based on at least one parameter of the vehicle at the current time, and forming a risk zone based on the two or more risks. The display unit is configured to display the environment of the vehicle with the feature and the risk zone of the feature.

A system for automatically parking a commercial vehicle includes a service braking system having at least one brake valve, a brake controller for controlling the service braking system, and an advanced driver assistance system (ADAS) controller. The ADAS controller communicates with the brake controller and requests activation of the foot brake of the commercial vehicle until the vehicle comes to a stop. In response to the ADAS controller's request to activate the service brake, the brake controller determines that the commercial vehicle should remain stationary and activates at least one brake valve to release system pressure, mechanically parking the vehicle.

A method increases the fidelity of a lane localization function on a motor vehicle by receiving input signals indicating a relative position of the vehicle with respect to a road. The input signals include GPS and geocoded map data, and an electronic turn signal indicating activation of a turn signal stalk. A sensor-specific lane probability distribution is calculated by the lane localization function using the input signals. The various distributions are fused by the localization function to generate a primary lane assignment. The primary lane assignment corresponds to the road lane with the highest probability among a set of possible lane assignments. In response to the primary lane assignment, an autonomous steering control action is performed on the motor vehicle using an advanced driver assistance system (ADAS). The motor vehicle has a controller that performs the method, for example, by executing instructions from a computer-readable medium.

A steering system for a vehicle that includes a driver-controllable steering device; a power steering actuator coupled to a steerable vehicle wheel; an advanced driver assistance system (ADAS) including a spatial monitoring system; and a steering controller. A steering control routine determines a target path and a target trajectory for the vehicle based upon an input from the spatial monitoring system; determines a driver steering command; determines external factors related to the target path and the target trajectory; determines a weighting factor for the driver steering command based upon the plurality of external factors; determines a steering angle command based upon the driver steering command, the weighting factor for the driver steering command, the target path, and the target trajectory; and controls the power steering actuator to control the steerable wheel responsive to the steering angle command.

An apparatus for generating a test case for an advanced driver assistance system (ADAS). The apparatus includes a communication interface and a processor connected to the communication interface. The processor is configured to generate virtual light detection and ranging (LIDAR) data by performing a process of receiving, via the communication interface, raw data generated using a LIDAR, detecting an object contained in the raw data, and randomly arranging LiDAR data within a boundary of the object.

The disclosure relates to a method and system for extracting disparity information out of a pair of stereo camera images for processing images of camera sensors for Automated Driving or Advanced Driver Assistance Systems in a vehicle. The method includes: a) receiving a pair of unrectified stereo camera images, b) performing a 2D-matching of the received pair of images using a trained convolutional neural network and providing the 2D-displacement on a pixel-by-pixel level as disparity information, and c) outputting the 2D-displacement on pixel level. The method is robust which does not require a high precision rectification. Applying rectification on the disparity values after the matching process leads to reduced algorithmic complexity with simultaneous preservation of all information.

Method for the automated calibration of functions of a driver assistance system (Advanced Driver Assistance System ADAS / Automated Driving System, ADS) for a variety of scenarios and vehicle configurations, comprising: - Training (S10) a learning reinforcement agent (410) of an optimization module (400) with a training data set (240), wherein the optimization module (400) comprises at least the learning reinforcement agent (410), an action module (420) with an action space, an environment module (430), a state module (440) and a reward module (450), and wherein the training data set (240) contains calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) of an ADAS / ADS system (10); - generating (S20) a strategy of the learning reinforcement agent (410) for varying calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) in the form of actions (A i) for optimising calibration data sets (15) of ADAS / ADS systems (10); - Entering (S30) a calibration data set (15) of the ADAS / ADS system (10) with calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) via a user interface (220) of an input module (200) or from a database (300); - Selecting (S40) a large number of test cases (T i ) from the database (300) and generating simulation environments with simulation scenarios from the test cases (T i ) in the environment module (430); - performing (S50) simulations of the behavior of the ADAS / ADS system (10) in the simulation scenarios and generating simulation results, wherein the simulation results reflect the system behavior of the ADAS / ADS system (10) in the simulation scenarios; - Evaluate (S60) the simulation results in the form of key performance indicators (KPI i ) and performance values; - Determining (S70) states (S i ) from the key performance indicators (KPI i ) and rewards (R i ) from the performance values; - Calculate (S80) modeled calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) to achieve an improved system behavior of the ADAS / ADS system (10) in the simulation scenarios by the trained learning reinforcement agent (410) in several iterations until an optimized calibration data set (17) has been generated, wherein to determine the decisions of the learning reinforcement agent (410) regarding the actions to be carried out (A i ) the action space of the action module (420) into discrete parameter step sizes with which a certain parameter (P i) during which the optimization is adapted or updated, is divided such that, in addition to a first parameter step size, which is predetermined by the discretization of the action space, a second parameter step size is added, the amount of which is smaller than the amount of the first parameter step size, and wherein the learning reinforcement agent (410) determines the calibration parameter value (PVcal i ) of a calibration parameter to be varied (Pcal i ) by one or more parameter increments, such that the learning reinforcement agent (410) decides whether to use a value of a calibration parameter (Pcal i ) by a certain number of the given first step size of the action area and by a certain number of the smaller second step size by adding, subtracting or leaving the first and second step sizes unchanged; - generating and outputting (S90) output results (550) by an output module (500), wherein the output results (550) contain the optimized calibration data set (17).

1. Name of the product in this design: Calibration and calibration plate (ADAS). 2. Purpose of this design: To be attached to the surface of vehicle glass to assist advanced driver assistance systems in diagnostic testing and calibration. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing alerts to the driver of a vehicle. Combinations of co-occurring driving events may be detected and used to warn on anomalies, prevent accidents, provide feedback to the driver, and in general provide a safer driver experience.

The present invention relates to the fields of advanced driver assistance systems (ADAS), computer vision and machine learning (ML). The present invention provides an ML-based method for training a neural network based on a synthetic image (generated from a real image or a virtual image) to improve the process of determining a depth map, relative pose or semantic segmentation. Therefore, the present invention provides a device (100) for determining a depth map (101), a relative pose (102) or a semantic segmentation (103). The device (100) comprises: a neural network (104) for determining the depth map (101), the relative pose (102) or the semantic segmentation (103) based on an input image (105) during an inference phase; a generator (106) for generating a synthetic image (107) based on a real image (108) or a virtual image (109) and according to a loss function (110) during a training phase; the neural network (104) is trained based on the synthetic image (107); wherein the loss function (110) includes a semantic edge function (111).