System and method for improving driver warnings during automated driving

Abstract

The present application relates to systems and methods for improving driver warnings during autonomous driving. The systems, methods, and other embodiments described herein relate to improving driver warnings for autonomous driving of a vehicle. In one embodiment, a method includes monitoring a vehicle control system during autonomous driving to obtain driver feedback. The method also includes adapting a score value at a defined rate based on whether driver feedback is present. The method also includes generating a warning to the driver in response to determining that the score value meets a threshold before driver feedback is present.

Description

Technical Field

[0001] The subject matter described herein relates generally to improving automated vehicle operation and, more particularly, to adapting driver warnings to improve automated driving. Background Art

[0002] A vehicle may be equipped with an autonomous driving module for autonomous driving as part of an automated driving system (ADS). The vehicle's ADS may adapt to driving behavior, driving complexity, or driver feedback. ADS adaptation may improve operation in certain ADS modes that use driver feedback or interaction. Among various methods, the vehicle's ADS may adapt the driver warning system based on driving behavior or driving complexity by testing in dedicated driving facilities, virtual training systems, testing using dedicated vehicle equipment, etc. ADS adaptation performed by these methods may be time-consuming, inconvenient, or ineffective for changing driving behavior, driving complexity, or driver feedback.

[0003] Some automated driving modes can also request driver feedback within a certain time period before alerting the driver. Vehicle systems can request steering wheel feedback, brake feedback, and other information at fixed intervals. For example, a lane keeping assist (LKA) system can use steering wheel feedback to maintain safe operation. In one approach, the LKA system can reset a timer when the driver provides desired steering wheel feedback. If the timer expires before receiving driver feedback, the LKA system can generate a warning or notification in another manner.

[0004] Furthermore, if the ADS is performing simple vehicle maneuvers, frequent driver feedback may be unnecessary. In this approach, the vehicle system can generate warnings or prompts at the expiration of fixed time intervals, regardless of the complexity of the automated driving maneuver or driving conditions. For example, due to the fixed warning interval, the ADS may sometimes generate unnecessary warnings when maneuvering the vehicle in a straight line on an open, straight, or flat road. Therefore, current systems may be ineffective in effectively generating driver warnings during automated driving. Summary of the Invention

[0005] In one embodiment, example systems and methods relate to a method for improving driver warnings for automated driving systems (ADS) during driving. Certain ADS modes may request driver feedback for safe and effective automated driving. If driver feedback is not received within a fixed period of automated driving, a vehicle operating in ADS mode may generate a warning indicating insufficient driver interaction. However, driving behavior and driving conditions may vary, hindering the functionality of these systems. In various implementations, current ADS solutions that adapt driver warnings to driving behavior and driving conditions may be time-consuming, inconvenient, or ineffective. Therefore, an improved method for automated driving is disclosed, in which a warning system uses a score value that adapts a time period before warning the driver. The warning system can adapt the score value required before warning the driver based on different feedback types, driving conditions, and other factors. A vehicle system that detects driver feedback can cause the score value to be adapted. In one method, the warning system can also vary the rate at which the score value is adapted based on the complexity of the automated driving state, driver feedback, driver awareness, or driver interaction. In this way, the vehicle improves automated driving by adapting driver warnings to the time interval based on driving behavior, driving complexity, or driving conditions.

[0006] In one embodiment, a warning system for improving driver warnings for autonomous driving of a vehicle is disclosed. The warning system includes one or more processors and a memory communicatively coupled to the one or more processors. The memory stores a monitoring module including instructions that, when executed by the one or more processors, cause the one or more processors to monitor a vehicle control system for driver feedback during autonomous driving. The memory also stores an adaptation module including instructions that, when executed by the one or more processors, cause the one or more processors to adapt a score value at a defined rate based on whether driver feedback is present. The adaptation module also includes instructions for generating a warning to the driver in response to determining that the score value meets a threshold before driver feedback is present.

[0007] In one embodiment, a non-transitory computer-readable medium for improving driver warnings for autonomous driving of a vehicle is disclosed, and includes instructions that, when executed by one or more processors, cause the one or more processors to perform one or more functions. The instructions include instructions for monitoring a vehicle control system for driver feedback during autonomous driving. The instructions also include instructions for adapting a score value at a defined rate based on whether driver feedback is present. The instructions also include instructions for generating a warning to the driver in response to determining that the score value meets a threshold before driver feedback is present.

[0008] In one embodiment, a method for improving driver warnings for automated vehicle driving is disclosed. In one embodiment, the method includes monitoring a vehicle control system during automated driving to obtain driver feedback. The method also includes adapting a score value at a defined rate based on whether driver feedback is present. The method also includes generating a warning to the driver in response to determining that the score value satisfies a threshold before driver feedback is present. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings that are incorporated into the specification and form a part of the specification illustrate various systems, methods and other embodiments of the present disclosure. It should be understood that the element boundaries (e.g., boxes, box groups or other shapes) shown in the figures represent an embodiment of boundaries. In some embodiments, an element can be designed as multiple elements, or multiple elements can be designed as one element. In some embodiments, an element that is shown as an internal component of another element can be implemented as an external component, and vice versa. In addition, elements may not be drawn to scale.

[0010] Figure 1 One embodiment of a vehicle is shown in which the systems and methods disclosed herein may be implemented.

[0011] Figure 2 Shown in Figure 1 An embodiment of a warning system that uses an adaptation score system to monitor driver feedback and generate driver warnings during automated driving of a vehicle.

[0012] Figure 3 One embodiment of a method for monitoring driver feedback during automated driving using an adapted score system in association with a warning system is shown.

[0013] Figure 4 1 is a diagram illustrating adaptation of warnings to a driving environment for autonomous driving on a road with other vehicles.

[0014] Figure 5 is a diagram illustrating use of a scoring system during autonomous driving to generate warnings based on driving complexity for driver takeover or autonomous driving mode.

[0015] Figure 6 is a diagram illustrating the use of an adaptive score system to generate warnings according to varying driving complexity of an autonomous driving mode.

[0016] Figure 7 is a diagram illustrating the use of an adaptive score system where the defined rate of change varies to generate warnings during autonomous driving.

[0017] Figure 8 is a diagram illustrating the use of an adaptive score system in which different types of driver warnings are provided after a threshold is met during autonomous driving.

[0018] Figure 9 is a diagram illustrating the use of an adaptive score system where the defined rate varies according to the driver state during autonomous driving. DETAILED DESCRIPTION

[0019] Disclosed herein are systems, methods, and other embodiments associated with improving driver warnings for autonomous driving. In one embodiment, a warning system can use a score value that adapts how driver warnings are triggered based on driver feedback, driving complexity, sensor data, and the like during certain autonomous driving system (ADS) modes. These ADS modes can use driver interaction, engagement, or attention associated with the autonomous driving functionality. By reducing the score value over time while the driver is in control of the vehicle, warnings are triggered less frequently. In one approach, the warning system can adapt how warnings are triggered by increasing, decreasing, or maintaining a defined rate of change of the score value during autonomous driving. For example, the warning system can increase or decrease a defined rate of change of the score value based on location or dangerous maneuvers to adapt the time period until a driver warning is triggered. Thus, the adapted score value changes when and how a threshold is reached, which more effectively warns the driver during autonomous driving.

[0020] Furthermore, the warning system can alert the driver to ensure that driver awareness, interaction, and engagement are sufficient for different autonomous driving conditions. For example, the type of warning can vary based on the score that meets various thresholds. In one approach, as the score exceeds different thresholds over time during autonomous driving, an audible warning can precede a more severe haptic warning to the driver. In this way, the vehicle improves autonomous driving by adapting driver warnings to varying intervals based on driving behavior, driving complexity, or driving state.

[0021] refer to Figure 1, shows an example of a vehicle 100. As used herein, a "vehicle" is any form of motor vehicle. In one or more implementations, vehicle 100 is an automobile. Although the arrangement herein will be described with respect to an automobile, it will be understood that the embodiments are not limited to automobiles. In some implementations, vehicle 100 can be in the form of any robotic device or motor vehicle, for example, including sensors to sense various aspects of operation in ADS mode. Also as described herein, ADS can include (one or more) autonomous driving modules 160, components of sensor system 120, components of vehicle systems 140, actuators 150, and (one or more) data repositories 115 that operate together to autonomously drive and control vehicle 100 in ADS mode. In ADS mode, vehicle 100 can utilize a warning system that adapts a scoring system during autonomous driving based on driver behavior or conditions associated with the driving scenario or maneuver. The warning system can generate driver warnings based on driver feedback and the satisfaction of thresholds for the adapted score values. Warnings can include alarms, displayed prompts, tactile feedback, and the like.

[0022] In addition, the vehicle 100 includes various components. It should be understood that in various embodiments, Figure 1 It may not be necessary for all elements of the vehicle 100 to be present. The vehicle 100 may have Figure 1 In addition, the vehicle 100 may have any combination of the various elements shown in FIG. Figure 1 In some arrangements, the vehicle 100 may be operated without Figure 1 Although various elements are shown as being located Figure 1 100, it should be understood that one or more of these elements may be located external to the vehicle 100. Furthermore, the elements shown may be physically separated by a significant distance. For example, as discussed, one or more components of the disclosed system may be implemented within the vehicle, while other components of the system may be implemented in a cloud computing environment or other systems remote from the vehicle 100.

[0023] Some of the possible components of vehicle 100 are Figure 1 is shown in and will be used with the subsequent Figure 1 However, for the sake of brevity in this description, Figure 2-Figure 9 After the discussion Figure 1. Additionally, it should be understood that for simplicity and clarity of illustration, reference numerals have been repeated in different figures to indicate corresponding or similar elements, where appropriate. Furthermore, the discussion outlines numerous specific details to provide a thorough understanding of the embodiments described herein. However, those skilled in the art will appreciate that various combinations of these elements may be used to practice the embodiments described herein. In either case, the vehicle 100 operates in an environment with improved driver warnings during automated driving using an adaptation score system.

[0024] Figure 2 Shown in Figure 1 An embodiment of a warning system that uses an adaptation score system to monitor driver feedback and generate driver warnings during autonomous driving of a vehicle in FIG. The warning system 170 is shown to include Figure 1 The processor 110 of the vehicle 100 is configured to monitor the status of the vehicle 100. Thus, the processor 110 can be part of the warning system 170, the warning system 170 can include a processor separate from the processor 110 of the vehicle 100, or the warning system 170 can access the processor 110 via a data bus or another communication path. In one embodiment, the warning system 170 includes a memory 210 that stores the monitoring module 220 and the adaptation module 230. The memory 210 is a random access memory (RAM), read-only memory (ROM), hard drive, flash memory, or other suitable memory for storing the modules 220 and 230. The modules 220 and 230 are, for example, computer-readable instructions that, when executed by the processor 110, cause the processor 110 to perform the various functions disclosed herein.

[0025] Figure 2The warning system 170 shown in FIG is generally an abstract form including a monitoring module 220 and an adaptation module 230. The monitoring module 220 and the adaptation module 230 may generally include instructions that function to control the processor 110 to receive data input from one or more vehicle systems or sensors of the vehicle 100. In one approach, the modules 220 and 230 may use the received data input to adapt the score value 260 during autonomous driving of the vehicle 100. In one embodiment, the monitoring module 220 may control corresponding sensors (e.g., an inertial measurement unit (IMU), input sensors, etc.) to provide data input in the form of sensor data 250. In one approach, the sensor data 250 may include vehicle input data from the sensor data 119. The warning system 170 and the monitoring module 220 may use the sensor data 250 to detect driver feedback during autonomous driving and adapt the score value 260 accordingly to prevent premature driver warnings. For example, the warning system 170 may determine that the driver's manipulation is a takeover and reduce the score value 260. The score value 260 may be reduced below the threshold for driver warning due to sufficient engagement during autonomous driving of the vehicle 100. This may indicate to the warning system 170 that the driver is aware of the operation of the vehicle 100 during autonomous driving and is exercising the desired supervision of the vehicle 100.

[0026] Additionally, the monitoring module 220 can actively or passively acquire sensor data 250. For example, the monitoring module 220 can passively sniff sensor data 250 from electronic information streams provided by various sensors to other components within the vehicle 100. Thus, the warning system 170 can employ various methods to fuse data from multiple sensors and / or sensor data acquired via wireless communication links when providing sensor data 250. Thus, in one embodiment, the sensor data 250 represents a combination of perceptions acquired from multiple sensors.

[0027] Typically, sensor data 250 includes at least vehicle control inputs. In one example, vehicle control inputs include steering inputs (e.g., steering wheel angle, rotation rate, and direction), braking inputs (e.g., brake pedal activation / pressure extent), and acceleration inputs (e.g., accelerator pedal activation / pressure extent). In further aspects, vehicle control inputs also specify transmission control inputs (e.g., gear selection), drive mode (e.g., 2-wheel drive, 4-wheel drive, etc.), engine parameters (e.g., engine revolutions per minute (RPM), driving mode for hybrid vehicles, etc.), and the like. In yet further aspects, sensor data 250 includes current dynamic data such as angular velocity, g-forces (e.g., longitudinal, lateral, etc.), velocity profiles, wheel speeds, active controls (e.g., anti-lock braking system (ABS) activation, traction control activation, stability control activation, etc.), and the like.

[0028] As an example, depending on the particular implementation, the vehicle 100 may include different versions of an IMU sensor that are each capable of making different measurements. That is, in one implementation, the IMU sensor may provide yaw rate, lateral acceleration, and longitudinal acceleration, while in a separate implementation with a more robust IMU sensor, the IMU sensor may provide additional data such as pitch rate, roll rate, vertical acceleration, etc. As such, in one or more approaches, the warning system 170 may be configured to adapt different electronic inputs depending on the availability of such information. As an additional note, the telematics data used herein generally includes sensor data 250 and may also include information such as a vehicle identifier, location information (e.g., a Global Positioning System (GPS) location), and other information that can be used by the warning system 170 to generate a warning associated with the location of the vehicle 100.

[0029] Furthermore, in one embodiment, warning system 170 includes a data repository 240. In one embodiment, data repository 240 is a database. In one embodiment, a database is an electronic data structure stored in memory 210 or another data repository and configured with routines executable by processor 110 for analyzing stored data, providing stored data, organizing stored data, and the like. Thus, in one embodiment, data repository 240 stores data used by modules 220 and 230 in performing various functions. In one embodiment, data repository 240 includes sensor data 250 and, for example, metadata characterizing various aspects of sensor data 250.

[0030] In one embodiment, the data repository 240 also includes a score value 260, a threshold value 270, and a defined rate 280. The warning system 170 can adapt (e.g., increase, decrease, etc.) the score value 260 unit by unit or step by step over time based on driver feedback at the defined rate 280. The defined rate 280 can be a rate of increase, decrease, or a step size for accumulating or decreasing the score value 260. For example, the score value 260 can change at a rate of 0.7, 1, or 1.5 per second. During autonomous driving of the vehicle 100, the warning system 170 can use a higher rate for more complex or difficult driving environments. In one approach, the warning system 170 can use a higher rate to trigger a warning for insufficient driver interaction, engagement, awareness, etc. during autonomous driving.

[0031] Furthermore, the automated driving module(s) (ADM) 160 may use the adapted score value 260 to generate warnings in an ADM mode using driver feedback. In one example, the warning system 170 may adapt the score value 260 based on driver behavior, driving complexity, driving conditions, traffic conditions, driver experience, and the like. Driver behavior may include the driver frequently controlling the vehicle 100 by touching the steering wheel during automated driving, based on perceived safety. This driver behavior may be unnecessary if the automated driving of the vehicle 100 meets the conditions for the motion plan. In one approach, a takeover may reset the score value 260 because the driver is fully aware during automated driving of the vehicle 100. A takeover during automated driving may involve the driver steering, braking, accelerating, and the like, rather than control by the automated driving module(s) 160. Driving conditions may include scenarios during automated driving involving complex curves, intersections, lane changes, and the like at specific locations. For example, a complex driving path may increase the defined rate 280 for the score value 260 to maintain the driver's attention by triggering warnings more frequently.

[0032] Regarding curves in the road, warning system 170 can shorten the time interval between triggering warnings by lowering score value 260 during autonomous driving on curved roads to attract more driver attention. Furthermore, traffic conditions may include driving on a highway, in an urban environment, in a densely populated area, in high traffic volume, etc. For example, warning system 170 may set a lower score value 260 or threshold value 270 on roads with fewer vehicles. In light traffic driving environments, a longer interval for triggering safety-related warnings may be sufficient. Conversely, warning system 170 may operate with a higher score value 260 or threshold value 270 on roads with more vehicles to trigger driver warnings more frequently.

[0033] In one approach, the warning system 170 and adaptation module 230 can adapt the score at a certain rate based on the deceleration of the vehicle 100 during a dangerous driving state. Furthermore, if the vehicle 100 maintains a stable speed or distance from another vehicle based on the sensor data 250, the warning system 170 can adapt the score value 260. In one approach, the warning system 170 can lower the score value 260 if the speed of the vehicle 100 is low (e.g., 5 miles per hour (MPH)) or increase the score value 260 at higher speeds (e.g., 90 MPH), so that the driver warning adapts to the speed during autonomous driving. If the steering wheel feedback indicates sufficient responsiveness, attention, alertness, engagement, etc. for the autonomous driving of the vehicle 100, the adaptation module 230 can lower the score value 260 for the vehicle 100. In this way, the vehicle 100 has an accurate and reliable system for generating driver warnings during certain ADS modes.

[0034] In the following example, monitoring module 220 and adaptation module 230 of test system 170 can utilize sensor data 250, score value 260, and / or threshold value 270 stored in data repository 240 to adapt warning system 170 during autonomous driving of vehicle 100. In particular, monitoring module 220 can utilize sensor data 250 and / or sensor data 119 for driver feedback from vehicle system 140 during autonomous driving. Adaptation module 230 can adapt score value 260 at a defined rate 280 based on whether driver feedback is present. When score value 260 is subsequently determined to meet threshold value 270 while awaiting driver feedback, warning system 170 generates a driver warning. Thus, warning system 170 adapts the score system based on driver behavior or driving conditions to more accurately and precisely warn the driver during autonomous driving.

[0035] Figure 3 One embodiment of a method for monitoring driver feedback using an adaptive score system during autonomous driving is shown in conjunction with a warning system. Figure 1 and Figure 2 Although the method 300 is discussed in conjunction with the warning system 170, it should be understood that the method 300 is not limited to implementation within the warning system 170, but rather the warning system 170 is one example of a system in which the method 300 may be implemented.

[0036] As a brief introduction to method 300, before discussing specifically identified functions, the warning system 170 can monitor driver feedback during automated driving of the vehicle 100. The warning system 170 generates a driver warning for a lack of driver feedback in certain ADS modes after a threshold 270 associated with an adapted score value 260 is met. The warning system 170 can warn or notify the driver that more attention, awareness, interaction, etc., is currently required for safer automated driving. Driver feedback can be input associated with steering, acceleration, braking, etc., based on sensor data 250 used by certain ADS modes for operation.

[0037] As an example, monitoring module 220 can use sensor data 250 and / or sensor data 119 for driver feedback from vehicle system 140 during autonomous driving of vehicle 100. Adaptation module 230 can adapt score value 260 at a defined rate 280 until driver feedback is received. Warning system 170 and adaptation module 230 adapt and adjust score value 260 to trigger driver warnings during autonomous driving. For example, this can be achieved by changing the defined rate 280 for score value 260 during autonomous driving of vehicle 100 based on driver state, driving complexity, driving conditions, vehicle location, etc.

[0038] Refer again to adapting driver warnings for improvement Figure 3 In the case of autonomous driving in the present embodiment, at 310, the warning system 170 can adapt the score value 260 over time. For example, the warning system 170 can adapt (e.g., increase, decrease, etc.) the score value 260 over time, unit by unit or step by step, at a defined rate 280. As explained herein, the defined rate 280 can be a rate of increase, decrease, or a step size for accumulating or de-accumulating the score value 260. As explained herein, the warning system 170 can adapt the score value 260 based on driver behavior or driving complexity. In one approach, the defined rate 280 can increase when the vehicle 100 is traveling at a high speed on a winding road during autonomous driving. If the vehicle 100 is traveling on a road with less traffic during autonomous driving, the defined rate can decrease. For each scenario, the warning system 170 adapts to warn the driver more or less frequently during autonomous driving by taking into account specific aspects of the current context.

[0039] At 320, monitoring module 220 may use sensor data 250 and / or sensor data 119 to detect driver feedback. Monitoring may include collecting and analyzing information about the driver in the form of sensor data 250 and / or sensor data 119. In one approach, warning system 170 may analyze sensor data 250 to determine when the driver modifies control inputs to vehicle 100. Warning system 170 may also analyze specific characteristics of the control inputs to further gauge the nature of the driver's inputs. As an example, the measured strength of the driver's grip on the steering wheel may determine whether takeover of vehicle 100 is possible. Warning system 170 may lower score value 260 after takeover, as the warning was unnecessary due to driver control. In another example, warning system 170 may use driver feedback regarding detected gaze to measure driver attention, awareness, etc. If the driver looks outside of the field of view due to lack of engagement during automated driving of vehicle 100, warning system 170 may increase score value 260 at a higher, defined rate 280.

[0040] After detecting driver feedback at 320, the warning system 170 reduces the score value 260 at 330. The warning system 170 can reduce the score value 260 based on a comparison with a threshold value 270. In one approach, the rate of reduction can depend on a defined rate 280. As explained herein, the defined rate 280 can be a rate of increase, decrease, or a step size for accumulating or de-accumulating the score value 260. The adaptation module 230 can use a higher rate for more complex or difficult driving environments during autonomous driving to obtain more driver engagement or awareness required in certain ADS modes. The warning system 170 can use a higher defined rate 280 to keep the driver more engaged in certain autonomous driving modes by triggering warnings more frequently. Conversely, for simpler driving environments, the warning system 170 can use a lower defined rate 280 during autonomous driving to trigger warnings less frequently.

[0041] At 340, if the monitoring module 220 does not detect the presence of driver feedback, the adaptation module 230 may compare the score value 260 to the threshold value 270. Specifically, the adaptation module 230 may determine whether the score value 260 satisfies the threshold value 270 in the data repository 240. When the score value 260 is equal to or greater than the threshold value 270, the threshold value 270 may be satisfied. As also explained herein, the warning system 170 may utilize multiple thresholds, where each threshold level triggers a different alert based on one or more hazard scenarios. As also explained herein, the score value 260 may increase or decrease over time until the threshold value 270 is satisfied, triggering a warning based on driver behavior, driving complexity, driving conditions, traffic conditions, driver experience, and the like. For example, if the speed and direction of the vehicle 100 are stable, the warning system 170 may decrease the score value 260, otherwise rapidly increase the score value 260, so that the driver warning is adapted accordingly during autonomous driving.

[0042] At 350, the adaptation module 220 determines that the score value 260 meets the threshold value 270. The score value 260 may significantly exceed the threshold value 270 to seriously warn the driver. In another approach, the warning system 170 may gradually increase the score value 260 to reach the threshold value 270. When the score value 260 meets the threshold value 270, the warning system 170 may determine that the driver input, interaction, etc. for the automated driving of the vehicle 100 is insufficient.

[0043] At 360, the warning system 170 and the adaptation module 220 may warn the driver of the vehicle 100. The warning may be visual, audible, tactile, or the like. In one approach, the warning system 170 may adapt the amount or level of the warning from low to high urgency based on the amount by which the threshold 270 is exceeded. In one approach, the warning system 170 may utilize multiple thresholds, each of which triggers a different alert based on one or more hazard scenarios. For example, if the warning system 170 suddenly increases the defined rate 280 due to a potential collision, the threshold 270 may be significantly exceeded to generate an emergency alert.

[0044] At 370, the warning system 170 may stop or cancel the warning(s). In some scenarios, the warning system 170 may have to stop or cancel the warning before the adaptation module 230 determines that the score value 260 does not meet the threshold 270. In one approach, the warning system 170 may continue to adapt the score value 260 after the warning(s) are stopped.

[0045] Now turn Figure 4 , which shows a driving environment adapted for warnings for autonomous driving 400 on a road with other vehicles. Figure 4In this embodiment, warning system 170 can adapt score value 260 during autonomous driving based on driving behavior, driving complexity, driving conditions, driving status, and the like on a highway. Driving environment 410 may include vehicle 100 traveling on highway 420. If driving environment 410 meets the safety criteria for vehicle 100, warning system 170 can adapt score value 260 while vehicle 100 is traveling on highway 420. Warning system 170 can adapt score value 260 to provide more precise driver warnings during autonomous driving. In this approach, other vehicles on highway 420 may cause score value 260 to increase rapidly, leading to more frequent warnings. Consequently, warning system 170 may require more driver attention for autonomous driving on highways with more vehicles. In this way, more frequent driver warnings by warning system 170 can improve the safety and reliability of autonomous driving.

[0046] Figure 5-Figure 9 Various scenarios and examples for adapting the warning system 170 will now be discussed and illustrated. Figure 5 is a schematic diagram illustrating the use of a scoring system 500 to generate warnings based on driver takeover or driving complexity during autonomous driving of vehicle 100. In the absence of driver feedback, warning system 170 may accumulate score value 260 in scoring system 510 over time using a fixed, defined rate 280. Score system 510 may not adapt to driver behavior, driving complexity, driver status, etc. Warning system 170 may also generate driver warnings without detecting driver feedback before threshold 270 is reached. In another approach, warning system 170 and adaptation module 230 may reset score value 260 to zero at t1 based on driver takeover of vehicle 100 in scoring system 520. Score value 260 may remain at zero while the driver is in control of vehicle 100 and begin increasing at t2 once autonomous driving of vehicle 100 is re-initiated. In this manner, scoring system 520 adapts driver warnings based on driver takeover during periods of disengagement and engagement of autonomous driving mode.

[0047] Furthermore, in the scoring system 530, the warning system 170 and the adaptation module 230 can use an increasing defined rate 280 at t1 to adapt to complex, dangerous, and other driving conditions during autonomous driving of the vehicle 100. The warning system 170 changes from the score function 532 based on the change in condition to increase safety by warning the driver more frequently during autonomous driving. In one approach, in the scoring system 540, the warning system 170 can decrease the score value 260 at t1 based on driver feedback detected by the monitoring module 220, and continue to increase it at the defined rate 280 after the driver feedback. In this way, the scoring systems 530 and 540 adapt warnings based on driving condition changes or driver feedback during autonomous driving mode.

[0048] Figure 6 is a diagram illustrating the use of an adaptive scoring system 600 to generate warnings based on the changing driving complexity of the automated driving mode. In one approach, within the scoring system 610 , the warning system 170 may transition to a higher defined rate 280 at t1 to adapt to complex, dangerous, and other driving conditions during automated driving of the vehicle 100. Complex driving conditions may include driving conditions with numerous moving objects, vehicles, pedestrians, bicycles, and the like. Complex driving conditions may also include hilly terrain, steep slopes, intersections, junctions, unpaved roads, winding roads, obstructions, blind spots, poor visibility, and high speeds. When the vehicle 100 enters a simpler driving condition during automated driving, the warning system 170 may reduce the defined rate 280 at t2. For example, a simple driving condition may include driving conditions with fewer moving objects, vehicles, pedestrians, bicycles, and the like. Simple driving conditions may also include flat roads, gentle slopes, paved roads, straight roads, good visibility, and lower speeds. In this way, the warning system 170 adapts the score system 610 during complex maneuvers of the vehicle 100 rather than using the score function 612 to improve safety by gaining the driver's attention more quickly at t1.

[0049] Furthermore, the warning system 170 using the scoring system 620 can adapt the score value 260 based on a reset and driver takeover, a defined rate of decrease 280 during driver control, or a reset and driver takeover during the autonomous driving time periods t1 and t2. Furthermore, the warning system 170 can adapt the score value 260 based on driver takeover, resulting in a defined rate of decrease 280 during periods of driver control and inactivity during the autonomous driving time periods t1-t3. In one approach, less frequent warnings are required when the driver is controlling the vehicle as part of certain autonomous driving modes. The monitoring module 220 can detect the presence of driver feedback based on touching, gripping, turning, etc., of the steering wheel of the vehicle 100. Other examples of driver feedback can include the driver engaging the accelerator pedal of the vehicle 100, engaging the brake pedal of the vehicle 100, turning on the turn signal of the vehicle 100, etc. In the scoring system 620, the warning system 170 can substantially continuously reduce the score value 260 while the driver is in control of the vehicle 100.

[0050] Figure 7 is a diagram illustrating the use of an adaptive score system 700 in which a defined rate of change is varied to generate warnings during autonomous driving. In one approach, once score value 260 meets threshold 270, warning system 170 may change or reduce the value of defined rate 280 to zero after t1 in score system 710. Warning system 170 changing or reducing the value of defined rate 280 to zero may be performed based on driver behavior, driving complexity, driving conditions, and the like. For example, vehicle 100 may be transitioning to an area with less traffic that requires less frequent warnings during autonomous driving. In one approach, score system 720 may adapt when score value 260 meets threshold 270 between t1 and t2 due to driver control during autonomous driving of vehicle 100. For example, driver control may reduce score value 260 below threshold 270 to end or stop the warning. Score value 260 may have accumulated due to driver drowsiness or unconsciousness during autonomous driving of vehicle 100. The warning may awaken the driver to take control of vehicle 100. The warning system 170 may reduce or reset the score value 260 at a defined rate 280 based on the amount of driver awareness or attention following the warning at t1. In this way, the warning system 170 automatically adapts to improve warnings during autonomous driving by returning to a pre-alert state based on driver behavior, habits, driving complexity, etc.

[0051] Figure 8is a diagram illustrating the use of an adaptive score system 800, in which the warning system provides different types of driver warnings after a threshold is met during automated driving. Warning system 170, using score system 810, can adapt score value 260 based on driver control or inactivity at or between transition points t1 or t2. Score value 260 can remain constant after t1 without driver control of vehicle 100. Score value 260 can also decrease after t2 with driver control. In this way, warning system 170 automatically adapts based on driver control and inactivity to improve warnings during automated driving of vehicle 100.

[0052] In one approach, the warning system 170 can utilize a scoring system 810 that includes multiple values ​​or levels associated with the thresholds 270. For example, when the score value 260 meets the "Warning A" threshold at t1, the warning system 170 can generate an audible warning. When the score value 260 meets the "Warning B" threshold, the warning system 170 can also generate a visual warning. When the score value 260 meets the "Warning C" threshold, the warning system 170 can also generate a tactile warning. Furthermore, the warning system 170 can adapt the defined rate 280 at or between times t1 or t2 based on driving behavior, driving conditions, driving complexity, driver state, and the like. In this way, the warning system 170 improves driver warnings by adapting the type and level of driver warnings to changing conditions.

[0053] Figure 9 is a diagram illustrating the use of an adaptive score system 900, wherein a defined rate varies based on driver state during autonomous driving. Using score system 910, warning system 170 can adapt score value 260 based on driver behavior and control during autonomous driving of vehicle 100. For example, when the highest threshold level is met, warning system 170 can reset score value 260 to the minimum warning threshold at t2. In one approach, warning system 170 can selectively change or transition score value 260 from the highest tactile warning level to an intermediate visual warning level within score system 920. Warning system 170 can subsequently reduce score value 260 based on driver interaction or driving state during autonomous driving of vehicle 100 to cross the lowest audible warning threshold level. Furthermore, warning system 170 can reduce score value 260 at a defined rate 280 due to driver control after t2.

[0054] Furthermore, in the scoring system 930, the warning system 170 can adapt the score value 260 based on the driver's state. The warning system 170 can adapt the defined rate 280 based on the sensor data 119 or 250 associated with the driver's state. For example, gaze tracking or attention data may indicate that the driver is awake at t1, and therefore the defined rate 280 is adapted. The warning system 170 can determine from the sensor data 119 or 250 that the driver is paying attention and fully controlling the vehicle 100. Therefore, the defined rate 280 can also be reduced to zero at t1, and the score value 260 remains constant until further adaptation is triggered.

[0055] As an example environment in which the systems and methods disclosed herein may operate, the following will now be discussed in full detail: Figure 1 In some cases, the vehicle 100 is configured to selectively switch between different operating / control modes depending on the direction of one or more modules / systems of the vehicle 100. In one approach, the modes include: 0, no automation; 1, driver assistance; 2, partial automation; 3, conditional automation; 4, high automation; 5, full automation. In one or more arrangements, the vehicle 100 can be configured to operate in only a subset of the possible modes.

[0056] In one or more embodiments, the vehicle 100 is an automatic or autonomous vehicle. As an automatic vehicle, the vehicle 100 can be configured to use ADS to perform autonomous functions through (one or more) autonomous driving modules 160. As used herein, an "autonomous vehicle" refers to a vehicle that can operate in an autonomous, automatic or ADS mode (e.g., Category 5, fully automated). "Autonomous mode" or ADS mode refers to using one or more computing systems to navigate and / or manipulate the vehicle 100 along a driving route to control the vehicle 100 with minimal input from a human driver or without input from a human driver. In one or more embodiments, the vehicle 100 is highly automated or fully automated. In one embodiment, the vehicle 100 is configured with one or more semi-autonomous operating modes in which one or more computing systems perform a portion of the navigation and / or manipulation of the vehicle along the driving route, and the vehicle operator (i.e., the driver) provides input to the vehicle to perform a portion of the navigation and / or manipulation of the vehicle 100 along the driving route.

[0057] The vehicle 100 may include one or more processors 110. In one or more arrangements, the processor(s) 110 may be the main processor of the vehicle 100. For example, the processor(s) 110 may be an electronic control unit (ECU), an application-specific integrated circuit (ASIC), a microprocessor, or the like. The vehicle 100 may include one or more data repositories 115 for storing one or more types of data. The data repositories 115 may include volatile and / or non-volatile memory. Examples of suitable data repositories 115 include RAM (random access memory), flash memory, ROM (read-only memory), PROM (programmable read-only memory), EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), registers, magnetic disks, optical disks, and hard drives. The data repositories 115 may be components of the processor(s) 110, or the data repositories 115 may be operably connected to the processor(s) 110 for use thereby. As used throughout this specification, the term "operably connected" may include direct or indirect connection, with an indirect connection including a connection without direct physical contact.

[0058] In one or more arrangements, one or more data repositories 115 may include map data 116. Map data 116 may include maps of one or more geographic areas. In some cases, map data 116 may include information or data about roads, traffic control devices, road markings, structures, features, and / or landmarks in one or more geographic areas. Map data 116 may have any suitable form. In some cases, map data 116 may include a bird's-eye view of the area. In some cases, map data 116 may include a ground-level view of the area, including a 360-degree ground-level view. Map data 116 may include measurements, dimensions, distances, and / or information for one or more items included in map data 116 and / or relative to other items included in map data 116. Map data 116 may include a digital map with information about road geometry.

[0059] In one or more arrangements, the map data 116 may include one or more terrain maps 117. The terrain map(s) 117 may include information about the terrain, roads, surfaces, and / or other features of one or more geographic areas. The terrain map(s) 117 may include elevation data for the one or more geographic areas. The terrain map(s) 117 may define one or more ground surfaces, which may include paved roads, unpaved roads, land, and other features that define a ground surface.

[0060] In one or more arrangements, map data 116 may include one or more static obstacle maps 118. Static obstacle map(s) 118 may include information about one or more static obstacles located within one or more geographic areas. A "static obstacle" is a physical object whose position does not change or does not change significantly over a period of time and / or whose size does not change or does not change significantly over a period of time. Examples of static obstacles may include trees, buildings, curbs, fences, railings, medians, utility poles, statues, monuments, signs, benches, furniture, mailboxes, boulders, and hills. A static obstacle may be an object that extends above ground level. The one or more static obstacles included in static obstacle map(s) 118 may have associated therewith location data, size data, dimensional data, material data, and / or other data. Static obstacle map(s) 118 may include measurements, dimensions, distances, and / or information about the one or more static obstacles. Static obstacle map(s) 118 may be high-quality and / or highly detailed. Static obstacle map(s) 118 may be updated to reflect changes within the mapped area.

[0061] The one or more data repositories 115 may include sensor data 119. In this context, "sensor data" means any information about sensors with which the vehicle 100 is equipped, including the capabilities and other information about such sensors. As will be explained below, the vehicle 100 may include a sensor system 120. The sensor data 119 may relate to one or more sensors of the sensor system 120. As an example, in one or more arrangements, the sensor data 119 may include information about one or more light detection and ranging (LIDAR) sensors 124 of the sensor system 120.

[0062] The sensor data 119 includes at least vehicle control inputs. In one example, the vehicle control inputs include steering inputs (e.g., steering wheel angle, rotation rate, and direction), braking inputs (e.g., degree of brake pedal activation / pressure), and acceleration inputs (e.g., degree of accelerator pedal activation / pressure). In further aspects, the vehicle control inputs also specify transmission control inputs (e.g., gear selection), drive mode (e.g., 2-wheel drive, 4-wheel drive, etc.), engine / motor parameters (e.g., engine revolutions per minute (RPM), hybrid vehicle driving mode, etc.), etc. In yet further aspects, the sensor data 119 includes current dynamic data such as angular velocity, g-forces (e.g., longitudinal, lateral, etc.), velocity profiles, wheel speeds, activated controls (e.g., anti-lock braking system (ABS) activation, traction control activation, stability control activation, etc.), etc.

[0063] In some cases, at least a portion of the map data 116 and / or the sensor data 119 may be located in one or more data repositories 115, where the one or more data repositories 115 are located onboard the vehicle 100. Alternatively or additionally, at least a portion of the map data 116 and / or the sensor data 119 may be located in one or more data repositories 115 remote from the vehicle 100.

[0064] As described above, the vehicle 100 may include a sensor system 120. The sensor system 120 may include one or more sensors. A "sensor" refers to a device that can detect and / or sense something. In at least one embodiment, one or more sensors detect and / or sense in real time. As used herein, the term "real time" refers to a level of processing responsiveness that a user or system perceives as sufficiently immediate for a particular process or determination to be performed, or that enables a processor to keep up with certain external processes.

[0065] In an arrangement where the sensor system 120 includes multiple sensors, the sensors may function independently or two or more sensors may function in combination. The sensor system 120 and / or one or more sensors may be operably connected to the processor(s) 110, the data repository(s) 115, and / or another element of the vehicle 100. The sensor system 120 may generate observations about a portion of the environment of the vehicle 100 (e.g., nearby vehicles).

[0066] The sensor system 120 may include any suitable type of sensor. Various examples of different types of sensors will be described herein. However, it should be understood that the embodiments are not limited to the specific sensors described. The sensor system 120 may include one or more vehicle sensors 121. The vehicle sensor(s) 121 may detect information about the vehicle 100 itself. In one or more arrangements, the vehicle sensor(s) 121 may be configured to detect changes in the position and orientation of the vehicle 100, such as based on inertial acceleration. In one or more arrangements, the vehicle sensor(s) 121 may include one or more accelerometers, one or more gyroscopes, an inertial measurement unit (IMU), a dead reckoning system, a global navigation satellite system (GNSS), GPS, a navigation system 147, and / or other suitable sensors. The vehicle sensor(s) 121 may be configured to detect one or more characteristics of the vehicle 100 and / or the manner in which the vehicle 100 operates. In one or more arrangements, the vehicle sensor(s) 121 may include a rateometer to determine the current speed of the vehicle 100.

[0067] Alternatively or in addition, the sensor system 120 may include one or more environmental sensors 122 configured to acquire data about the environment surrounding the vehicle 100 in which the vehicle 100 is operating. "Surrounding environment data" includes data about the external environment in which the vehicle is located, or one or more portions thereof. For example, the one or more environmental sensors 122 may be configured to sense obstacles in at least a portion of the external environment of the vehicle 100 and / or data about such obstacles. Such obstacles may be stationary objects and / or dynamic objects. The one or more environmental sensors 122 may be configured to detect other things in the external environment of the vehicle 100, such as, for example, lane markings, signs, traffic lights, traffic signs, lane lines, crosswalks, curbs close to the vehicle 100, objects not on the road, etc.

[0068] Various examples of sensors of sensor system 120 are described herein. Example sensors may be part of one or more environmental sensors 122 and / or one or more vehicle sensors 121. However, it should be understood that embodiments are not limited to the specific sensors described.

[0069] As an example, in one or more arrangements, the sensor system 120 may include one or more of the following: a radar sensor 123, a LIDAR sensor 124, a sonar sensor 125, a weather sensor, a tactile sensor, a position sensor, and / or one or more cameras 126. In one or more arrangements, the one or more cameras 126 may be a high dynamic range (HDR) camera, a stereo, or an infrared (IR) camera.

[0070] Vehicle 100 may include an input system 130. An "input system" includes components, arrangements, or groups thereof that enable various entities to input data into the machine. Input system 130 may receive input from vehicle occupants. Vehicle 100 may include an output system 135. An "output system" includes one or more components that facilitate the presentation of data to vehicle occupants.

[0071] The vehicle 100 may include one or more vehicle systems 140. Various examples of the one or more vehicle systems 140 are described in Figure 1 . However, vehicle 100 may include more, fewer, or different vehicle systems. It should be understood that although specific vehicle systems are defined separately, each or any of the systems or portions thereof may be combined or separated in other ways via hardware and / or software within vehicle 100. Vehicle 100 may include a propulsion system 141, a braking system 142, a steering system 143, a throttle system 144, a transmission system 145, a signaling system 146, and / or a navigation system 147. Each of these systems may include one or more devices, components, and / or combinations thereof now known or later developed.

[0072] The navigation system 147 may include one or more devices, applications, and / or combinations thereof, now known or later developed, configured to determine the geographic location of the vehicle 100 and / or determine the travel route of the vehicle 100. The navigation system 147 may include one or more mapping applications to determine the travel route of the vehicle 100. The navigation system 147 may include a global positioning system, a local positioning system, or a geographic positioning system.

[0073] The processor(s) 110 and / or the autonomous driving module(s) 160 may be operably connected to communicate with various vehicle systems 140 and / or individual components thereof. For example, the processor(s) 110 and / or the autonomous driving module(s) 160 may communicate to send and / or receive information from the various vehicle systems 140 to control the movement of the vehicle 100. The processor(s) 110 and / or the autonomous driving module(s) 160 may control some or all of the vehicle systems 140 and, thus, may be partially or fully autonomous as defined by Society of Automotive Engineers (SAE) Levels 0 to 5.

[0074] As another example, the processor(s) 110 and / or the autonomous driving module(s) 160 may be operably connected to communicate with various vehicle systems 140 and / or individual components thereof. For example, the processor(s) 110 and / or the autonomous driving module(s) 160 may communicate to send and / or receive information from the various vehicle systems 140 to control the movement of the vehicle 100. The processor(s) 110 and / or the autonomous driving module(s) 160 may control some or all of the vehicle systems 140.

[0075] Processor(s) 110 and / or autonomous driving module(s) 160 may be operable to control navigation and maneuvering of vehicle 100 by controlling one or more of vehicle systems 140 and / or components thereof. For example, when operating in an automatic or autonomous mode, processor(s) 110 and / or autonomous driving module(s) 160 may control the direction and / or speed of vehicle 100. Processor(s) 110 and / or autonomous driving module(s) 160 may cause vehicle 100 to accelerate, decelerate, and / or change direction. As used herein, "cause" or "result" means to cause, compel, force, direct, command, direct, instruct, or instruct an event or action to occur, or at least to be in a state where such an event or action may occur, whether directly or indirectly, and / or to enable an event or action to occur, or at least to be in a state where such an event or action may occur.

[0076] The vehicle 100 may include one or more actuators 150. The actuators 150 may be an element or combination of elements operable to modify one or more of the vehicle systems 140 or components in response to receiving signals or other inputs from the processor(s) 110 and / or the autonomous driving module(s) 160. For example, the one or more actuators 150 may include a motor, a pneumatic actuator, a hydraulic piston, a relay, a solenoid, a piezoelectric actuator, etc.

[0077] The vehicle 100 may include one or more modules, at least some of which are described herein. These modules may be implemented as computer-readable program code that, when executed by the processor 110, implements one or more of the various processes described herein. One or more of the modules may be components of the processor(s) 110, or one or more of the modules may be executed on and / or distributed in other processing systems to which the processor(s) 110 are operatively connected. A module may include instructions (e.g., program logic) that may be executed by one or more processors 110. Alternatively or additionally, one or more data repositories 115 may contain such instructions.

[0078] In one or more arrangements, one or more of the modules described herein may include artificial intelligence elements, such as neural networks, fuzzy logic, or other machine learning algorithms. Additionally, in one or more arrangements, one or more of the modules may be distributed across multiple modules described herein. In one or more arrangements, two or more of the modules described herein may be combined into a single module.

[0079] The vehicle 100 may include one or more autonomous driving modules 160. The autonomous driving module(s) 160 may be configured to receive data from the sensor system 120 and / or any other type of system capable of capturing information about the vehicle 100 and / or the environment outside of the vehicle 100. In one or more arrangements, the autonomous driving module(s) 160 may use such data to generate one or more driving scenario models. The autonomous driving module(s) 160 may determine the location and velocity of the vehicle 100. The autonomous driving module(s) 160 may determine the location of obstacles or other environmental features including traffic signs, trees, shrubs, neighboring vehicles, pedestrians, and the like.

[0080] The autonomous driving module(s) 160 may be configured to receive and / or determine position information of obstacles within the external environment of the vehicle 100 for use by the processor(s) 110 and / or one or more of the modules described herein to estimate the position and orientation of the vehicle 100, the position of the vehicle in a global coordinate system based on signals from a plurality of satellites, or any other data and / or signals that may be used to determine the current state of the vehicle 100 or to determine the position of the vehicle 100 relative to its environment for use in creating a map or determining the position of the vehicle 100 relative to map data.

[0081] The autonomous driving module(s) 160 can be configured to determine driving path(s), the current autonomous driving maneuver of the vehicle 100, future autonomous driving maneuvers, and / or modifications to the current autonomous driving maneuver based on data acquired by the sensor system 120, a driving scenario model, and / or data from any other suitable source (such as determinations based on sensor data 250 implemented by the occupancy module). A "driving maneuver" means one or more actions that affect the movement of the vehicle. Examples of driving maneuvers include: accelerating, decelerating, braking, turning, lateral movement of the vehicle 100, changing lanes, merging into lanes, reversing, etc. The autonomous driving module(s) 160 can be configured to implement the determined driving maneuvers. The autonomous driving module(s) 160 can directly or indirectly cause such autonomous driving maneuvers to be implemented. As used herein, "cause" or "cause" means to cause, command, instruct, or instruct an event or action to occur, or at least to establish a state in which such an event or action may occur, whether directly or indirectly, and / or to enable an event or action to occur, or at least to establish a state in which such an event or action may occur. (One or more) autonomous driving modules 160 can be configured to perform various vehicle functions and / or send data to, receive data from, interact with, and / or control vehicle 100 or one or more of its systems (e.g., one or more of vehicle systems 140).

[0082] Detailed embodiments are disclosed herein. However, it should be understood that the disclosed embodiments are intended to be illustrative only. Therefore, the specific structural and functional details disclosed herein should not be construed as limiting, but rather as a basis for the claims and as a representative basis for teaching those skilled in the art to employ the aspects of this disclosure in various ways in virtually any suitable detailed configuration. Furthermore, the terms and phrases used herein are not intended to be limiting, but rather to provide an understandable description of possible implementations. Figures 1-9Various embodiments are shown in the drawings, but the embodiments are not limited to the structures or applications shown.

[0083] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of the systems, methods, and computer program products according to various embodiments. In this regard, each box in the flowchart or block diagram may represent a module, segment, or portion of a code that includes one or more executable instructions for implementing (one or more) specified logical functions. It should also be noted that in some alternative implementations, the functions illustrated in the box may not occur in the order illustrated in the figure. For example, depending on the functions involved, two boxes shown in succession may actually be executed substantially in parallel, or the boxes may sometimes be executed in reverse order.

[0084] The systems, components and / or processes described above can be implemented in hardware or a combination of hardware and software and can be implemented in a centralized manner in a processing system, or in a distributed manner with different elements spread across several interconnected processing systems. Any type of processing system or another device suitable for performing the methods described herein is suitable. A typical combination of hardware and software can be a processing system with a computer-usable program code that controls the processing system when loaded and executed so that the processing system performs the methods described herein. The systems, components and / or processes can also be embedded in a computer-readable storage device that tangibly embodies a machine-executable instruction program to perform the methods and processes described herein, such as a computer program product or other data program storage device. These elements can also be embedded in an application product that includes all the features that enable the methods described herein to be implemented and that can perform these methods when loaded into a processing system.

[0085] In addition, the arrangements described herein may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied (e.g., stored) thereon. Any combination of one or more computer-readable media may be utilized. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The phrase "computer-readable storage medium" means a non-transitory storage medium. A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media would include the following: a portable computer floppy disk, a hard disk drive (HDD), a solid-state drive (SSD), a ROM, an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain or store a program used by or in conjunction with an instruction execution system, apparatus, or device.

[0086] Typically, modules as used herein include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific data types. In a further aspect, memory typically stores the modules described. The memory associated with the module can be a buffer or cache embedded in a processor, RAM, ROM, flash memory, or other suitable electronic storage medium. In a further aspect, the module as contemplated by the present disclosure is implemented as a hardware component of an ASIC, a system on a chip (SoC), as a programmable logic array (PLA), or as another suitable hardware component embedded with a defined configuration set (e.g., instructions) for performing the disclosed functions.

[0087] Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber, cable, radio frequency (RF), etc., or any suitable combination of the foregoing. Computer program code for performing operations of various aspects of the present arrangement may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, TM, Smalltalk, C++, etc.) and traditional procedural programming languages ​​(such as the "C" programming language or similar programming languages). The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be to an external computer (e.g., over the Internet using an Internet service provider).

[0088] As used herein, the terms "one" and "an" are defined as one or more than one. As used herein, the term "plurality" is defined as two or more. As used herein, the term "another" is defined as at least a second or more. As used herein, the terms "include" and / or "have" are defined as including (i.e., open language). As used herein, the phrase "at least one of ... and ... " refers to and includes any and all possible combinations of one or more of the associated listed items. For example, the phrase "at least one of A, B, and C" includes only A, only B, only C, or any combination thereof (e.g., AB, AC, BC, or ABC).

[0089] The aspects herein may be embodied in other forms without departing from the spirit or essential attributes thereof. Accordingly, reference should be made to the following claims, rather than to the foregoing specification, as indicating the scope thereof.

Claims

1. A warning system for improving driver warnings for autonomous driving by a vehicle, comprising: one or more processors; a memory communicatively coupled to the one or more processors and storing: a monitoring module comprising instructions that, when executed by the one or more processors, cause the one or more processors to: monitoring a vehicle control system to obtain driver feedback during said automated driving; and an adaptation module comprising instructions that, when executed by the one or more processors, cause the one or more processors to: adapting the score value at a defined rate based on whether the driver feedback is present; and generating a warning to the driver in response to determining that the score value satisfies a threshold value before the driver feedback is present, The adaptation module includes instructions for adapting the score value, the instructions for adapting the score value including instructions for adapting the defined rate according to the complexity of the autonomous driving driving maneuver associated with a driving state, wherein the driving state includes a position of the vehicle.

2. The warning system of claim 1 , wherein the adaptation module includes instructions for adapting the score value, the instructions for adapting the score value including instructions for adjusting the score value from a reduced value and re-initiating the automated driving upon detecting the presence of the driver feedback.

3. The warning system of claim 2, wherein the adaptation module includes instructions for adapting the score value, the instructions for adapting the score value including instructions for maintaining the score value constant before the autonomous driving is restarted.

4. The warning system of claim 1 , wherein the adaptation module includes instructions for adapting the score value, the instructions for adapting the score value including instructions for adapting the defined rate in the absence of driver feedback before the threshold is met.

5. The warning system of claim 1 , wherein the adaptation module further comprises instructions for adapting the score value, the instructions for adapting the score value comprising instructions for adapting the defined rate as a function of the complexity of the automated driving maneuver associated with the speed of the vehicle.

6. The warning system of claim 1, wherein the adaptation module further comprises instructions for adapting the score value, the instructions for adapting the score value comprising instructions for adapting the defined rate based on driver status including driver experience level.

7. The warning system of claim 1 , wherein the adaptation module further comprises instructions that, when executed by the one or more processors, cause the one or more processors to reset the score value to zero if the driver feedback is a manipulation by the driver as a vehicle takeover.

8. A non-transitory computer-readable medium for improving driver warnings for autonomous driving by a vehicle and comprising instructions that, when executed by one or more processors, cause the one or more processors to: monitoring a vehicle control system to obtain driver feedback during said automated driving; adapting the score value at a defined rate based on whether the driver feedback is present; and generating a warning to the driver in response to determining that the score value satisfies a threshold value before the driver feedback is present, Wherein the instructions for adapting the score value include instructions for adapting the defined rate based on a complexity of the automated driving maneuver associated with a driving state, wherein the driving state includes a position of the vehicle.

9. The non-transitory computer-readable medium of claim 8, wherein the instructions for adapting the score value include instructions for adjusting the score value from a reduced value and re-initiating the automated driving upon detecting the presence of the driver feedback.

10. The non-transitory computer-readable medium of claim 9, wherein the instructions for adapting the score value include instructions for maintaining the score value constant before the autonomous driving is restarted.

11. A method for improving driver warnings for automated driving by a vehicle, the method comprising: monitoring a vehicle control system to obtain driver feedback during said automated driving; adapting the score value at a defined rate based on whether the driver feedback is present; as well as generating a warning to the driver in response to a determination that the score value satisfies a threshold value before the driver feedback is present, Wherein adapting the score value further comprises adapting the defined rate according to a complexity of the automated driving maneuver associated with a driving state, wherein the driving state comprises a position of the vehicle. 12 . The method of claim 11 , further comprising adjusting the score value from a reduced value and re-initiating the automated driving upon detecting the presence of the driver feedback.

13. The method of claim 12 further comprising maintaining the score value constant before the autonomous driving is restarted.

14. The method of claim 11, wherein adapting the score value further comprises adapting the defined rate in the absence of driver feedback prior to meeting the threshold.

15. The method of claim 11, wherein adapting the score value further comprises adapting the defined rate based on a complexity of the automated driving maneuver associated with a speed of the vehicle.

16. The method of claim 11, wherein adapting the score value further comprises adapting the defined rate based on driver status including driver experience level. 17 . The method of claim 11 , further comprising resetting the score value to zero if the driver feedback is a manipulation by the driver as a vehicle takeover.

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