114 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.

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.

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.

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 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.

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.

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 method for generating a most probable path for an advanced driver assistance system of a vehicle (10), comprising the steps of: (a) checking whether or not there is a junction to a plurality of road

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.

The present application discloses a method and device for determining road constraints, which are applied to the field of intelligent driving, and particularly relate to sensors in advanced driver assistance systems (ADAS) or autonomous driving systems, such as radars and / or cameras. In this method, the motion state of the target is determined based on the detection information of the target, and at least one road geometry of the road where the target is located is determined based on the detection information of the target; the road constraint of the target is determined based on the at least one road geometry and the motion state of the target, and the road constraint includes at least one of a road direction constraint and a road width constraint. The solution of the present application can improve the accuracy of determining road constraints and further improve the accuracy of target tracking. It further enhances the capabilities of autonomous driving or ADAS, and can be applied to vehicle networks, such as vehicle-to-vehicle (V2X), long-term evolution (LTE‑V) technology for inter-vehicle communication, and vehicle-to-vehicle (V2V).

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.

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.

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.

The invention discloses a collision mitigation control method based on an active and passive security fusion technology, and belongs to the technical field of active and passive security fusion. Comprising the following steps: collision risk identification; judging a collision grade; active collision avoidance control; and performing active and passive fusion control. The invention aims to solve the problem that passengers are severely injured due to insufficient collision energy absorption when the car is in rear-end collision with the truck. An active safety technology and a passive safety technology are combined, kinematics parameters of a front vehicle and a vehicle tail outline are collected through an advanced auxiliary driving system of a vehicle, and collision risks are analyzed in combination with the kinematics parameters and the structure of the vehicle. By integrating sensor data and a hierarchical control strategy, the passenger protection effect under the truck rear-end collision scene is effectively improved, the problem of ignition lag caused by weak collision deceleration signals of a traditional passive safety device is solved, and the passenger injury risk is remarkably reduced.

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