183 results about "Advanced driver assistance systems" patented technology

A system for detecting sensor position change (DCPC) on movable vehicle components includes two or more sensors on at least one of the movable vehicle components. The sensors detect optical information within a distinct field of view (FOV) about an environment surrounding the vehicle. The distinct FOV of each sensor at least partially overlaps with an FOV of at least one other sensor. The system includes controllers that execute an application for DCPC. The DCPC application acquires overlapping optical information from the sensors, computes a conditional correspondence probability distribution for feature points in the overlapping optical information, computes a normalized joint entropy of the feature points, determines that the sensor is in a position manageable by the DCPC, continuously monitors positions of each of the sensors, selectively dynamically aligns the sensors, and ensures that the sensors are calibrated for vehicle perception tasks including advanced driver assistance system (ADAS) functions.

Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS), lane drift detection, passing guidance, bandwidth conservation and caching based on road data, vehicle speed correction, suspension and vehicle system performance tracking and control, road estimation calibration, and others.

Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS), lane drift detection, passing guidance, bandwidth conservation and caching based on road data, vehicle speed correction, suspension and vehicle system performance tracking and control, road estimation calibration, and others.

A method for cooperative vehicle operation includes receiving a first target angle signal associated with a handwheel of a vehicle, receiving a second target angle signal associated with an autonomous steering system of the vehicle, and, in response to a determination that a handwheel angle error between the first target angle signal and the second target angle signal is less than a handwheel error threshold: generating a handwheel motor torque command based on a position control motor torque command and a torque control motor torque command; and controlling a position of the handwheel of the vehicle based on the handwheel motor torque command.

Aspects presented herein may enable a UE (e.g., a vehicle, an autonomous vehicle, an on-board unit (OBU) of a vehicle, an advanced driver assistance systems (ADAS) of a vehicle) to calculate and output safety margins for objects around the UE or a vehicle. In one aspect, a UE receives or transmits a request to provide safety margin visualization assistance. The UE obtains, in response to the request, an indication of a safety margin associated with an object based on a distance between the UE and the object and at least one of: a size of the object, a speed of the object, a location of the object, a type of the object, a direction of the object, or a movement pattern of the object. The UE outputs the safety margin associated with the object.

Disclosed herein are system, method, and device embodiments for determining the mental state of a driver using gaze tracking and object detection. Specifically, an Advanced Driver Assistance System may determine if the driver's gaze is associated with spatial locations of objects of interest in the surrounding environment. The surrounding environment may be represented with a 360-degree, three-dimensional model generated by the external-facing cameras and other sensors. The driver's gaze direction may be represented by a heatmap derived based on the driver's eye movements. Whether or not the driver's gaze is associated with a spatial location of an object may be used as evidence about the driver's mental state. The system may perform appropriate actions based on the mental state including generating alerts, engaging mechanical systems, and adjusting parameters in ancillary safety systems to increase or decrease tolerance. The mental state may be tracked throughout a driving session and stored in a driver profile.

Disclosed herein are systems and methods for generating and displaying navigation guidance by utilizing data acquired by an advanced driver assistance system (ADAS) for driver behavior prediction and route planning. In embodiments, a method comprises generating navigation guidance based on a plurality of driver state events detected via an ADAS, and outputting the navigation guidance via a navigation system, wherein the navigation guidance comprises at least one of a map layer and a route recommendation.

The invention relates to a hidden ultrasonic sensing system suitable for advanced driver assistance. Exemplary sensor controllers, sensors, sensing systems, and sensing methods that can enable cost-effective implementation of ADAS (Advanced Driver Assistance System) features with hidden sensors. As one example, an exemplary sensing method includes emitting a train of acoustic pulses through a surface on an ultrasonic transducer; receiving the acoustic signal with a MEMS (Micro Electro Mechanical System) microphone, the MEMS microphone representing the acoustic signal as an electrically received signal; and processing the electrical receive signal to detect reflections of the acoustic pulse train.

The technology disclosed teaches a system and methods for providing driver assistance alerts to a driver using an end-to-end artificially-intelligent advanced driver assistance system. The technology disclosed further includes receiving environmental data for a sequence of driving states including at least video from a camera, returns from an optical sensor, and location data from a GNSS receiver, wherein 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 prescriptive steering and speed control actions in response to a present 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 driver assistance alerts based on the collision avoidance data.

In various examples, a line of sight (LOS) filter may be used to determine whether or not a detected object represents a potential collision risk, filter out detected objects that are deemed not to be a collision risk, and provide a representation of the remaining detected objects for evaluation by one or more downstream automatic emergency braking (AEB), collision mitigation braking systems (CMBS), and / or other modules of an ego-machine. For example, the LOS filter may use an estimated position of a detected object to derive a corresponding LOS angle between the heading of the ego-machine and the detected object, and apply one or more thresholds based on the LOS angle to identify detected objects that are non-collision risks. As such, the LOS filter may remove non-collision risks from consideration by the downstream AEB, CMBS, and / or other module(s).

A method comprises: activating a full lane centering control mode in an advanced driver assistance system (ADAS) of a vehicle, wherein in the full lane centering control mode the ADAS keeps the vehicle centered on a trajectory of a lane regardless of surroundings of the vehicle and applies a first torque level against steering input that a driver applies using a steering wheel; detecting, in the full lane centering control mode, a driver input corresponding to a departure from the trajectory; and in response to the driver input, instead activating a manual lane biasing mode in which the ADAS applies a second torque level against the steering input, the second torque level lower than the first torque level, the manual lane biasing mode allowing the driver to steer away from the trajectory.

Aspects presented herein may enable a UE (e.g., a vehicle, an on-board unit (OBU) of the vehicle, an advanced driver assistance systems (ADAS) of the vehicle, a device running a navigation application, etc.) to download map data in packets based on a set of priorities to improve the efficiency of updating / retrieving map data. In one aspect, a UE calculates a route to a destination based on a current location of the UE and map data. The UE receives, from a server, an indication of updated map data associated with the calculated route, where the updated map data includes a plurality of packets. The UE sets a priority for a download of one or more packets of the plurality of packets based on a set of live parameters. The UE downloads the one or more packets of the updated map data based on the set priority.

The technology disclosed teaches a system and methods for providing driver assistance alerts to a driver using an end-to-end artificially-intelligent advanced driver assistance system. The technology disclosed further includes receiving environmental data for a sequence of driving states including at least video from a camera, returns from an optical sensor, and location data from a GNSS receiver, wherein 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 prescriptive steering and speed control actions in response to a present 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 driver assistance alerts based on the collision avoidance data.

An advanced driver assistance system (ADAS) radar includes at least one transmit antenna coupled with a vehicle, the at least one transmit antenna configured to transmit radar signals from the vehicle towards a scene of interest, at least one receive antenna coupled with the vehicle, the at least one receive antenna configured to receive reflected radar signals, and at least one processor. The processor is configured to execute computer-executable instructions to obtain multiple time-samples of the reflected radar signals from the at least one receive antenna at specified periodic intervals, execute an auto-correlation algorithm on the obtained multiple time-samples, normalize a ratio of zeroth lag and first lag according to an output of the auto-correlation algorithm, and compare the normalized ratio of the zeroth lag and the first lag to a specified threshold, to determine whether the reflected radar signals include an identified road clutter object.

A method includes obtaining multiple images of a scene using an imaging sensor associated with a vehicle, where the images of the scene capture lane marking lines associated with a traffic lane in which the vehicle is traveling. The method also includes identifying, in each of at least some of the images, a vanishing point based on the lane marking lines captured in the image. The method further includes identifying an average position of the vanishing points in the at least some of the images. In addition, the method includes determining one or more extrinsic calibration parameters of the imaging sensor based on the average position of the vanishing points. The one or more extrinsic calibration parameters of the imaging sensor may include a pitch angle and a yaw angle of the imaging sensor.

A computer-implemented method comprises: collecting, in a vehicle having an advanced driver assistance system (ADAS), sensor data using an ADAS sensor suite of the vehicle, the sensor data collected while the vehicle is moving and reflecting surroundings of the vehicle; generating a three-dimensional (3D) map of the surroundings using the sensor data; defining a boundary condition regarding the vehicle based on the 3D map; and controlling a closure actuator of a closure of the vehicle based on the boundary condition, the closure actuator configured for opening or closing the closure, the closure actuator controlled without the closure having any obstacle detection sensor.

A system in a vehicle for distributing signals to controllers comprises: an advanced driver assistance system (ADAS) controller configured for controlling an ADAS of the vehicle; an infotainment controller configured for controlling an infotainment system of the vehicle; a sensor device; and a switch, the switch being powered by power over coaxial from the ADAS controller or the infotainment controller, the switch configured for distribution of a first signal from the sensor device to either of the ADAS controller or the infotainment controller.

A system for scheduling vehicle tasks for vehicles is described. The system includes a plurality of vehicles having the advanced driver assistance systems and cellular vehicle-to-everything communication, and a plurality of road side unit (RSU) nodes arranged at predetermined locations to execute driver assist tasks. The system further includes a central base station configured to receive and dynamically maintain a record of task data rates between the vehicles and the RSUs and delay time to return an executed task, and schedule offloading of the tasks for the advanced driver assistance systems. The RSUs are fog nodes for computing the tasks in accordance with the schedule, and the tasks are control tasks for the advanced driver assistance systems.

An advanced driver assistance system (ADAS) configured to assist a driver in low visibility conditions. The ADAS controls operation of one or more automotive systems including an external lighting system, an automotive user interface, and an automotive cruise control system based on the visibility in front of the vehicle. The ADAS system visually assists the driver via an automotive user interface by displaying a streaming video of one or more first front cameras configured to operate in a spectrum visible to the human eye, one or more second cameras configured to operate in a spectrum invisible to the human eye, or a virtual depiction of an environment in front of or surrounding the motor vehicle based on information from an automotive sensory system.

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.

A system can include a mobile device that includes one or more sensors for sensing information during a trip in a vehicle. A hardware processor can execute operations including receiving telematics information produced by one or more sensors during a trip in a vehicle; processing, by a hardware processor, the received telematics information to identify vehicle movement information for the vehicle during the trip; determining, by the hardware processor, a probability that an advanced driver assistance system (ADAS) feature of the vehicle was operational during the trip based, at least in part, on the vehicle movement information for the vehicle during the trip; and determining, by the hardware processor, a risk score for the vehicle or a driver of the vehicle based, at least in part, on the probability that the ADAS feature was operational.

An integrated system for vehicle and trailer management is provided, including a multifunctional tablet and docking station. The system includes an adapter for connecting a trailer to a vehicle, facilitating control over lights, brakes, and camera data, featuring a novel multi-pin configuration that accommodates connections for cameras attached to a trailer in addition to non-video data connections for the trailer and the truck. The docking station processes video signals from trailer-mounted or vehicle-mounted cameras, displaying them on a tablet that may also provide fleet management and fleet safety functionality, including Electronic Logging Device (ELD) functionality, and Advanced Driver Assistance Systems (ADAS) alerts. The tablet can be undocked for mobile tasks like vehicle inspections and delivery confirmations. The system enhances safety, compliance, and operational efficiency by consolidating multiple functions into a single, user-friendly platform, supporting both wired and wireless communication options for stable, real-time video and data transmission.

The invention provides a vehicle joint simulation method and system carrying ACC and ESC. The method depends on a dynamics simulation platform of CarSim software, and vehicle dynamics models of ACC and ESC are integrated; secondly, a multi-dimensional traffic scene is built based on PreScan, various typical road networks are fused, variable illumination (day and night / rain and snow) and intelligent road facilities are integrated, and environment sensing data are generated through a camera, a millimeter wave radar and a laser radar multi-mode sensor; and finally, a simulation system is built in Simulink, real-time data interaction with vehicle dynamics and environmental perception is realized through an S-Function interface, and system verification including a decision planning algorithm and a sensor fusion model is built. According to the invention, a basis example is provided for verifying the joint effect between the ADAS, and through the combined use of a plurality of simulation software, the defects between the simulation software are effectively avoided, so that the data is more real and credible, and meanwhile, the safety problem and the economic cost of a real vehicle experiment are reduced.

A computer-implemented method comprises: receiving sensor data collected during operation of a vehicle, the sensor data including images from at least one camera on the vehicle and other sensor data from at least one other sensor on the vehicle; identifying, based on the sensor data, a corner case for a first neural network model of an advanced driver assistance system (ADAS) of the vehicle; filtering the images to identify a subset of the images depicting the corner case, the filtering performed using a second neural network model; labeling the subset of the images with regard to the corner case, wherein a remainder of the images are not labeled; and retraining the first neural network model using the labeled subset of the images and a subset of the other sensor data corresponding to the subset of the images.

A method comprises: determining that a vehicle is at a first location; controlling motion of the vehicle using an advanced driver assistance system (ADAS); determining a second location for the vehicle based on receiving a first sensor return indicating at least a first static object and a second static object, and based on a first travel distance of the vehicle since the first location; determining a third location for the vehicle based on lane sensing information of a lane monitoring system of the vehicle and a second travel distance of the vehicle since the first location, the lane sensing information reflecting a vehicle curvature; determining a fourth location for the vehicle by fusing the second and third locations with each other; and controlling the motion of the vehicle using the ADAS based on the fourth location.

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.

An advanced driver assistance system is provided. The advanced driver assistance system of the vehicle comprises a communicator configured to communicate with an obstacle detector configured to detect an obstacle; and a processor configured to determine a riding intention of a user in response to reception of a door unlocking instruction, obtain location information of other vehicles based on obstacle information detected by the obstacle detector in response to determining that the riding intention exists, determine a collision possibility with other vehicles based on the location information of other vehicles, and control output of notification information for a collision in response to determining that the collision possibility exists.

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.