Method and system for vehicle and lane departure warning and lane keep assist features

Abstract

A method of operating a vehicle, determining whether the vehicle is operating in a segment of roadway having low visibility conditions resulting in loss of sensor data input to a vehicle controller operating an assist feature, activating one or more adaptive alerts based on a road departure risk of the vehicle and use of the assist feature by a driver in the upcoming segment of roadway, wherein the road departure risk is determined by calculating a road departure risk index that compares an estimated vehicle path based on vehicle state data to a probabilistic vehicle path for the upcoming segment of roadway and predicting whether the vehicle will operate within an acceptable path in the upcoming segment of roadway, and tracking the vehicle in the upcoming segment of roadway based on vehicle navigation data to provide at least one adaptive alert based on the prediction of the road departure risk.

Description

Technical Field

[0001] This disclosure generally relates to autonomous and semi-autonomous vehicles, and more specifically to methods and systems for adaptive driver notifications that adjust lane departure warning and lane keeping assist features using positioning and risk index formulas in low-visibility operating environments based on driver use and preferences, vehicle status, and adverse weather conditions. Background Technology

[0002] Advanced driver assistance systems (ADAS) capabilities such as lane departure warning, blind spot detection, and emergency braking are common and offered by manufacturers today. Furthermore, more advanced semi-autonomous ADAS (S-ADAS) and autonomous vehicle functions are at different stages of manufacturer rollout and include the ability to sense and navigate the vehicle's environment with little or no user input. Inclement weather presents challenges to ADAS functions that autonomous vehicles and drivers must contend with.

[0003] Vehicle automation has been categorized into numerical levels ranging from Level 0 (corresponding to non-automation with complete human control) to Level 5 (corresponding to full automation without human control). Various automated driver assistance systems (such as cruise control, adaptive cruise control, and parking assistance systems) correspond to lower levels of automation, while truly "driverless" vehicles correspond to higher levels of automation. Despite significant progress in autonomous vehicles in recent years, designers continue to seek improvements, particularly in navigation functions such as trajectory planning.

[0004] Therefore, there is a need to provide systems and methods that can initiate advanced adaptive driver notifications based on the risk of road departure in adverse weather conditions, such as low visibility driving conditions caused by adverse weather, which result in the loss of camera data by the vehicle controller, affecting driver assistance functions such as lane departure warning and lane keeping assist.

[0005] The goal is to enable adaptive driver notifications that activate driver assistance features based on driver preferences, vehicle status, and operating environment conditions when actuating or using autonomous or semi-autonomous modes of vehicle operation. In particular, this is a driver notification method and system that addresses the loss of sensor data input under adverse weather driving conditions.

[0006] Moreover, other desirable features and characteristics of the system and method will become apparent from the following detailed description and appended claims, taking into account the accompanying drawings and the foregoing technical field and background. Summary of the Invention

[0007] A system is disclosed for providing adaptive notifications to the driver when operating a vehicle in low visibility conditions, where the low visibility conditions result in the loss of input of vehicle camera, lidar, or other sensor data that affects the activation of driver road departure warning and lane keeping assist features.

[0008] In at least one exemplary embodiment, a method for operating a vehicle is provided. The method includes receiving vehicle state data and vehicle environment data by a processor; in response to the activation of at least one auxiliary feature of the vehicle, determining by the processor, based on the vehicle state data and vehicle environment data, whether the vehicle is operating in an upcoming road segment with at least low visibility conditions, causing a vehicle controller operating the at least one auxiliary feature to lose at least one sensor data input; in response to the loss of at least one sensor data input, activating one or more adaptive alerts by the processor based on the vehicle's road departure risk and the driver's use of the at least one auxiliary feature in the upcoming road segment, wherein the road departure risk is determined by: calculating a road departure risk index that compares an estimated vehicle path based on the vehicle state data with a probabilistic vehicle path for the upcoming road segment; and predicting whether the vehicle will operate within an acceptable path in the upcoming road segment based on the difference between the vehicle state data and a prediction error level derived by calculating the road departure risk index; and tracking the vehicle in the upcoming road segment by the processor based on vehicle navigation data to provide at least one of one or more adaptive alerts based on the prediction of the road departure risk on the estimated vehicle path in the upcoming road segment.

[0009] In at least one exemplary embodiment, the method includes a processor calculating a road departure risk index in the event of the loss of at least one sensor data input, including image sensor data from a vehicle camera.

[0010] In at least one exemplary embodiment, the method includes having a processor estimate, based on vehicle navigation data, estimated vehicle path, and vehicle state data, the risk of road deviation in an upcoming road segment in the event of loss of image sensor data from a vehicle camera due to weather conditions.

[0011] In at least one exemplary embodiment, the method includes, in response to determining low visibility conditions, issuing an alert by a processor via at least one of one or more adaptive alerts to prevent the driver from making road deviation actions in the upcoming road segment.

[0012] In at least one exemplary embodiment, the method includes, when operating on an upcoming road segment, escalating at least one adaptive alarm among one or more adaptive alarms by a processor based on a calculation of a road deviation risk index.

[0013] In at least one exemplary embodiment, the method includes providing information about the risk of road departure via at least one adaptive alarm system, based on a road departure risk index in the upcoming road segment.

[0014] In at least one exemplary embodiment, the method includes at least one adaptive alarm comprising at least one icon indicating the activation of one or more adaptive alarms based on road departure risk and low visibility vehicle operating modes, wherein the low visibility vehicle operating mode includes the vehicle controller losing image sensor data input.

[0015] In at least one exemplary embodiment, the method includes vehicle navigation data comprising Global Navigation Satellite System (GNSS) data, which includes at least data on road segment curvature.

[0016] In at least one exemplary embodiment, the method includes at least one adaptive alarm configured to incorporate traffic information when displayed and to change at display based on the risk of road deviation associated with at least road surface conditions.

[0017] In at least one exemplary embodiment, the method includes configuring at least one adaptive alarm by a processor according to the driver's preferences; and adjusting at least one adaptive alarm by the processor based on the risks perceived by the driver in the operating environment and the driver's preferences regarding the distance between the vehicle and the road edge in the upcoming road segment.

[0018] In at least one exemplary embodiment, the method includes displaying the location where the vehicle is stopped on a vehicle navigation display by a processor until low visibility conditions improve or the vehicle is no longer operating in a low visibility vehicle operation mode.

[0019] In at least one exemplary embodiment, the method includes, in response to determining low visibility conditions, the processor notifying the lane assist feature that it is inoperable via at least one adaptive alarm and presenting an option to activate the navigation vehicle guidance assist feature.

[0020] In another exemplary embodiment, a vehicle is provided.

[0021] The vehicle includes at least one sensor that provides sensor data within the vehicle environment as vehicle environment data and sensor data about the vehicle state as vehicle state data; a controller having a processor and configured based on the sensor data to: receive the vehicle state data and the vehicle environment data; in response to the activation of at least one auxiliary feature of the vehicle, determine, based on the vehicle state data and the vehicle environment data, whether the vehicle is operating in an upcoming road segment with at least one low visibility condition, causing the vehicle controller operating at least one auxiliary feature to lose at least one sensor data input; in response to the loss of at least one sensor data input, activate one or more adaptive alarms based on the vehicle's road departure risk and the driver's use of at least one auxiliary feature in the upcoming road segment, wherein the road departure risk is determined by: calculating a road departure risk index that compares an estimated vehicle path based on the vehicle state data with a probabilistic vehicle path for the upcoming road segment, and predicting whether the vehicle will operate within an acceptable path in the upcoming road segment based on the difference between the vehicle state data and a prediction error level derived by calculating the road departure risk index; and tracking the vehicle in the upcoming road segment based on vehicle navigation data to provide at least one of one or more adaptive alarms based on the prediction of the road departure risk on the estimated vehicle path in the upcoming road segment.

[0022] In at least one exemplary embodiment, the vehicle includes a controller configured to calculate a road departure risk index in the event of the loss of at least one sensor data input, including image sensor data from a vehicle camera.

[0023] In at least one exemplary embodiment, the vehicle includes a controller configured to estimate the risk of road deviation in an upcoming road segment in the event of loss of image sensor data from the vehicle's cameras due to weather conditions, based on vehicle navigation data, estimated vehicle path, and vehicle state data.

[0024] In at least one exemplary embodiment, the vehicle includes issuing an alert via at least one of one or more adaptive alerts in response to determining low visibility conditions, to prevent the driver from making road deviation actions in the upcoming road segment.

[0025] In at least one exemplary embodiment, the vehicle includes a controller configured to: when operating in an upcoming road segment, upgrade at least one of one or more adaptive alarms based on a calculation of a road departure risk index; and systematically provide information about road departure risk via at least one adaptive alarm based on the road departure risk index in the upcoming road segment.

[0026] In at least one exemplary embodiment, the vehicle includes at least one adaptive alarm comprising at least one icon indicating activation of one or more adaptive alarms based on road departure risk and a low visibility vehicle operating mode, wherein the low visibility vehicle operating mode includes the vehicle controller losing image sensor data input; wherein the vehicle navigation data includes Global Navigation Satellite System (GNSS) data, which includes at least road segment curvature data; wherein the at least one adaptive alarm is configured to incorporate road condition information when displayed and to change at display based on road departure risk associated with at least road surface conditions.

[0027] In at least one exemplary embodiment, the vehicle includes a controller configured to: configure at least one adaptive alarm according to the driver's preferences; and adjust the at least one adaptive alarm based on the risks perceived by the driver in the operating environment and the driver's preference for the distance between the vehicle and the edge of the road in the upcoming road segment.

[0028] In yet another exemplary embodiment, a system is provided. The system includes a processing unit disposed in a vehicle, the processing unit including one or more processors configured via programming instructions encoded on a non-transient computer-readable medium, the processing unit configured to: receive vehicle state data and vehicle environment data; in response to the activation of at least one auxiliary feature of the vehicle, determine, based on the vehicle state data and vehicle environment data, whether the vehicle is operating in an upcoming road segment with at least one low visibility condition, causing a vehicle controller operating at at least one auxiliary feature to lose at least one sensor data input; in response to the loss of at least one sensor data input, activate one or more adaptive alerts based on the vehicle's road departure risk and the driver's use of at least one auxiliary feature in the upcoming road segment, wherein the road departure risk is determined by: calculating a road departure risk index that compares an estimated vehicle path based on the vehicle state data with a probabilistic vehicle path for the upcoming road segment and predicts whether the vehicle will operate within an acceptable path in the upcoming road segment; and tracking the vehicle in the upcoming road segment based on vehicle navigation data to provide at least one of the one or more adaptive alerts based on a prediction of the road departure risk on the estimated vehicle path in the upcoming road segment.

[0029] The present invention includes the following solutions.

[0030] Option 1. A method comprising:

[0031] The processor receives vehicle status data and vehicle environment data.

[0032] In response to the activation of at least one auxiliary feature of the vehicle, the processor determines, based on the vehicle status data and the vehicle environment data, whether the vehicle is operating in an upcoming road segment with at least low visibility conditions, causing the vehicle controller operating the at least one auxiliary feature to lose at least one sensor data input.

[0033] In response to the loss of at least one sensor data input, the processor activates one or more adaptive warnings based on the vehicle's road departure risk and the driver's use of the at least one assistive feature in the upcoming road segment, wherein the road departure risk is determined by:

[0034] A road deviation risk index is calculated, which compares the estimated vehicle path based on the vehicle state data with the probabilistic vehicle path of the upcoming road segment.

[0035] Based on the difference between the vehicle status data and the prediction error level obtained by calculating the road deviation risk index, it is predicted whether the vehicle will operate within an acceptable path in the upcoming road segment; and

[0036] The processor tracks the vehicle in the upcoming road segment based on vehicle navigation data to provide at least one of the one or more adaptive alerts based on a prediction of the road deviation risk on the estimated vehicle path in the upcoming road segment.

[0037] Option 2. The method according to Option 1 further includes:

[0038] The processor calculates the road departure risk index in the event of the loss of at least one sensor data input, including image sensor data from the vehicle's camera.

[0039] Option 3. The method according to Option 2 further includes:

[0040] The processor estimates the risk of road deviation in the upcoming road segment in the event that image sensor data from the vehicle's camera is lost due to weather conditions, based on the vehicle navigation data, the estimated vehicle path, and the vehicle status data.

[0041] Option 4. The method according to Option 3 further includes:

[0042] In response to determining the low visibility conditions, the processor issues an alert via at least one of the one or more adaptive alerts to prevent the driver from deviating from the road in the upcoming road segment.

[0043] Option 5. The method according to Option 4 further includes:

[0044] When operating on the upcoming road segment, the processor upgrades at least one of the one or more adaptive alerts based on the calculation of the road deviation risk index.

[0045] Option 6. The method according to Option 5 further includes:

[0046] The processor systematically provides information about the road departure risk via at least one adaptive alarm, based on a road departure risk index in the upcoming road segment.

[0047] Option 7. According to the method of Option 1, wherein the at least one adaptive alarm includes at least one icon indicating the activation of one or more adaptive alarms based on the road departure risk and low visibility vehicle operation mode, wherein the low visibility vehicle operation mode includes the vehicle controller losing image sensor data input.

[0048] Option 8. The method described in Option 1, wherein...

[0049] The vehicle navigation data includes Global Navigation Satellite System (GNSS) data, which includes at least data on road curvature.

[0050] Option 9. The method according to Option 1, wherein...

[0051] The at least one adaptive alarm is configured to incorporate traffic information when displayed and to change based on the risk of road deviation associated with at least the road surface condition when displayed.

[0052] Option 10. The method according to Option 1 further includes:

[0053] The processor configures the at least one adaptive alarm according to the driver's preferences; and

[0054] The processor adjusts at least one adaptive alarm based on the risks perceived by the driver in the operating environment and the driver's preference for the distance between the vehicle and the road edge in the upcoming road segment.

[0055] Option 11. The method according to Option 1 further includes:

[0056] The processor displays the vehicle's stopping position on the vehicle navigation display until low visibility conditions improve or the vehicle ceases to operate in low visibility vehicle operation mode.

[0057] Option 12. The method according to Option 1 further includes:

[0058] In response to determining the low visibility conditions, the processor notifies the lane keeping assist feature that it is inoperable via the at least one adaptive alarm and presents the option to activate the navigation vehicle guidance assist feature.

[0059] Option 13. A vehicle comprising:

[0060] At least one sensor provides sensor data within the vehicle environment as vehicle environment data and provides sensor data about the vehicle state as vehicle state data;

[0061] The controller, which utilizes a processor and is configured based on the sensor data, is as follows:

[0062] Receive the vehicle status data and the vehicle environment data;

[0063] In response to the activation of at least one auxiliary feature of the vehicle, the vehicle controller operating the at least one auxiliary feature loses at least one sensor data input based on the vehicle status data and the vehicle environment data.

[0064] In response to the loss of at least one sensor data input, one or more adaptive alerts are activated based on the vehicle's road departure risk and the driver's use of at least one assistive feature in the upcoming road segment, wherein the road departure risk is determined by: calculating a road departure risk index that compares an estimated vehicle path based on the vehicle state data with a probabilistic vehicle path for the upcoming road segment, and predicting whether the vehicle will operate within an acceptable path in the upcoming road segment based on the difference between the vehicle state data and the prediction error level derived by calculating the road departure risk index; and

[0065] The vehicle is tracked in the upcoming road segment based on vehicle navigation data to provide at least one of the one or more adaptive alerts based on a prediction of the road deviation risk on the estimated vehicle path in the upcoming road segment.

[0066] Option 14. The vehicle according to Option 13, wherein the controller is configured to:

[0067] Calculate the road departure risk index in the event of the loss of at least one sensor data input, including image sensor data from the vehicle's camera.

[0068] Option 15. The vehicle according to Option 14, wherein the controller is configured to:

[0069] The risk of road deviation in the upcoming road segment is estimated based on the vehicle navigation data, the estimated vehicle path, and the vehicle status data in the event that image sensor data from the vehicle's camera is lost due to weather conditions.

[0070] Option 16. The vehicle according to Option 15 further includes:

[0071] In response to determining the low visibility conditions, an alert is issued via at least one of the one or more adaptive alerts to prevent the driver from deviating from the road in the upcoming road segment.

[0072] Option 17. The vehicle according to Option 16, wherein the controller is configured to:

[0073] When operating on the upcoming road segment, at least one of the one or more adaptive alerts is escalated based on the calculation of the road deviation risk index; and

[0074] Information about the road departure risk is provided via at least one adaptive alert based on the road departure risk index in the upcoming road segment.

[0075] Option 18. The vehicle according to Option 13, comprising:

[0076] The at least one adaptive alarm includes at least one icon indicating the activation of one or more adaptive alarms based on the road departure risk and low visibility vehicle operation mode, wherein the low visibility vehicle operation mode includes the vehicle controller losing image sensor data input;

[0077] The vehicle navigation data mentioned therein includes Global Navigation Satellite System (GNSS) data, which includes at least road curvature data;

[0078] The at least one adaptive alarm is configured to incorporate traffic information when displayed and to change based on the risk of road deviation associated with at least the road surface condition when displayed.

[0079] Option 19. The vehicle according to Option 13, wherein the controller is configured to:

[0080] Configure at least one adaptive alarm based on the driver's preferences; and

[0081] At least one adaptive alarm is adjusted based on the risk perceived by the driver in the operating environment and the driver's preference for the distance between the vehicle and the road edge in the upcoming road segment.

[0082] Option 20. A system comprising:

[0083] A processing unit disposed in a vehicle, the processing unit comprising one or more processors configured via programming instructions encoded on a non-transient computer-readable medium, the processing unit being configured to:

[0084] Receive vehicle status data and vehicle environment data;

[0085] In response to the activation of at least one auxiliary feature of the vehicle, based on the vehicle status data and the vehicle environment data, it is determined whether the vehicle is operating in an upcoming road segment with at least low visibility conditions, causing the vehicle controller operating the at least one auxiliary feature to lose at least one sensor data input.

[0086] In response to the loss of at least one sensor data input, one or more adaptive alerts are activated based on the vehicle's road departure risk and the driver's use of at least one assistive feature in the upcoming road segment, wherein the road departure risk is determined by: calculating a road departure risk index that compares an estimated vehicle path based on the vehicle state data with a probabilistic vehicle path for the upcoming road segment and predicts whether the vehicle will operate within an acceptable path in the upcoming road segment; and

[0087] Based on the vehicle navigation data, the vehicle is tracked in the upcoming road segment to provide at least one of the one or more adaptive alerts based on a prediction of the road deviation risk on the estimated vehicle path in the upcoming road segment. Attached Figure Description

[0088] Exemplary embodiments will now be described in conjunction with the following accompanying drawings, wherein similar numbers denote similar elements, and in the drawings:

[0089] Figure 1 A functional block diagram of an exemplary autonomous vehicle used with an adaptive driver notification system according to various embodiments is shown;

[0090] Figure 2 It is shown that, according to various embodiments, there are Figure 1 A functional block diagram of the (back-end communication) system of one or more autonomous vehicles;

[0091] Figure 3Example diagrams of an autonomous driving system including an adaptive driver notification system for an autonomous vehicle, according to various embodiments, are shown.

[0092] Figure 4 An exemplary external object computing module (EOCM) of an adaptive driving notification system according to various embodiments is shown.

[0093] Figure 5A A flowchart illustrating error prediction for an adaptive driver notification system according to various embodiments is shown;

[0094] Figure 5B A flowchart of a repetitive step in an adaptive driver notification system according to various embodiments is shown, which determines an acceptable safety boundary for the prediction error to ensure that the vehicle is within a defined bounded error.

[0095] Figure 5C Example diagrams are shown of historical data and prediction range for vehicle tracking and probability measurements of vehicle tracking for an adaptive notification system according to various embodiments;

[0096] Figure 6A An exemplary scenario is shown for use cases of low visibility events and risk factors caused by surface friction in an adaptive driver notification system according to an exemplary embodiment;

[0097] Figure 6B An exemplary scenario is shown for use cases of low visibility events and risk factors caused by high road curvature of the adaptive driver notification system according to an exemplary embodiment;

[0098] Figure 6C An exemplary scenario is shown for use cases of low visibility events and risk factors caused by sudden road closures in an adaptive driver notification system according to an exemplary embodiment;

[0099] Figure 7 An exemplary set of charts is shown, depicting a comparison of upcoming road curvature with vehicle path prediction by an adaptive driver notification system according to various embodiments, as well as road deviation risk calculated based on a risk index formula.

[0100] Figure 8 These are example diagrams illustrating the estimated prediction error and driver feedback of an adaptive driver notification system according to various embodiments, and their display on a head-up display; and

[0101] Figure 9This is an exemplary flowchart of a process for an adaptive driver notification system according to various embodiments, the process of calculating a road departure risk index in the absence of camera input, and using navigation data and vehicle dynamics to calculate an estimated vehicle path and a desired path and generate intelligent warnings. Detailed Implementation

[0102] The following detailed description is merely exemplary in nature and is not intended to limit application and use. Furthermore, it is not expected to be construed as being bound by any express or implied theory set forth in the foregoing technical fields, background art, summary of the invention, or the following detailed description. As used herein, the term "module" refers individually or in any combination of hardware, software, firmware, electronic control components, processing logic, and / or processor devices, including but not limited to: application-specific integrated circuits (ASICs), electronic circuits, processors (shared, dedicated, or grouped), and memory executing one or more software or firmware programs, combinational logic circuits, and / or other suitable components providing the aforementioned functionality.

[0103] Embodiments of this disclosure are described herein in terms of functional and / or logical block components and various processing steps. It should be appreciated that such block components can be implemented by any number of hardware, software, and / or firmware components configured to perform specified functions. For example, embodiments of this disclosure may employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, etc., which may perform various functions under the control of one or more microprocessors or other control devices. Furthermore, those skilled in the art will recognize that embodiments of this disclosure can be practiced in conjunction with any number of systems, and the systems described herein are merely exemplary embodiments of this disclosure.

[0104] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, and other functional aspects of the system (as well as the various operational components of the system) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures included herein are intended to represent exemplary functional relationships and / or physical connections between various elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in the embodiments of this disclosure.

[0105] Weather conditions can affect the ability of ADAS to perceive the driving environment. For example, in low-visibility driving scenarios (e.g., heavy fog, blizzards), some sensors (such as cameras and lidar) may not be available to assist the driver, causing some active safety functions to fail to activate. The expectation is to provide intelligent road departure warnings and lane-keeping navigation assistance in adverse weather and low visibility conditions, especially on roads with high curvature, to reduce the risk of veering off the road.

[0106] In exemplary embodiments, this disclosure describes a method for risk assessment under adverse weather conditions, which enables lane departure warnings or lane keeping assist features to alert the driver and provide notification of potential road departures even in low visibility and when camera / LiDAR sensor input is unavailable. A process for initiating escalation based on precise positioning and mapping, driver state, vehicle state, and environmental conditions is also described.

[0107] In exemplary embodiments, this disclosure describes systems, methods, and apparatus for an adaptive driver notification process that addresses the availability of driver assistance features when a vehicle driver may be highly dependent, such as when operating the vehicle under poor visibility conditions, and provides the driver with an escalation alert indicating a loss of driver assistance feature availability. The alert escalation is adjusted based on a risk index formula calculated from the possibility of loss of sensor data input due to poor visibility conditions. The adaptive alert notification process can be configured by systematically predicting vehicle behavior at risk of road departure under adverse weather conditions, which can lead to improved driver ability to ensure safe vehicle operation while providing greater psychological comfort to the driver when operating the vehicle under such conditions.

[0108] In exemplary embodiments, this disclosure describes a system, method, and apparatus for an adaptive driver notification process that incorporates communications returned to the driver, wherein icons displayed on a screen show intelligent adaptive notifications and road departure warnings, the notifications being combined with communications returned to the driver having icons displaying low visibility modes, incorporating vehicle maps (i.e., Google®, Apple® Maps) and GNSS for precise positioning and information about road curvature and slope, incorporating road conditions and adjusting alert notifications based on risk (visibility, weather, surface friction), understanding driver preferences and adjusting severe weather notifications based on perceived risk of environmental and dynamic factors (such as expected distance from the road edge), and indicating the location of the safest stopping point to the driver via a navigation system and OnStar until visibility improves.

[0109] In exemplary embodiments, this disclosure describes a system, method, and apparatus for an adaptive driver notification process that notifies a driver to enable the customer to use navigation assistance options if visibility is low and the vehicle's cameras cannot assist the driver in lane-keeping functionality.

[0110] Figure 1A block diagram depicting an exemplary vehicle 10 is shown, which may include a processor 44 implementing a lane centering system 100. Generally, input data is received in the lane centering system (or simply "system") 100. System 100 determines, based on the received input data, a process for transitioning from manual steering of the vehicle path to automatic manual lane centering operation.

[0111] like Figure 1 As depicted, vehicle 10 generally comprises a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is mounted on the chassis 12 and substantially surrounds the components of vehicle 10. The body 14 and chassis 12 may together form a frame. Wheels 16-18 are each rotatably connected to the chassis 12 near a corresponding corner of the body 14. Vehicle 10 is depicted as a passenger car in the illustrated embodiment, but it should be understood that any other vehicle, including motorcycles, trucks, SUVs, RVs, boats, aircraft, etc., may also be used.

[0112] As shown in the figure, the vehicle 10 generally includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. In this example, the propulsion system 20 may include a motor such as a permanent magnet (PM) motor. The transmission system 22 is configured to transmit power from the propulsion system 20 to the wheels 16 and 18 according to a selectable speed ratio.

[0113] Braking system 26 is configured to provide braking torque to wheels 16 and 18. In various exemplary embodiments, braking system 26 may include friction brakes, brake-by-wire brakes, regenerative braking systems such as electric motors, and / or other suitable braking systems.

[0114] Steering system 24 affects the position of wheels 16 and / or 18. Although described for illustrative purposes as including a steering wheel 25, in some exemplary embodiments contemplated within the scope of this disclosure, steering system 24 may not include a steering wheel.

[0115] The sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the external and / or internal environment of the vehicle 10 and generate sensor data associated therewith.

[0116] The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features, such as, but not limited to, the propulsion system 20, the transmission system 22, the steering system 24, and the braking system 26. In various exemplary embodiments, the vehicle 10 may also include components not in... Figure 1The interior and / or exterior vehicle features shown include various doors, trunk and cabin features such as air, music, lighting, touchscreen display components, etc.

[0117] Data storage device 32 stores data that can be used to control vehicle 10. Data storage device 32 may be part of controller 34, separate from controller 34, or part of controller 34 and a separate system.

[0118] The controller 34 (i.e., the vehicle controller) includes at least one processor 44 (integrated with or connected to system 100) and a computer-readable storage device or medium 46. The processor 44 may be any custom or commercially available processor, central processing unit (CPU), graphics processing unit (GPU), application-specific integrated circuit (ASIC) (e.g., a custom ASIC implementing a neural network), field-programmable gate array (FPGA), an auxiliary processor among several processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), any combination thereof, or any means generally used for executing instructions. For example, the computer-readable storage device or medium 46 may include volatile and non-volatile storage in read-only memory (ROM), random access memory (RAM), and non-volatile memory (KAM). KAM is persistent or non-volatile memory that can be used to store various operational variables when the processor 44 is powered off. The computer-readable storage device or medium 46 may be implemented using any of a number of known storage devices, such as PROM (programmable read-only memory), EPROM (electrical PROM), EEPROM (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combined storage device capable of storing data (some of which represent executable instructions), and is used by the controller 34 to control the vehicle 10.

[0119] The instructions may include one or more separate programs, each including an ordered list of executable instructions for implementing logical functions. When executed by processor 44, the instructions receive and process signals (e.g., sensor data) from sensor system 28, execute logic, calculations, methods, and / or algorithms for automatically controlling components of vehicle 10, and generate control signals based on the logic, calculations, methods, and / or algorithms. These control signals are transmitted to actuator system 30 to automatically control components of vehicle 10. Although in Figure 1 Only one controller 34 is shown, but embodiments of vehicle 10 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication media and cooperate to process sensor signals, execute logic, calculations, methods and / or algorithms, and generate control signals to automatically control the features of vehicle 10.

[0120] For example, system 100 may include any number of additional submodules embedded within controller 34, which may be combined and / or further subdivided to similarly implement the systems and methods described herein. Additionally, inputs to system 100 may be received from sensor system 28, from other control modules (not shown) associated with vehicle 10, and / or by… Figure 1 Other sub-modules (not shown) within the controller 34 determine / model the input. Furthermore, the input may undergo preprocessing, such as subsampling, noise reduction, normalization, feature extraction, and loss reduction.

[0121] Autonomous systems may include Level 4 systems, which represent “high automation” and refer to the driving mode-specific performance of the autonomous driving system in all aspects of a dynamic driving task (even if the human driver does not respond appropriately to an intervention request); and Level 5 systems, which represent “full automation” and refer to the overall performance of the autonomous driving system in all aspects of a dynamic driving task under all road and environmental conditions that a human driver can successfully handle.

[0122] Figure 2 An exemplary embodiment of an operating environment, generally shown at 50, is illustrated, which includes [the following information regarding...]. Figure 1 The one or more autonomous vehicles 10a-10 are associated with an autonomous vehicle-based remote transportation system 52. In various embodiments, the operating environment 50 further includes one or more user devices 54 that communicate with the autonomous vehicle 10 and / or the remote transportation system 52 via a communication network 56.

[0123] Communication network 56 supports communication between devices, systems, and components supported by operating environment 50 (e.g., via physical and / or wireless communication links). For example, communication network 56 may include wireless carrier system 60, such as a cellular telephone system, which includes multiple cell towers (not shown), one or more mobile switching centers (MSCs) (not shown), and any other network components required to connect wireless carrier system 60 to terrestrial communication systems. Each cell tower includes transmit and receive antennas and a base station, with base stations from different cell towers connected to the MSC directly or via intermediate devices such as base station controllers. Wireless carrier system 60 can implement any suitable communication technology, including, for example, digital technologies such as CDMA (e.g., CDMA2000), LTE (e.g., 4G LTE or 5G LTE), GSM / GPRS, or other current or emerging wireless technologies. Other cell tower / base station / MSC arrangements are possible and can be used with wireless carrier system 60. For example, base stations and cell towers can be located at the same site, or they can be far apart from each other. Each base station can be responsible for a single cell tower, or a single base station can serve multiple cell towers, or each base station can be connected to a single MSC. These are just a few possible arrangements.

[0124] In addition to the wireless carrier system 60, a second wireless carrier system in the form of a satellite communication system 64 may be included to provide one-way or two-way communication with autonomous vehicles 10a-100. This can be accomplished using one or more communication satellites (not shown) and uplink transmitting stations (not shown). For example, one-way communication may include a satellite radio service in which program content (news, music, etc.) is received, packaged, uploaded, and then sent to a satellite by a transmitting station, which broadcasts the program to subscribers. For example, two-way communication may include a satellite telephone service using telephone communication between satellite relay vehicle 10 and a station. Satellite telephones may be used in addition to or in place of wireless carrier system 60.

[0125] It may also include a terrestrial communication system 62, which is a conventional terrestrial telecommunications network that connects to one or more landline telephones and connects the wireless carrier system 60 to the remote transportation system 52. For example, the terrestrial communication system 62 may include a public switched telephone network (PSTN), such as a PSTN used to provide hard-wired telephone, packet-switched data communications, and Internet infrastructure. One or more segments of the terrestrial communication system 62 may be implemented using standard wired networks, fiber optic or other optical networks, cable networks, power lines, other wireless networks such as wireless local area networks (WLANs), or networks providing broadband wireless access (BWA), or any combination thereof. Furthermore, the remote (transportation) system 52 does not need to be connected via the terrestrial communication system 62, but may include wireless telephone equipment that allows it to communicate directly with wireless networks such as the wireless carrier system 60.

[0126] although Figure 2Only one user device 54 is shown, but embodiments of the operating environment 50 may support any number of user devices 54, including multiple user devices 54 owned, operated, or otherwise used by a single person. Each user device 54 supported by the operating environment 50 may be implemented using any suitable hardware platform. In this regard, the user device 54 may be implemented in any common form, including but not limited to: desktop computers; mobile computers (e.g., tablet computers, laptop computers, or netbook computers); smartphones; video game devices; digital media players; home entertainment devices; digital cameras or video cameras; wearable computing devices (e.g., smartwatches, smart glasses, smart clothing); and so on. Each user device 54 supported by the operating environment 50 is implemented as a computer-implemented or computer-based device having the hardware, software, firmware, and / or processing logic required to perform the various techniques and methods described herein. For example, the user device 54 includes a microprocessor in the form of a programmable device, which includes one or more instructions stored in an internal memory structure and used to receive binary input to create binary output. In some embodiments, the user device 54 includes a GPS module capable of receiving GPS satellite signals and generating GPS coordinates based on those signals. In other embodiments, as discussed herein, user device 54 includes cellular communication capabilities, enabling the device to perform voice and / or data communications via communication network 56 using one or more cellular communication protocols. In various embodiments, user device 54 includes a visual display, such as a touchscreen graphic display or other display.

[0127] As will be appreciated, the subject matter disclosed herein provides certain enhanced features and functionalities to objects that can be considered as standards, baselines, semi-autonomous or autonomous vehicles 10 and / or autonomous vehicle-based remote transportation systems 52. To this end, autonomous vehicles and autonomous vehicle-based remote transportation systems may be modified, enhanced, or otherwise supplemented to provide additional features as described in more detail below.

[0128] According to various embodiments, the controller 34 is implemented as follows: Figure 3 The autonomous driving system (ADS) 70 shown is described above. That is, suitable software and / or hardware components of the controller 34 (e.g., processor 44 and computer-readable storage device or medium 46) are used to provide the autonomous driving system 70 for use in conjunction with the vehicle 10.

[0129] In various embodiments, the instructions of the autonomous driving system 70 can be organized by function, module, or system. For example, such as Figure 3As shown, the autonomous driving system 70 may include a computer vision system 74, a positioning system 76, a guidance system 78, and a vehicle control system 80. The positioning system 76 may communicate with an adaptive alert notification system 82, which implements various algorithms to provide multiple adaptive driver notifications regarding the availability of driver assistance features, including predictions of lost vehicle sensor data input due to driving conditions. For example, driving conditions may include weather-related conditions causing low visibility, which could lead to a loss of camera data input, resulting in driver assistance features such as lane centering or lane / road departure warnings for the vehicle's autonomous driving system 70 (or semi-autonomous driving system) potentially being unavailable (i.e., temporarily disabled in the near future, such as for an upcoming road segment or lane segment). As will be appreciated, in various embodiments, instructions may be organized into any number of systems (e.g., combined, further subdivided, etc.), as this disclosure is not limited to this example.

[0130] In various embodiments, the computer vision system 74 synthesizes and processes sensor data and predicts the presence, location, classification, and / or path of objects, as well as environmental characteristics of the vehicle 10. In various embodiments, the computer vision system 74 may combine information from multiple sensors, including but not limited to cameras, lidar, radar, and / or any number of other types of sensors (including in the event of loss of input from one sensor, such as a camera sensor).

[0131] The positioning system 76 processes sensor data along with other data to determine the position of vehicle 10 relative to its environment (e.g., local location relative to a map, precise location relative to a road lane, vehicle heading, speed, etc.). The guidance system 78 processes sensor data along with other data to determine the path that vehicle 10 should follow. The vehicle control system 80 generates control signals for controlling vehicle 10 based on the determined path.

[0132] In various embodiments, the controller 34 implements machine learning techniques to assist the functions of the controller 34, such as feature detection / classification, obstacle mitigation, route traversal, mapping, sensor integration, ground condition determination, trajectory prediction and estimation, and route in upcoming road segments.

[0133] The autonomous driving system 70 is configured to perform steering and speed control maneuvers, as well as other possible autonomous driving possibilities, to avoid collisions and, in part, to coordinate movement with the tracked object based on control commands. See below for more information. Figure 4 The autonomous driving system 70 operates known autonomous vehicle control computer instructions based on control data through its processor.

[0134] Figure 4Exemplary External Object Computing Modules (EOCMs) according to various embodiments are shown, which utilize an adaptive driver notification system to initiate vehicle control actions. Figure 4 In this system, system 70 further includes an external object computing module (or "EOCM") 400. EOCM 400 is a computer system or controller that may generally include components (not shown) such as a processor, computer-readable storage, and interfaces.

[0135] In an embodiment, such as Figure 4 As shown, the EOCM 400 includes modules and interfaces, a receiver and input process 445 via a high-definition map and positioning module (HDLM), a waypoint generation module 450, a road curvature and receiver and input process 455, a driver road departure intention detection module 460, and a road departure risk calculation module 465.

[0136] In this embodiment, the EOCM 400 receives status and environmental vehicle data from various vehicle sensors, including an inertial measurement unit (IMU), a steering angle sensor (SAS), and a wheel speed sensor (WSS), a global positioning system (GPS) 430, and navigation data via Ethernet through an infotainment system 435. Furthermore, the EOCM 400 transmits signal data to the driver notification module 470 based on a formula for calculating a lane (road) departure risk index in the absence of (at least) camera input, and sends signal data to the driver notification module 470 to intelligently warn the driver to avoid road deviation in poor visibility conditions. In this embodiment, the EOCM 400 includes other features that trigger activation 410 to determine various driver characteristic states via software and algorithms, and predict risks such as road departure based on sensing data from adverse weather condition detection 415.

[0137] In this embodiment, the receiver and input process 455 updates the high-fidelity map. For example, data from the sensor suite can be used to update the high-fidelity map, where information is used to develop a layer with waypoints to identify selected events, the location of camera data loss events, and the frequency of camera data loss events encountered at the identified locations. In this way, sensor suite data from the autonomous vehicle can continuously provide feedback to the mapping system, and the high-fidelity map (of the receiver and input process 445) can be updated as more and more information is collected.

[0138] In one embodiment, the vehicle path prediction module 440 implements an intelligent algorithm for trajectory or path prediction modeling to predict the actual path of the operating vehicle based on sensing data from the sensor suite 425, including data related to vehicle lateral forces, wheel angles, wheel speeds, and other vehicle dynamics.

[0139] In an embodiment, the waypoint generation module 450 generates one or more target waypoints to predict the vehicle trajectory, path, or route from the current location of the autonomous or semi-autonomous vehicle to the target waypoint or selected area. In some instances, the path may include several target waypoints and / or target areas through which the operating autonomous or semi-autonomous vehicle may lose input from cameras or other vehicle sensors.

[0140] In this embodiment, the predicted or estimated vehicle path is based on information available from sensor suite 325 (such as...). Figure 3 The sensor data obtained (as shown) and the data provided by GPS 430 for current vehicle status data, as well as the location of road curvature and slope received using GNSS.

[0141] In this embodiment, the driver's road deviation intention detection module 460 determines the detection probability rather than the vehicle path prediction.

[0142] In this embodiment, the road deviation risk calculation module 465 calculates the probability based on a formula for road deviation risk at a waypoint, location, or road curvature.

[0143] Figure 5A A flowchart illustrating error prediction for an adaptive driver notification system according to various embodiments is shown. In Figure 5, at block 505 (i.e., the error prediction processor), the driver input from block 515 (i.e., the probabilistic vehicle path processor) is processed based on the input from block 510. Probability vehicle route prediction i=1..n, and the expected path curvature estimate from box 520 (i.e., the path curvature estimator processor) based on GPS data from box 525 and NAV information from box 530. The input is used to generate the prediction error. Other states of the estimated expected path (heading, curvature, etc.) can also be derived using a similar method as described above for estimating road curvature.

[0144] Figure 5B A flowchart illustrating a repetitive step in an adaptive driver notification system according to various embodiments is shown, which determines an acceptable safety boundary for the prediction error to ensure the vehicle remains within a defined bounded error. Figure 5B In the diagram, the variable filter operation at box 535 and the update algorithm at box 540 define bounded errors. Due to the uncertainty of data under adverse weather conditions, error cannot be guaranteed. Therefore, the acceptable error safety boundary is determined through variable filtering operations and algorithm updates to ensure that the vehicle operates within bounded errors or to ensure... .

[0145] Error prediction algorithm is limited to This is used to calculate the road deviation risk index based on the following: ,as well as

[0146] (Uncertainty measure)

[0147] And the following (vehicle dynamic parameters) factors:

[0148]

[0149]

[0150]

[0151] In an exemplary embodiment, the virtual compensator is configured as follows: In this case, then If the virtual control parameter u is approximately zero, the driver is tracking the road contour, and when |u| >> 0, this may mean or indicate that the driver is not tracking the road contour, thus creating the possibility or risk of road deviation.

[0152] In one embodiment, the parameters of the error prediction algorithm Updated and limited to the following: K c Design parameters - controller gain; E: prediction error; A, B1, B2, B3: vehicle lateral dynamic parameters are as follows: u: vehicle wheel angle, θ i Road slope, ρ D Road curvature : Expected path data from the navigation system, and Probabilistic vehicle route data.

[0153] Figure 5C Example diagrams are shown illustrating historical data and predicted range for vehicle tracking in an adaptive notification system according to various embodiments, as well as probabilistic measurements of vehicle tracking (as opposed to deterministic measurements). The estimated desired path curvature is based on an update algorithm. The path is 560, and the probabilistic vehicle path prediction is path 570, which has a specific feature for probabilistic path prediction. The region i = 1..n is 555. The uncertainty in the calculation is the difference between the two paths, which is achieved through... compensate.

[0154] Figure 6AExemplary scenarios are depicted for use cases of low visibility events and risk factors caused by surface friction in an adaptive driver notification system according to an exemplary embodiment. Figure 6A In the illustrated use case, in scenario 605, the road edges and road markings are obscured by severe weather such as snow, fog, and heavy rain, resulting in a probabilistic path prediction 600 based on the risk factors of the obscured edges and markings in the risk index formula.

[0155] Figure 6B Exemplary scenarios are depicted for use cases of low visibility events and risk factors caused by high road curvature in an adaptive driver notification system according to an exemplary embodiment. Figure 6B In the usage scenario shown, scenario 610 is caused by the high curvature of the road (the steep slope of the road may also be a factor), which makes the road edge undetectable, resulting in a probabilistic path prediction 615 based on the risk factor of the inability to detect the road edge in the risk index formula.

[0156] Figure 6C Exemplary scenarios are depicted for use cases of low visibility events and risk factors caused by sudden road closures in an adaptive driver notification system according to exemplary embodiments. Figure 6C In the illustrated use case, scenario 630, the T-shaped intersection causes the road edge to be undetectable, resulting in probability path prediction 625 based on the risk factor of the undetectable road edge in the risk index formula.

[0157] In this embodiment, road trajectories and probabilistic path predictions can be displayed in a head-up display (HUD), and voice-over narration can also be implemented. These two features can also be user-touch selectable for actuation using various in-vehicle actuation buttons for displaying the HUD, voice notifications, and other visual and auditory notifications utilizing an adaptive driver notification system.

[0158] Figure 7 Exemplary graph sets of adaptive driver notification systems according to various embodiments are shown, depicting a comparison of upcoming road curvature with vehicle path prediction, and road deviation risk calculated based on a risk index formula. Figure 7In the diagram, vehicle 700 is shown operating in an upcoming road curvature, and the expected vehicle path 710, based on an estimate of the upcoming curvature using NAV information, is compared to the predicted vehicle path curvature 720 based on a risk index formula. Figure 760 depicts a comparison of the estimated upcoming road curvature 715 based on GPS data with the predicted vehicle path curvature 725 based on a risk index formula. Figure 760 depicts the road departure risk 730, calculated by comparing the vehicle state and dynamics with the road curvature estimation and vehicle path prediction depicted in Figure 750. Based on data on vehicle state and dynamics received by the adaptive driver notification system and calculations based on the risk index formula and environmental factors, the road departure risk in Figure 760 is shown at 735 as being at its maximum approximately 130 seconds from the current vehicle position. A driver-available map display 770 depicts the predicted path and estimated road path curvature, along with audible or visual notifications 780 that are tied to and escalated according to the road departure risk amount in Figure 760.

[0159] Figure 8 These are example diagrams illustrating the estimated prediction error and driver feedback of an adaptive driver notification system according to various embodiments, and their display on a head-up display. Figure 8 The diagram shows prediction error 810, estimated vehicle path 815 based on vehicle state, expected path 820 from NAV feedback on upcoming curvature, driver feedback 830 by notifying the use of lane keeping assist features and curve warnings on the map, and corresponding HUD 840 for the two paths seen by the driver.

[0160] Figure 9 This is an exemplary flowchart of a process for an adaptive driver notification system according to various embodiments, the process of calculating a road departure risk index in the absence of camera input, and calculating an estimated vehicle path and a desired path using navigation data and vehicle dynamics, and generating an intelligent warning. As will be appreciated from this disclosure, the sequence of operations within the method is not limited to... Figure 9 The method may be executed in one or more different orders as shown, depending on the circumstances and in accordance with this disclosure. In various embodiments, the method may be scheduled to run based on one or more predetermined events (such as loss of sensor data input), and / or may run continuously during operation of vehicle 10.

[0161] The method may begin at 905. At step 910, the driver makes a request to enable an autonomous mode for autonomous vehicle operation. At the time of the driver's request, in step 910, the vehicle may be entering a low-visibility type of adverse weather condition with road curvature and / or experiencing scenarios that cause the driver to potentially rely on driver assistance features such as lane departure warnings or lane keeping assist functions based on driver operating preferences, driver state, vehicle state, and current environmental conditions. In embodiments, the driver may operate the vehicle in autonomous or semi-autonomous driving mode, or may wish to activate this mode (e.g., before entering a curved road segment).

[0162] At step 910, after the driver activates the driver assistance lane keeping or lane departure warning feature, the vehicle sensor suite provides vehicle status data and vehicle environment data to the vehicle controller. The vehicle controller is configured to determine, based on the vehicle status data and vehicle environment data, whether the vehicle is operating on a road segment with impending severe weather conditions that would cause low visibility. This could cause the vehicle controller operating at least one assistance feature (such as the lane keeping assist feature or the lane departure warning feature) to lose at least one sensor data input.

[0163] At step 915, in response to the vehicle controller losing at least one sensor data input, the system is activated and may consist of a set of adaptive alerts based on the vehicle's road departure risk and the driver's use of at least one assistive feature in the upcoming road segment. The road departure risk is determined by the adaptive driver notification system (hereinafter referred to as the "System") by: calculating a road departure risk index that compares an estimated vehicle path based on vehicle state data with a probabilistic vehicle path for the upcoming road segment, and predicting whether the vehicle will operate within an acceptable path in the upcoming road segment based on the difference between the vehicle state data and the prediction error level derived by calculating the road departure risk index. At step 920, the vehicle controller tracks the vehicle in the upcoming road segment based on vehicle navigation data so that the System can provide at least one adaptive alert based on the prediction of the road departure risk on the estimated vehicle path in the upcoming road segment.

[0164] At step 925, the vehicle controller is configured to calculate a road departure risk index via the system in the event of loss of at least one sensor data input (such as image sensor data from the vehicle's camera). At step 930, the vehicle controller estimates, via the system, the road departure risk in the upcoming road segment in the event of loss of image sensor data from the vehicle's camera due to weather conditions, based on vehicle navigation data, estimated vehicle path, and vehicle state data.

[0165] At step 935, in response to determining low visibility conditions, the vehicle controller causes the system to provide at least one adaptive warning to prevent the driver from making any road deviation actions in the upcoming road segment.

[0166] At step 940, when operating in an upcoming road segment, the system upgrades at least one of one or more adaptive alerts based on the calculation of the road deviation risk index.

[0167] At step 945, the system systematically provides information about road / lane departure risk via at least one adaptive alert based on a road departure risk index for the upcoming road segment. The at least one adaptive alert may be configured to include at least one icon indicating the activation of one or more adaptive alerts based on road / lane departure risk and a low-visibility vehicle operating mode, wherein the low-visibility vehicle operating mode includes the vehicle controller losing image sensor data input. Vehicle navigation data includes Global Navigation Satellite System (GNSS) data, which includes at least data on road segment curvature. The at least one adaptive alert is configured to incorporate road condition information and change based on the road departure risk associated with at least the road surface condition when displayed.

[0168] At step 950, the system is configured to activate at least one adaptive alarm based on the driver's preference; and to adjust at least one adaptive alarm based on the risk perceived by the driver in the operating environment and the driver's preference for the distance between the vehicle and the road edge in the upcoming road segment.

[0169] At step 955, the system is configured to display the vehicle's stopping location on the vehicle navigation display until low visibility conditions improve or the vehicle is no longer operating in low visibility vehicle operation mode.

[0170] At step 960, in response to determining low visibility conditions, the system is configured to notify lane assist features that are inoperable via at least one adaptive alarm and present an option to activate navigation vehicle guidance assist features to compensate for the loss of lane keeping assist or road departure warning functions.

[0171] At step 965, once the low visibility conditions have passed or the weather has improved so that the vehicle controller no longer loses camera input, the system is configured to initiate an action to automatically disengage and can notify the driver of the changed status of the vehicle assistance functions and external conditions that no longer cause the low visibility vehicle operation mode.

[0172] The foregoing detailed description is merely exemplary in nature and is not intended to limit the embodiments of the subject matter or the application and use of such embodiments. As used herein, the word "exemplary" means "serving as an example, instance, or illustration." Any implementation described herein as exemplary is not necessarily to be construed as superior or better than other implementations. Moreover, no constraint is expected from any express or implied theory arising from the foregoing technical field, background art, or specific implementation.

[0173] While at least one exemplary aspect has been presented in the foregoing detailed description of the invention, it should be recognized that numerous variations exist. It should also be understood that the one or more exemplary aspects are merely examples and are not intended to limit the scope, applicability, or construction of the invention in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient roadmap for implementing the exemplary aspects of the invention. It should be understood that various changes can be made to the function and arrangement of the elements described in the exemplary aspects without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for adjusting lane departure warning and lane keeping assist features, comprising: The processor receives vehicle status data and vehicle environment data. In response to the activation of at least one auxiliary feature of the vehicle, the processor determines, based on the vehicle status data and the vehicle environment data, whether the vehicle is operating in an upcoming road segment with at least low visibility conditions, causing the vehicle controller operating the at least one auxiliary feature to lose at least one sensor data input. In response to the loss of at least one sensor data input, the processor activates one or more adaptive warnings based on the vehicle's road departure risk and the driver's use of the at least one assistive feature in the upcoming road segment, wherein the road departure risk is determined by: A road deviation risk index is calculated, which compares the estimated vehicle path based on the vehicle state data with the probabilistic vehicle path of the upcoming road segment. Based on the difference between the vehicle status data and the prediction error level obtained by calculating the road deviation risk index, it is predicted whether the vehicle will operate within an acceptable path in the upcoming road segment. as well as The processor tracks the vehicle in the upcoming road segment based on vehicle navigation data to provide at least one of the one or more adaptive alerts based on a prediction of the road deviation risk on the vehicle's path in the upcoming road segment.

2. The method according to claim 1, further comprising: The processor calculates the road departure risk index in the event of the loss of at least one sensor data input, including image sensor data from the vehicle's camera.

3. The method according to claim 2, further comprising: The processor estimates the risk of road deviation in the upcoming road segment in the event that image sensor data from the vehicle's camera is lost due to weather conditions, based on the vehicle navigation data, the estimated vehicle path, and the vehicle status data.

4. The method of claim 3, further comprising: In response to determining the low visibility conditions, the processor issues an alert via at least one of the one or more adaptive alerts to prevent the driver from deviating from the road in the upcoming road segment.

5. The method of claim 4, further comprising: When operating on the upcoming road segment, the processor upgrades at least one of the one or more adaptive alerts based on the calculation of the road deviation risk index.

6. The method of claim 5, further comprising: The processor systematically provides information about the road departure risk via at least one adaptive alarm, based on a road departure risk index in the upcoming road segment.

7. The method of claim 1, wherein the at least one adaptive alarm comprises at least one icon indicating the activation of one or more adaptive alarms based on the road departure risk and low visibility vehicle operating mode, wherein the low visibility vehicle operating mode includes the vehicle controller losing image sensor data input.

8. The method according to claim 1, wherein The vehicle navigation data includes Global Navigation Satellite System (GNSS) data, which includes at least data on road curvature.

9. The method according to claim 1, wherein The at least one adaptive alarm is configured to incorporate traffic information when displayed and to change based on the risk of road deviation associated with at least the road surface condition when displayed.

10. The method of claim 1, further comprising: The processor configures the at least one adaptive alarm according to the driver's preferences; as well as The processor adjusts at least one adaptive alarm based on the risks perceived by the driver in the operating environment and the driver's preference for the distance between the vehicle and the road edge in the upcoming road segment.

11. The method of claim 1, further comprising: The processor displays the vehicle's stopping position on the vehicle navigation display until low visibility conditions improve or the vehicle ceases to operate in low visibility vehicle operation mode.

12. The method of claim 1, further comprising: In response to determining the low visibility conditions, the processor notifies the lane keeping assist feature that it is inoperable via the at least one adaptive alarm and presents the option to activate the navigation vehicle guidance assist feature.

13. A vehicle comprising: At least one sensor provides sensor data within the vehicle environment as vehicle environment data and provides sensor data about the vehicle state as vehicle state data; The controller, which utilizes a processor and is configured based on the sensor data, is as follows: Receive the vehicle status data and the vehicle environment data; In response to the activation of at least one auxiliary feature of the vehicle, the vehicle controller operating the at least one auxiliary feature loses at least one sensor data input based on the vehicle status data and the vehicle environment data. In response to the loss of at least one sensor data input, one or more adaptive alerts are activated based on the vehicle's road departure risk and the driver's use of at least one auxiliary feature in the upcoming road segment, wherein the road departure risk is determined by: calculating a road departure risk index that compares an estimated vehicle path based on the vehicle state data with a probabilistic vehicle path in the upcoming road segment, and predicting whether the vehicle will operate within an acceptable path in the upcoming road segment based on the difference between the vehicle state data and a prediction error level derived by calculating the road departure risk index; as well as The vehicle is tracked in the upcoming road segment based on vehicle navigation data to provide at least one of the one or more adaptive alerts based on a prediction of the road deviation risk on the vehicle's path in the upcoming road segment.

14. The vehicle of claim 13, wherein the controller is configured to: Calculate the road departure risk index in the event of the loss of at least one sensor data input, including image sensor data from the vehicle's camera.

15. The vehicle of claim 14, wherein the controller is configured to: Based on the vehicle navigation data, the estimated vehicle path, and the vehicle status data, the risk of road deviation in the upcoming road segment is estimated in the event that image sensor data from the vehicle's camera is lost due to weather conditions.

16. The vehicle according to claim 15, further comprising: In response to determining the low visibility conditions, an alert is issued via at least one of the one or more adaptive alerts to prevent the driver from deviating from the road in the upcoming road segment.

17. The vehicle of claim 16, wherein the controller is configured to: When operating on the upcoming road segment, at least one of the one or more adaptive alerts is escalated based on the calculation of the road deviation risk index; and Information about the road departure risk is provided via at least one adaptive alert based on the road departure risk index in the upcoming road segment.

18. The vehicle according to claim 13, comprising: The at least one adaptive alarm includes at least one icon indicating the activation of one or more adaptive alarms based on the road departure risk and low visibility vehicle operation mode, wherein the low visibility vehicle operation mode includes the vehicle controller losing image sensor data input; The vehicle navigation data mentioned therein includes Global Navigation Satellite System (GNSS) data, which includes at least road curvature data; The at least one adaptive alarm is configured to incorporate traffic information when displayed and to change based on the risk of road deviation associated with at least the road surface condition when displayed.

19. The vehicle according to claim 13, wherein, The controller is configured to: Configure at least one adaptive alarm based on the driver's preferences; as well as At least one adaptive alarm is adjusted based on the risk perceived by the driver in the operating environment and the driver's preference for the distance between the vehicle and the road edge in the upcoming road segment.

20. A system for adjusting lane departure warning and lane keeping assist features, comprising: A processing unit disposed in a vehicle, the processing unit including one or more processors, the processors being configured via programming instructions encoded on a non-transient computer-readable medium, the processing unit being configured to: Receive vehicle status data and vehicle environment data; In response to the activation of at least one auxiliary feature of the vehicle, based on the vehicle status data and the vehicle environment data, it is determined whether the vehicle is operating in an upcoming road segment with at least low visibility conditions, causing the vehicle controller operating the at least one auxiliary feature to lose at least one sensor data input. In response to the loss of at least one sensor data input, one or more adaptive alerts are activated based on the vehicle’s road departure risk and the driver’s use of at least one assistive feature in the upcoming road segment, wherein the road departure risk is determined by: calculating the road departure risk index, which compares an estimated vehicle path based on the vehicle state data with a probabilistic vehicle path in the upcoming road segment, and predicts whether the vehicle will operate within an acceptable path in the upcoming road segment; as well as Based on the vehicle navigation data, the vehicle is tracked in the upcoming road segment to provide at least one of the one or more adaptive alerts based on a prediction of the road deviation risk on the estimated vehicle path in the upcoming road segment.

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