213 results about "Advance driver assistance system" patented technology

A method and system of diagnosing a vehicle with a vehicle diagnostic system that is configured to scan an electrical system of a vehicle for error codes. The vehicle diagnostic system is operatively connected with the electrical system of a vehicle via a diagnostic port of the vehicle, with the vehicle diagnostic system determining whether the vehicle is equipped with Advanced Driver Assistance Systems (ADAS).

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

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.

An electronic device according to an embodiment of the present disclosure includes: a display; a memory configured to store first map data and advanced driver assistance system (ADAS) map content mapped to the first map data in units of links; and a processor functionally connected to the display and the memory, and the processor checks whether content related to a current location among the ADAS map content falls within a predetermined range while guiding a path searched from the first map data, and when the content related to the current location falls within the predetermined range, the processor implements a UI screen, which highlights the content related to the current location, and outputs the implemented screen through the display.

A method and system of diagnosing a vehicle with a vehicle diagnostic system to provide repair recommendations includes connecting a vehicle diagnostic computer tool to a diagnostic port of a vehicle to be in communication with an electronic system of the vehicle and reading vehicle data information of the vehicle with the vehicle diagnostic computer, such as diagnostic trouble codes (DTCs). A diagnostic evaluation program is operable to detect whether particular electrical vehicle systems are present on the vehicle, such as safety systems including Advanced Driver Assistance Systems. The system may additionally receive information regarding damage to the vehicle, such as from a collision. The system includes a repair information database and a report generator program that is operable to generate a repair recommendation report, including for example based on the vehicle data information, the particular electrical vehicle systems determined to be present, and the damage to the vehicle.

The invention provides an open environment automatic driving danger early warning method and system based on the Internet of Things, and the method comprises the steps: a first sensor group disposed on a vehicle continuously monitors the condition of a driving path, and transmits the monitored condition information of the driving path to an intelligent auxiliary driving center; a second sensor group deployed on the vehicle continuously monitors the state of the driver and sends the monitored state information of the driver to the intelligent auxiliary driving center; and the advanced driving assistance system fuses the road condition information and the driver state information to form a danger evaluation coefficient, and performs graded early warning according to the danger evaluation coefficient. By means of the method and the corresponding system, the attention index of the driver can be monitored in real time, the early warning threshold value and strategy are dynamically adjusted according to the fatigue degree and the concentration degree of the driver, and personalized risk prompts are provided; the risk level is subdivided, early warning measures are changed from light to heavy, safety is ensured, excessive interference is avoided, and the driving experience is improved.

Various embodiments include methods and vehicles that may include managing driving policies in a vehicle advanced driver assist system (ADAS) that include obtaining vehicle sensor data, determining a current vehicle context based on the vehicle sensor data, selecting a modified vehicle driving policy from a plurality of saved modified vehicle driving policies based on the determined current vehicle context, and controlling vehicle behavior by the ADAS based upon the selected modified vehicle driving policy. Methods may also include receiving user voice inputs from a vehicle microphone, using a generative AI to infer relevance of the user voice inputs to vehicle driving policies or actions of the ADAS, adjusting a vehicle driving policy of the ADAS based on the inferred relevance of the user voice inputs, and commanding vehicle behavior based upon the adjusted vehicle driving policy.

The present invention describes an Artificial Intelligence (AI) based Advanced Driver Assistance System (ADAS) testing tool. The testing tool comprises a processing unit configured to filter out, using a pre-trained noise cancellation ML model, the received audio signals to isolate noise signals from the received audio signals. Sequentially, the processing unit is configured to, using a using a pre-trained audio classification ML model, identify one or more audio signal from the filtered-out audio signals relating to ADAS audio signals and classify each of the one or more identified signals into a frequency class based on their distinct frequency characteristics. Finally, the processing unit is configured to evaluate each of the classified signal based on ADAS actions associated with the vehicle and generate test results of ADAS testing on each of the classified audio signals, based on the evaluation.

The present application provides a method and device for window occlusion detection, which belongs to the field of sensor technology and can be used for assisted driving and autonomous driving. The method includes: obtaining a first window echo intensity and a second window echo intensity, the first window echo intensity represents the reference window echo intensity, and the second window echo intensity represents the window echo intensity at the current moment; obtaining the detection parameters of the target point at the first moment and the detection parameters at the second moment, the detection parameters including the reflectivity and / or position coordinates of the target point; according to the first window echo intensity, the second window echo intensity, the detection parameters at the first moment and the detection parameters at the second moment, obtaining a window occlusion parameter, the window occlusion parameter is used to indicate the window occlusion state. This method improves the accuracy of lidar window occlusion detection, so as to improve the terminal's advanced driving assistance system (ADAS) capabilities in autonomous driving or assisted driving.

Methods and systems for translating natural language traffic rules into formal logic for autonomous moving vehicles. The present invention relates to a system and method for translating natural language traffic rules into formal logic, including a training method for machine learning systems, and may be used in the context of advanced driving assistance systems or automatic driving systems (ADAS or AD) for vehicles or other autonomous moving vehicles such as robots and drones. A method for preparing a machine learning system for translating natural language traffic rules into formal logic representations for autonomous moving (or autonomous driving) vehicles. The machine learning system includes a (large) pre-trained language model that has been trained on a large amount of text data to assign probabilities to a sequence of words. The machine learning system is prepared by one of the following selectable schemes: i. Prompting using a pre-trained language model in the following manner:-the inputs of the pre-trained language model are natural language instructions and language traffic rules,-presenting multiple inputs and corresponding formal logic target outputs to the machine learning system as (less sample) instances, the machine learning system is enabled to learn and generate formal logic traffic rules when the natural language instructions and the natural language traffic rules are prompted. Ii. Fine tuning the machine learning system by using training data comprising language traffic rules as training inputs and corresponding formal logic traffic rules as target outputs for the pre-trained language model (or accordingly, the machine learning system). These language traffic rules include natural language rules and / or synthetic language traffic rules.

A lane hazard mitigation algorithm includes a sensor for detecting a first hazard in a host road lane and a second hazard in an adjacent road lane, a processor for determining a host lane mitigation including a host lane speed reduction in response to the first hazard and an adjacent lane mitigation including an adjacent lane speed reduction in response to the second hazard, the processor being further configured to generate a lane change control signal in response to the adjacent lane speed reduction being less than the host lane speed reduction and to generate a vehicle speed reduction control signal in response to the host lane speed reduction being less than the adjacent lane speed reduction, and a vehicle controller for controlling a host vehicle in response to the lane change control signal and the vehicle speed reduction control signal.

A vehicle system of a vehicle may include one or more processors that receive first data indicating a driving environment of the vehicle, determine a risk factor indicating a riskiness of a driving situation of the vehicle based on the first data, when the risk factor is between a first threshold value and a second threshold value, output information to a driver of the vehicle, when the risk factor is between the second threshold value and the third threshold value, output a warning to the driver of the vehicle, and when the risk factor is above the third threshold value, cause the vehicle to perform a driving action that reduces the riskiness of the driving situation.

Techniques are disclosed for the implementation of machine learning model training utilities to generate models for advanced driving assistance system (ADAS), driving assistance, and / or automated vehicle (AV) systems. The techniques described herein may be implemented in conjunction with the utilization of open source and cloud-based machine learning training utilities to generate machine learning trained models. One example of such an open source solution includes TensorFlow, which is a free and open-source software library for dataflow and differentiable programming across a range of tasks. TensorFlow may be used in conjunction with many different types of machine learning utilities, such as Amazon's cloud-based SageMaker utility for instance, which is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.

Systems and methods for providing a real-time, augmented reality (mixed reality) simulation of an experience operating a semi-autonomous (e.g., SAE levels 2 to 5) vehicle are disclosed. In an exemplary embodiment, a user is presented with a simulated environment that allows the user to blend the immediate surroundings with interactive visual data representing various automated features ranging from ADAS (Automated Braking, Lane Keeping . . . ) to operating a fully autonomous vehicle. The first person perspective allows the user to get familiar with these vehicles before operating them in the real world. Additionally the interactions of the user with the semi-automated vehicles and automated features are recorded so they can be analyzed for future improvements.

A computer-implemented method is provided that, when executed by the computing hardware of a vehicle, causes the computing hardware to perform operations. The operations include collecting sensor data, calculating one or more rut detection confidence values on the sensor data using one or more rut detection modules, evaluating the one or more rut detection confidence values using a rut detection fusion module, determining whether a rut has been detected, communicating rut data to an advanced driver assistance system, and adjusting the vehicle's trajectory and steering control.

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.

Distance calculations for autonomous vehicle (AV) or advanced driver assistance system (ADAS) navigation may include calculating a distance between two points within an image captured by a vehicle camera. To calculate distance in ADAS and AV systems, the vectors and other values may be represented as floating-point (FP) values. The use of FP values may provide improved performance of these calculations in ADAS, AV systems, and other systems that seek to reduce or minimize computation speed. When the input values are represented as FP values, the distance may be calculated as a square root of a sum of squares of the two FP values. Improved systems and methods are provided for floating-point calculation related to a square root, such as for determining distance calculations.

Aspects of the disclosure relate to automotive grade MCM technology which may include advanced driver assistance system (ADAS) system-on-chip and multiple dynamic random access memories (DRAMs) in one package. In addition, a thermal-mechanical testing vehicle (TMTV) may be used to mimic ADAS chips on ADAS system from thermal, mechanical, or manufacturability perspective. There may be a system which includes multiple MCM components designed into a customized printed circuit board (PCB) with a liquid cooling system.

A smart driving method, device, storage medium, and computer program are disclosed, which can be used for advanced driver assistance systems (ADAS) and autonomous driving. The method includes: acquiring environmental information surrounding the vehicle and the vehicle's current state information; determining a control point based on the environmental and state information; and generating a control trajectory for the current moment based on the control point, historical control trajectories, and state information. This control trajectory represents the path used to guide the vehicle's movement. By concentrating the influence of environmental information on characteristics at the control point and generating the control trajectory at the current moment through the control point, the control requirements of different scenarios are met. Furthermore, it eliminates the need to pre-process environmental information to generate multiple trajectory lines, significantly reducing the computational requirements. By using the priority and corresponding weight information of multiple information sources, switching between different characteristics is achieved, ensuring a smooth transition between multiple characteristics or scenarios and improving the performance of the advanced driver assistance system.

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

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.

The present application provides a range extender control method and an electronic device. The method obtains road condition data of the advanced driver assistance system in the vehicle and navigation data of the navigation system, and then, based on the road condition data and the navigation data, detects long-term congestion events and short-term congestion events in the road ahead of the vehicle, and determines the congestion state of the road ahead. According to the congestion state and the event detection results, the operation of the range extender in the vehicle is controlled to achieve range extension control based on road congestion conditions. This method can combine the advanced driver assistance system and the navigation system to accurately determine road congestion conditions, and combine the congestion state and the event detection results of long-term and short-term events to determine the specific adjustment strategy of the range extender, which can avoid frequent start-stop and high-load operation of the range extender in a congested environment, extend the service life of the range extender, and reduce the noise and emissions of the range extender.

1. The name of the design product: vehicle detector. 2. The use of the design product: used for diagnosing and calibrating the advanced driver assistance system of the vehicle, and four-wheel positioning of the vehicle, etc. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

A beam control method is provided in the invention. The beam control method may be applied to a beam control apparatus configured in a car. The beam control method may include the following steps. The beam control apparatus obtains an image. An advanced driver assistance system (ADAS) of the beam control apparatus may determine whether at least one pedestrian is detected in front of the car according to the image. When the ADAS detects at least one pedestrian in front of the car, a beam control device may obtain pedestrian information of the at least one pedestrian and speed information of the car, and determine whether to enable a polite mode according to the pedestrian information and the speed information. When the beam control device enables the polite mode, the beam control device may control the display pattern of the beam device according to the pedestrian information.

A computer-implemented method for detecting objects within an advanced driver assistance system (ADAS) is provided. The method includes obtaining road scene datasets from a plurality of cameras, including at least road scene images and road scene data annotations, to be provided to an object detection neural network communicating with an open-vocabulary detector of a vehicle, converting, by a text prompter, the road scene data annotations into natural text inputs, converting, by a text embedder, the natural text inputs into embeddings, minimizing objective functions during training to adjust parameters of the object detection neural network, and detecting, by the object detection neural network, objects within the road scene datasets to provide alerts or notifications to a driver of the vehicle pertaining to the detected objects.

Radiofrequency systems for use in advanced driver assistance systems (ADAS) can include a mixture of high frequency (e.g., above 1 GHz) analog and components and lower-frequency digital devices mounted on one main circuit board. Cost savings can be achieved by providing high-frequency signal paths on an interposer circuit board that is mounted to the main circuit board and couples devices requiring high-frequency signalling (e.g., distributing a local oscillator signal to several devices forming part of a cascaded radar system). The interposer is fabricated from special purpose materials suitable for routing high frequency signals with low loss, while the main circuit board can be fabricated from commodity materials suitable for lower frequency applications.

A method includes receiving a desired state from a path planner, the desire state including at least a first desired yaw rate of the vehicle and a first desired lateral velocity of the vehicle. The method further includes determining, by a path tracking controller based at least in part on the first desired yaw rate and the first desired lateral velocity, a second desired yaw rate of the vehicle and a second desired lateral velocity of the vehicle. The method further includes determining, by a vehicle motion controller based at least in part on the second desired yaw rate and the second desired lateral velocity, an active rear steering command and a torque vectoring command. The method further includes controlling the vehicle using at least one of the active rear steering command and the torque vectoring command to cause the vehicle to follow a path generated by the path planner.

A smart ultrasonic integrated-circuit that allows at least one MEMS device to be combined with a piezoelectric transducer in a sensor array module. The sensor array module is capable of additional functionality for an advanced driver assistance system. For example, the MEMS device can add functionality because its wide bandwidth allows for simultaneous detection of audio signals and ultrasonic signals. Accordingly, the sensor array module may provide dual-mode (audio and ultrasonic) sensing in a single module. Additionally, the size / cost of a MEMS device allows for the use of a two-dimensional array for receiving ultrasonic echoes which can add a dimension to the ultrasonic range detection for a vehicle.

The invention relates to a multi-task convolutional neural network architecture applied to an advanced driving assistance system, and the architecture comprises a batch processor which is used for merging data sets of a plurality of perception tasks from the advanced driving assistance system and outputting a merged data batch; the multi-task model is used for performing feature extraction on the merged data batches to extract shared features, fusing the shared features based on an attention mechanism and then outputting multiple tasks; and the task distributor is used for performing task configuration and loss weight adjustment on the multiple tasks. According to the invention, a plurality of sensing tasks can be processed in parallel, and the calculation efficiency and performance can be improved to the greatest extent.