Systems and methods for controlling autonomous vehicles

Abstract

To provide systems and methods.SOLUTION: Systems and methods for controlling an autonomous vehicle are disclosed herein. One embodiment determines a reference path for the autonomous vehicle along a roadway segment, and steers the autonomous vehicle along a path that includes controlled back and forth lateral deviations from the reference path along the roadway segment to provide feedback to an occupant of the autonomous vehicle, the feedback indicating to the occupant that the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The subject matter described herein relates generally to vehicles, and more particularly to systems and methods for controlling autonomous vehicles. [Background technology]

[0002] One of the challenges the automotive industry faces as it introduces autonomous vehicles is gaining consumer acceptance and trust that they are trustworthy and safe. Another challenge is that, in some situations, occupants of an autonomous vehicle may not be aware that the vehicle is truly operating in autonomous mode. Summary of the Invention

[0003] Presented herein is an example system for controlling an autonomous vehicle. The system includes one or more processors and a memory communicatively coupled to the one or more processors. The memory stores a path planning module including instructions that, when executed by the one or more processors, cause the one or more processors to determine a reference path for the autonomous vehicle along a road segment. The memory also stores a feedback generation module including instructions that, when executed by the one or more processors, cause the one or more processors to steer the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment and provide feedback to an occupant of the autonomous vehicle indicating that the autonomous vehicle is in autonomous driving mode and that the autonomous driving mode is operating correctly.

[0004] Another embodiment is a non-transitory computer-readable medium for controlling an autonomous vehicle storing instructions that, when executed by one or more processors, cause the one or more processors to determine a reference path for the autonomous vehicle along a road segment. The instructions also cause the one or more processors to steer the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment to provide feedback to an occupant of the autonomous vehicle that indicates the autonomous vehicle is in autonomous driving mode and that the autonomous driving mode is operating correctly.

[0005] In another embodiment, a method for controlling an autonomous vehicle is disclosed. The method includes determining a reference path for the autonomous vehicle along a road segment. The method also includes steering the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment to provide feedback to an occupant of the autonomous vehicle that indicates the autonomous vehicle is in autonomous driving mode and that the autonomous driving mode is operating correctly. [Brief explanation of the drawings]

[0006] So that aspects of the above-cited features of the present disclosure can be understood in detail, a more particular description of the present disclosure briefly summarized above will be made by reference to embodiments, some of which are illustrated in the accompanying drawings. It should be noted, however, that the accompanying drawings illustrate only possible embodiments of the present disclosure and therefore should not be considered as limiting its scope. The present disclosure may permit other embodiments.

[0007] [Figure 1] FIG. 1 is a diagram illustrating one embodiment of a vehicle in which the systems and methods disclosed herein can be implemented. [Figure 2] FIG. 2 is a functional block diagram of an autonomous driving feedback system in accordance with an exemplary embodiment of the present invention. [Figure 3]FIG. 3 is a diagram illustrating repeated controlled lateral deviations from a reference path according to an exemplary embodiment of the present invention. [Figure 4] FIG. 4 is a diagram illustrating the selection of repeated controlled lateral deviations from a reference path to avoid road obstacles according to an exemplary embodiment of the present invention. [Figure 5] FIG. 5 illustrates the selection of repeated controlled lateral deviations from a reference path that avoids moving the autonomous vehicle closer to objects detected outside but near lane boundaries, in accordance with an exemplary embodiment of the present invention. [Figure 6] FIG. 6 is a flowchart illustrating a method for controlling an autonomous vehicle in accordance with an exemplary embodiment of the present invention.

[0008] To facilitate understanding, the same reference numerals have been used, whenever possible, to identify identical components common to the figures. Furthermore, components of one or more embodiments may be advantageously adapted for use in other embodiments described herein. DETAILED DESCRIPTION OF THE INVENTION

[0009] Various embodiments of the autonomous driving feedback system described herein increase trust, confidence, and awareness of current system conditions related to autonomous driving. Increasing a vehicle owner's or operator's trust in the vehicle's autonomous driving system helps prevent premature or unnecessary handover of control of the vehicle to a human owner / operator.

[0010] One principle underlying the embodiments described herein is that, due to limitations in the human sensory system, human drivers have typically been observed to "zigzag" (go back and forth) at least slightly, even along perfectly straight road segments. As a result, an autonomous vehicle traveling perfectly straight along a straight road segment may feel unnatural to an occupant of the autonomous vehicle. This unnaturalness may cause the occupant to doubt whether the vehicle is truly operating in autonomous mode, and may even lead the occupant to rely on lights, icons, audible sounds, or other instruments on the instrument panel to determine the status of the vehicle's autonomous driving system.

[0011] In various embodiments, an autonomous driving feedback system within the vehicle determines a reference path for the autonomous vehicle along an upcoming road segment. In some embodiments, the reference path coincides, at least approximately, with a line extending longitudinally along the centerline of the lane in which the vehicle is traveling. Such a reference path may also be referred to as a “normal” or “ideal” path or trajectory that the vehicle should follow over the course of the road segment. In various embodiments, the autonomous driving feedback system autonomously steers the vehicle along a path that includes repeated, controlled lateral deviations from the reference path along the road segment. This provides feedback to an occupant of the autonomous vehicle, indicating to the occupant that the autonomous vehicle is in autonomous driving mode and that the autonomous driving mode is operating correctly. In this manner, intentionally controlled side-to-side imperfections in the vehicle's driving trajectory provide feedback to the occupant of the vehicle that increases confidence and awareness of the status of the autonomous driving system within the vehicle (i.e., that the system is assured and functioning correctly).

[0012] The feedback experienced by vehicle occupants can be thought of as being of two types. First, the occupant can visually see the vehicle moving slowly "zigzag," or repeatedly, toward one lane boundary and then the other. Second, the occupant can feel slight G-forces due to the vehicle's subtle, repeated lateral movements as it progresses along the road segment. These visual and kinesthetic (combined with the vestibular system) forms of feedback help reassure the occupant that the vehicle is in autonomous mode and that it is operating correctly in that mode. Of course, a blind occupant will only experience kinesthetic (combined with the vestibular system) feedback.

[0013] Referring to FIG. 1 , an example of an autonomous vehicle 100 (hereinafter, “vehicle 100”) capable of implementing the systems and methods disclosed herein is illustrated. As used herein, a “vehicle” refers to any form of motorized transportation. In one or more embodiments, vehicle 100 may be an automobile. Vehicle 100 may operate, at least part of the time, in a fully autonomous mode, i.e., at what is referred to as autonomy levels 3-5, particularly level 5, under the Society of Automotive Engineers (SAE) autonomy nomenclature. Vehicle 100 may include an autonomous driving feedback system 170 or functionality that supports or interacts with autonomous driving feedback system 170 and, therefore, may benefit from the functionality discussed herein. The example of vehicle 100 used herein is equally applicable to any device that may incorporate the systems or methods described herein.

[0014] Vehicle 100 also includes various components. It should be understood that in various embodiments, vehicle 100 need not include all of the components shown in FIG. 1 . Vehicle 100 may include any combination of the various components shown in FIG. 1 . Furthermore, vehicle 100 may include additional components to those shown in FIG. 1 . In some configurations, vehicle 100 may be implemented without one or more of the components shown in FIG. 1 , including autonomous driving feedback system 170. While various components are shown as being located within vehicle 100 in FIG. 1 , it should be understood that one or more of these components may be located external to vehicle 100. Furthermore, the components shown in the figures may be physically separated by a large distance.

[0015] Some of the possible components of vehicle 100 are shown in FIG. 1 and described in connection with subsequent figures. However, a description of many of the components of FIG. 1 is provided after the discussion of FIGS. 2-6 for purposes of brevity. Furthermore, it should be understood that, for simplicity and clarity of illustration, where deemed appropriate, reference numerals may be repeated among the different figures to indicate corresponding or analogous components. Additionally, numerous specific details are outlined in the discussion to provide a thorough understanding of the embodiments described herein. However, those skilled in the art will understand that the embodiments described herein may be implemented using various combinations of these components.

[0016] The sensor system 120 includes one or more vehicle sensors 121. The vehicle sensors 121 may include one or more positioning systems, such as a dead reckoning system or a global navigation satellite system (GNSS) such as a global positioning system (GPS). The vehicle sensors 121 may also include, for example, vehicle and bus sensors that output speed and steering angle data related to the vehicle 100. The sensor system 120 also includes one or more environmental sensors 122. The environmental sensors 122 may include one or more radar sensors 123, one or more light detection and ranging (LIDAR) sensors 124, one or more sonar sensors 125, and one or more cameras 126. One or more of these various types of environmental sensors 122, one or more positioning systems, and map data 116 are used in localization, navigation, and route planning. The environmental sensors 122 can also be used to detect and recognize objects in the environment external to the vehicle 100 (e.g., other vehicles, pedestrians, bicyclists, animals, obstacles, construction equipment, construction barriers, etc.).

[0017] To control and direct autonomous driving, the vehicle 100 includes one or more autonomous driving modules 160 that operate in conjunction with various vehicle systems 140, such as a propulsion system 141, a steering system 143, a braking system 142, and a navigation system 147.

[0018] Referring to Figure 2, this figure is a functional block diagram of autonomous driving feedback system 170, in accordance with an exemplary embodiment of the present invention. In this embodiment, autonomous driving feedback system 170 is shown as including one or more processors 110 from vehicle 100 of Figure 1. In general, one or more processors 110 may be part of autonomous driving feedback system 170, autonomous driving feedback system 170 may include one or more processors separate from one or more processors 110 of vehicle 100, or autonomous driving feedback system 170 may access one or more processors 110 via a data bus or another communication path, depending on the embodiment.

[0019] In one embodiment, memory 210 stores path planning module 220 and feedback generation module 230. Memory 210 may be random access memory (RAM), read-only memory (ROM), a hard disk drive, flash memory, or other suitable memory for storing modules 220, 230. Modules 220 and 230 are, for example, computer-readable instructions that, when executed by one or more processors 110, cause the one or more processors 110 to perform various functions disclosed herein.

[0020] 2 and as described above, autonomous driving feedback system 170 can be connected to and communicate with sensor system 120 and one or more autonomous driving modules 160. More specifically, autonomous driving feedback system 170 at least indirectly controls the operation of vehicle 100 by communicating outputs related to its feedback to one or more downstream autonomous driving modules 160, which ultimately control the steering, acceleration, braking, etc. of vehicle 100 (see vehicle system 140 in FIG. 1).

[0021] The autonomous driving feedback system 170 stores the reference path 250 and the lateral deviation model data 260 in the database 240. The lateral deviation model data 260 includes various types of data and intermediate results of calculations related to generating the repeated controlled lateral deviations from the reference path described above. As also described above, the autonomous driving feedback system 170 can access the high-definition map data 116 in connection with localization and path planning (e.g., determining the reference path 250 and planning a path augmented by the repeated controlled lateral deviations).

[0022] The path planning module 220 generally includes instructions that, when executed by one or more processors 110, cause the one or more processors 110 to determine a reference path for the autonomous vehicle along a road segment. As described above, in some embodiments, the reference path, at least for road segments that are straight or nearly straight, coincides at least approximately with an imaginary line extending longitudinally along the centerline of the lane in which the vehicle is traveling. Note that in some embodiments, if the road includes a curve, the reference path may not necessarily follow the exact longitudinal centerline of the lane for portions of the road segment that include a curve. As described herein, the reference path may also be referred to as the “normal” or “ideal” path or trajectory that the vehicle should follow over the road segment. As used herein, the terms “path” and “trajectory” are used interchangeably. The concept of a reference path is illustrated in FIG. 3.

[0023] FIG. 3 is a diagram illustrating controlled, repeated lateral deviations 340 from a reference path 335, in accordance with an exemplary embodiment of the present invention. In the scene depicted in FIG. 3, the vehicle 100 is traveling in a left lane 320 of a road 310. The lane 320 is defined by a left lane marking and a right lane marking 330. Based on environmental sensors (e.g., camera 126, LIDAR sensor 124) and / or map data 116, the path planning module 220 analyzes an upcoming road segment along the road 310 and, in this example, determines that the road segment is at least approximately straight. The path planning module 220 also determines a reference path 335 along the road segment, as shown in FIG. 3. FIG. 3 is further described below in connection with the feedback generation module 230.

[0024] The feedback generation module 230 generally includes instructions that, when executed by the one or more processors 110, cause the one or more processors 110 to steer the vehicle 100 along a path that includes repeated controlled lateral deviations 340 from a reference path 335 along a road segment to provide feedback to an occupant of the vehicle 100. The visual and kinesthetic (in combination with the vestibular system) feedback provided by the repeated controlled lateral deviations 340 indicates to the occupant of the vehicle 100 that the vehicle 100 is in autonomous driving mode and that the autonomous driving mode is operating correctly as described above.

[0025] Referring again to Figure 3, in this scenario, the feedback generation module 230 selects repeated controlled lateral deviations 340a (to the right), followed by repeated controlled lateral deviations 340b (to the left) over the depicted portion of the road segment of road 310. Although not shown in Figure 3, this pattern of repeated controlled lateral deviations 340 (i.e., alternating deviations in one direction followed by deviations in the other direction) from the reference path 335 extends (repeated) along the entire length of the road segment. The feedback generation module 230, via one or more autonomous driving modules 160, executes the intentional repeated controlled lateral deviations 340 from the reference path 335 and generates feedback to the occupants of the vehicle, as described above.

[0026] In some embodiments, the controlled lateral repetitive deviation 340 is modeled in terms of frequency of occurrence (i.e., how often the heading of the vehicle 100 is adjusted) and magnitude (the magnitude of the deviation). The frequency of occurrence and magnitude depend on factors such as how straight or curved a particular section of road 310 is, the width of the lane 320, the relative width of the particular vehicle 100, the current speed of the vehicle 100 while traveling, and traffic density (e.g., urban or rural environment). For example, in a highway driving situation, at higher speeds (e.g., 70 mph) along a nearly straight road segment, the controlled lateral repetitive deviation 340 may occur as often as once every two seconds, with a magnitude selected based on the width of the lane 320 and the relative width of the vehicle 100. In some embodiments, the frequency of occurrence is randomly varied within a predetermined range relative to the average frequency of occurrence. In the example above, the frequency of occurrence is randomly or pseudo-randomly varied between 1.7 and 2.3 seconds for an average frequency of two seconds.

[0027] In general, the magnitude of the repeated controlled lateral deviation 340 is selected to be large enough to provide the desired feedback to the vehicle occupants without bringing the vehicle 100 too close to any of the lane boundaries 330. The magnitude of the controlled lateral deviation 340, in some embodiments, is expressed in terms of an instantaneous change or adjustment in steering angle relative to the reference path 335. For example, in the scene depicted in FIG. 3, the feedback generation module 230 might specify a brief change in steering angle to the right by a predetermined angle to cause the vehicle 100 to follow the path shown by controlled lateral deviation 340a in FIG. 3. Similarly, for controlled lateral deviation 340b, the feedback generation module 230 might specify a brief change in steering angle to the left by a predetermined angle to cause the vehicle 100 to follow the path shown by controlled lateral deviation 340b in FIG. 3.

[0028] The frequency and magnitude of the controlled lateral deviation 340 varies depending on several factors, as discussed above. One factor discussed above is how straight or curved the identified road segment is. In connection with various embodiments described herein, the concept of applying the controlled lateral deviation 340 to the reference path 335 is primarily applicable to straight or nearly straight road segments. In one embodiment, the feedback generation module 230 considers a road segment to be nearly straight based on data from the sensor system 120 and / or the map data 116 if no steering / heading adjustment of the vehicle 100 is required or for a minimum time greater than or equal to a threshold number of seconds.

[0029] On curved portions of the road 310, the feedback generation module 230 adjusts the frequency and magnitude of the controlled lateral deviations 340 to be small or even zero, in some embodiments. This is because curved segments of the road 310 already provide sufficient visual and kinesthetic (combined with the vestibular system) feedback to the occupants of the vehicle. The type of feedback provided by the controlled lateral deviations 340 described herein is useful to the occupants of the vehicle along straight or nearly straight road segments. Generalizing, in some embodiments, the controlled lateral deviations 340 are larger when the road segment is straight (or nearly straight) than when the road segment is curved. As mentioned above, the frequency and magnitude of the controlled lateral deviations 340 can be adjusted to zero or near-zero along curved road segments in some embodiments.

[0030] In some embodiments, the feedback generation module 230 includes instructions that allow a user (e.g., an operator or other occupant) of the vehicle 100 to configure the frequency and / or magnitude of the controlled lateral repeatable deviation 340 within predetermined ranges. This can be accomplished via a suitable user interface (e.g., an integrated touchscreen-based user interface or a smartphone app) that communicates with the feedback generation module 230.

[0031] In some embodiments, the feedback generation module 230 at least partially selects a controlled recurring lateral deviation 340 (e.g., right or left initiation, its frequency of occurrence, its magnitude, etc.) to avoid a road obstacle, as shown in FIG.

[0032] 4 is a diagram illustrating the selection of repeated controlled lateral deviations 340 relative to a reference path 335 that avoids a road obstacle 410, according to an exemplary embodiment of the present invention. In the scene depicted in FIG. 4, the feedback generation module 230 selects controlled lateral deviation 340a followed by controlled lateral deviation 340b as a way to avoid a pothole 410 (an example of a road obstacle).

[0033] In some embodiments, the feedback generation module 230 selects a direction in which to initiate repeated controlled lateral deviations 340 to prevent the vehicle 100 from coming closer to objects detected in an area adjacent to the lane in which the vehicle 100 is traveling, as shown in FIG.

[0034] FIG. 5 illustrates the selection of repeated controlled lateral deviations 340 relative to a reference path 335 to prevent the autonomous vehicle from passing an object 510 detected near, but outside, a lane boundary 330, in accordance with an exemplary embodiment of the present invention. In the scene depicted in FIG. 5 , the feedback generation module 230 selects the controlled lateral deviations 340a and 340b in a manner that prevents the autonomous vehicle 100 from passing too close to the object 510 (e.g., a parked vehicle, a foreign object on the side of the road, construction equipment, a construction barrier, a pedestrian, etc.). In this example, the feedback generation module 230 avoids the pass by first selecting a controlled lateral deviation 340a to the right and then selecting a controlled lateral deviation 340b to the left. This selection of initial directions can prevent the autonomous vehicle 100 from passing the object 510 unnecessarily or undesirably close.

[0035] In some embodiments, the feedback generation module 230 derives the controlled lateral deviations 340 from the observed driving patterns of a particular human driver (e.g., the owner or primary driver of the vehicle 100) during periods when the vehicle 100 is in a manual driving mode in which the human driver controls the vehicle. In some embodiments, the controlled lateral deviations 340 are obtained from a model developed using machine learning techniques. In other embodiments, the driving patterns of the particular human driver are analyzed (e.g., through statistical analysis) to infer, for example, frequency of occurrence and magnitude parameters that enable the feedback generation module 230 to mimic the naturally occurring lateral deviations of the particular driver.

[0036] In some embodiments, the steering wheel of the vehicle 100 is coupled to the controlled lateral deflection 340 generated by the feedback generation module 230 such that the steering wheel moves repeatedly in synchronization with the steering adjustments that generate the controlled lateral deflection 340. In other embodiments, the steering wheel of the vehicle 100 is decoupled from the controlled lateral deflection 340. That is, the steering wheel remains stationary (e.g., in a reference position corresponding to straight ahead driving if the vehicle 100 were being manually driven) when the controlled lateral deflection 340 occurs.

[0037] In some embodiments, the feedback generation module 230 combines controlled (deliberate and constrained) variations in the vehicle's 100 speed with repeated controlled lateral deviations 340 from the reference path 335 to add another dimension to the feedback provided to the vehicle's occupants. This technique adds subtle longitudinal and lateral G-forces to the visual feedback. Human drivers without cruise control do not drive at a perfectly constant speed, even on an uncongested straight highway, and the controlled speed variations mimic those naturally occurring speed variations.

[0038] 6 is a flowchart of a method 600 for controlling the operation of autonomous vehicle 100, in accordance with an exemplary embodiment of the invention. Method 600 is discussed in terms of autonomous driving feedback system 170 in FIG. 2. Although method 600 is discussed in combination with autonomous driving feedback system 170, it should be understood that method 600 is not limited to being implemented within autonomous driving feedback system 170; instead, autonomous driving feedback system 170 may be an example of a system that implements method 600.

[0039] In block 610, the path planning module 220 determines a reference path 335 for the vehicle 100 along the road segment. As mentioned above, in some embodiments, the reference path 335 coincides, at least approximately, with an imaginary line extending longitudinally along the centerline of the lane 320 in which the vehicle is traveling. Also, as mentioned above, the reference path 335 may be referred to as a "normal" or "ideal" path that the vehicle 100 should follow across the road segment.

[0040] In block 620, the feedback generation module 230 steers the vehicle 100 along a path that includes repeated controlled lateral deviations 340 from the reference path 335 along the road segment to provide feedback to the occupants of the vehicle 100. As described above, the feedback indicates to the occupants of the vehicle 100 that the vehicle 100 is in autonomous driving mode and that the autonomous driving mode is operating correctly. As described above, in some embodiments, the repeated controlled lateral deviations 340 are modeled in terms of frequency of occurrence and magnitude. In some embodiments, a user can configure the frequency of occurrence and / or magnitude within predetermined ranges.

[0041] Also, as mentioned above, in some embodiments, the controlled lateral deviation 340 is greater when the road segment is straight (or nearly straight) than when the road segment is curved. As mentioned above, the frequency and magnitude of the controlled lateral deviation 340 can be adjusted to zero or near zero along curved road segments in some embodiments. In some embodiments, the feedback generation module 230 randomly varies the frequency of the controlled lateral deviation 340 within a predetermined range.

[0042] Also, as described above, in some embodiments, the feedback generation module 230 selects the repeated controlled lateral deviation 340 (e.g., whether to initiate to the right or left, how often it occurs, its magnitude, etc.) at least in part to avoid a road obstacle 410 (e.g., a pothole). Also, in some embodiments, the feedback generation module 230 selects the direction in which the repeated controlled lateral deviation 340 begins to avoid the vehicle 100 coming closer to an object 510 detected in an area adjacent to the lane 320 in which the vehicle 100 is traveling.

[0043] Also, as described above, in some embodiments, the feedback generation module 230 derives the controlled repeated lateral deviation 340 from the observed driving patterns of a particular human driver when the vehicle 100 is in a manual driving mode in which the human driver controls the vehicle. This can be done, for example, by machine learning or statistical analysis, depending on the embodiment.

[0044] Also, as described above, in some embodiments, the steering wheel of the vehicle 100 is coupled to the repeated controlled lateral deviation 340 generated by the feedback generation module 230 such that the steering wheel repeatedly moves in accordance with the repeated controlled lateral deviation 340. In other embodiments, the steering wheel of the vehicle 100 is decoupled from the repeated controlled lateral deviation 340, i.e., the steering wheel remains stationary when the repeated controlled lateral deviation 340 occurs.

[0045] Also, as described above, in some embodiments, the feedback generation module 230 further includes instructions to combine the controlled variations in the speed of the vehicle 100 with the controlled repeated lateral deviations 340 to provide an additional type of kinesthetic feedback (in combination with the vestibular system) to one or more occupants of the vehicle 100.

[0046] 1 will now be described in greater detail as an exemplary vehicle environment in which the systems and methods disclosed herein may be implemented. In some embodiments, vehicle 100 is configured to selectively switch between an autonomous mode, one or more semi-autonomous operating modes, and / or a manual mode. Such switching, also referred to as a handoff when transitioning to a manual mode, may be performed in any suitable manner now known or later developed. "Manual mode" means that all or most of the navigation and / or steering of the vehicle is performed according to inputs received from a user (e.g., a human driver / operator).

[0047] In one or more embodiments, vehicle 100 is an autonomous vehicle. As used herein, "autonomous vehicle" refers to a vehicle operating in an autonomous mode. "Autonomous mode" refers to using one or more computing devices to control the vehicle with minimal or no input from a human driver / operator to navigate and / or steer the vehicle along a driving route. In one embodiment, vehicle 100 is configured with one or more semi-autonomous driving modes in which one or more computing devices perform a portion of the navigation and / or steering of the vehicle along a driving route, and an operator (i.e., a driver) of the vehicle provides input to the vehicle to perform a portion of the navigation and / or steering of vehicle 100 along a driving route. Thus, in one or more embodiments, vehicle 100 operates autonomously according to a particular defined level of autonomy.

[0048] The vehicle 100 includes one or more processors 110. In one or more configurations, the one or more processors 110 are the main processors of the vehicle 100. For example, the one or more processors 110 are electronic control units (ECUs). The vehicle 100 includes one or more data stores 115 for storing one or more types of data. The one or more data stores 115 include volatile and / or non-volatile memory. Examples of suitable data stores 115 include RAM, flash memory, ROM, PROM (Programmable Read-Only Memory), EPROM, EEPROM (Electrically Erasable Programmable Read-Only Memory), registers, magnetic disks, optical disks, hard disks, or other suitable storage media, or any combination thereof. The one or more data stores 115 may be one or more components of the one or more processors 110, or the one or more data stores 115 may be operably connected to the one or more processors 110 for use by them. As used throughout this specification, the term "operably connected" includes direct or indirect connections, including connections that do not involve direct physical contact.

[0049] In one or more configurations, the one or more data stores 115 include map data 116. The map data 116 includes maps of one or more geographic regions. In some configurations, the map data 116 includes information or data regarding roads, traffic control devices, road signs, structures, features, and / or landmarks in the one or more geographic regions. In one or more configurations, the map data 116 includes one or more topographic maps 117. The one or more topographic maps 117 include information regarding the ground, terrain, roads, surfaces, and / or other features of the one or more geographic regions. In one or more configurations, the map data 116 includes one or more static obstacle maps 118. The one or more static obstacle maps 118 include information regarding one or more static obstacles located within the one or more geographic regions.

[0050] The one or more data stores 115 include sensor data 119. In this context, "sensor data" means any information related to sensors equipped on the vehicle, including capabilities and other information related to such sensors. As described below, the vehicle 100 includes a sensor system 120. The sensor data 119 is associated with one or more sensors of the sensor system 120. As an example, in one or more configurations, the sensor data 119 includes information related to one or more LIDAR sensors 124 of the sensor system 120. As mentioned above, in some embodiments, the vehicle 100 receives sensor data from other connected vehicles, from devices associated with ORUs, or both.

[0051] As described above, vehicle 100 includes sensor system 120. Sensor system 120 includes one or more sensors. A "sensor" refers to any device, component, and / or system that can detect and / or sense something. The one or more sensors are configured to 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 being immediate enough for a particular process or decision to be made, or that allows a processor to keep up with some external process.

[0052] In configurations where sensor system 120 includes multiple sensors, the sensors may function independently of one another. Alternatively, two or more sensors may function in combination with one another. In such cases, the two or more sensors form a sensor network. Sensor system 120 and / or one or more sensors may be operatively connected to one or more processors 110, one or more data stores 115, and / or other elements of vehicle 100 (including any of the elements shown in FIG. 1).

[0053] The sensor system 120 includes any suitable type of sensor. Various examples of different types of sensors are described herein. However, it should be understood that implementations are not limited to the particular sensors described. The sensor system 120 includes one or more vehicle sensors 121. The vehicle sensors 121 detect, determine, and / or sense information about the vehicle 100 itself, including the operating conditions of various vehicle components and systems.

[0054] In one or more configurations, the vehicle sensors 121 are configured to detect and / or sense changes in the position and / or orientation of the vehicle 100, such as based on inertial acceleration. In one or more configurations, the vehicle sensors 121 include one or more accelerometers, one or more gyroscopes, an inertial measurement unit (IMU), a dead reckoning system, a global navigation satellite system (GNSS), a global positioning system (GPS), a navigation system 147, and / or other suitable sensors. The vehicle sensors 121 are configured to detect and / or sense one or more characteristics of the vehicle 100. In one or more configurations, the vehicle sensors 121 include a speedometer for determining the current speed of the vehicle 100.

[0055] Alternatively or additionally, the sensor system 120 includes one or more environmental sensors 122 configured to acquire and / or sense driving environment data. "Driving environment data" includes any data or information regarding the external environment in which the driving vehicle is located, or one or more portions thereof. For example, the one or more environmental sensors 122 are configured to detect, quantify, and / or sense obstacles in at least a portion of the external environment of the driving vehicle 100, and / or information / data regarding such obstacles. The one or more environmental sensors 122 may be configured to detect, measure, quantify, and / or sense other objects in at least a portion of the external environment of the driving vehicle 100, such as nearby vehicles, lane markers, signs, traffic signals, traffic signs, lanes, crosswalks, curbs proximate to the driving vehicle 100, off-road objects, etc.

[0056] Various example sensors of sensor system 120 are described herein. Exemplary sensors may be part of one or more environmental sensors 122 and / or one or more vehicle sensors 121. Additionally, sensor system 120 includes an operator sensor that functions to track or monitor aspects related to the driver / operator of vehicle 100. However, it should be understood that implementations are not limited to the particular sensors described. By way of example, in one or more configurations, sensor system 120 includes one or more radar sensors 123, one or more LIDAR sensors 124, one or more sonar sensors 125, and / or one or more cameras 126.

[0057] The vehicle 100 further includes a communication system 130. The communication system 130 includes one or more components configured to facilitate communication between the vehicle 100 and one or more communication sources. As used herein, a communication source refers to a person or device with which the vehicle 100 can communicate, such as an external network, a computing device, an operator or a passenger of the vehicle 100, etc. As part of the communication system 130, the vehicle 100 includes an input system 131. An "input system" includes any device, component, system, element, or configuration, or group thereof, that allows information / data to be input into a machine. In one or more examples, the input system 131 receives input from a passenger (e.g., a driver or passenger) of the vehicle. The vehicle 100 includes an output system 132. An "output system" includes any device, component, or configuration, or group thereof, that allows information / data to be presented to one or more communication sources (e.g., a person, a passenger of the vehicle, etc.). The communication system 130 further includes certain elements that are part of or can interact with the input system 131 or the output system 132, such as one or more display devices 133 and one or more audio devices 134 (e.g., speakers and microphones).

[0058] The vehicle 100 includes one or more vehicle systems 140. Various examples of the one or more vehicle systems 140 are shown in FIG. 1 . However, the vehicle 100 may include more, fewer, or different vehicle systems. While certain vehicle systems are defined separately, it should be understood that each or any of the systems or portions thereof may be otherwise combined or separated via hardware and / or software within the vehicle 100. The vehicle 100 includes 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 includes one or more now known or later developed devices, components, and / or combinations thereof.

[0059] The one or more processors 110 and / or the one or more autonomous navigation modules 160 are operably connected to communicate with the various vehicle systems 140 and / or their individual components. For example, returning to FIG. 1 , the one or more processors 110 and / or the one or more autonomous navigation modules 160 communicate to send and / or receive information from the various vehicle systems 140 to control the movement, speed, steering, heading, direction, etc. of the vehicle 100. The one or more processors 110 and / or the one or more autonomous navigation modules 160 may control some or all of these vehicle systems 140 and, therefore, may be partially or fully autonomous.

[0060] Vehicle 100 includes at least some of the modules described herein. The modules are implemented as computer-readable program code that, when executed by processor 110, performs one or more of the various operations described herein. Processor 110 is a device, such as a CPU, capable of receiving and executing one or more threads of instructions to perform tasks. One or more modules may be components of one or more processors 110, or one or more modules may execute on and / or be distributed among other processing systems to which one or more processors 110 are operatively connected. A module contains instructions (e.g., program logic) executable by one or more processors 110. Alternatively, or additionally, one or more data stores 115 may contain such instructions.

[0061] In one or more configurations, one or more of the modules described herein include artificial intelligence or computational intelligence elements, such as neural networks, fuzzy logic, or other machine learning algorithms. Further, in one or more configurations, one or more modules are distributed among multiple modules described herein. In one or more configurations, two or more of the modules described herein are combined into a single module.

[0062] In some implementations, the vehicle 100 includes one or more autonomous navigation modules 160. The one or more autonomous navigation modules 160 are configured to receive data from the sensor system 120 and / or any other type of system capable of capturing information related to the vehicle 100 and / or the vehicle's external environment. In one or more configurations, the one or more autonomous navigation modules 160 use such data to generate one or more driving scene models. The one or more autonomous navigation modules 160 determine the position and velocity of the vehicle 100. The one or more autonomous navigation modules 160 determine the location of obstacles or other environmental features, including traffic signs, trees, shrubs, nearby vehicles, pedestrians, etc.

[0063] The one or more autonomous navigation modules 160 are configured to determine one or more driving paths, a current autonomous navigation maneuver for the vehicle 100, a future autonomous navigation maneuver, and / or a modification of the current autonomous navigation maneuver based on data acquired by the sensor system 120, a driving scene model, and / or data from any other suitable source. A "driving maneuver" refers to one or more actions that affect the movement of the vehicle. Examples of driving maneuvers include accelerating, decelerating, braking, turning, moving the vehicle 100 laterally, changing lanes of travel, merging into lanes of travel, and / or driving in the opposite direction, to name a few possibilities. The one or more autonomous navigation modules 160 are configured to implement the determined driving maneuvers. The one or more autonomous navigation modules 160 directly or indirectly cause such autonomous navigation maneuvers to be implemented. As used herein, "cause" or "causing" refers to causing, commanding, directing, and / or enabling an event or action, or at least being in a state in which such an event or action can occur, either directly or indirectly. One or more autonomous driving modules 160 are configured to perform various vehicle functions and / or transmit data to, receive data from, interact with, and / or control vehicle 100 or one or more of its systems (e.g., one or more vehicle systems 140). The indicated functions and methods will become more apparent with further discussion of the figures.

[0064] Detailed embodiments are disclosed herein. However, it should be understood that the disclosed embodiments are intended as examples only. Accordingly, the specific structural and functional details disclosed herein are not intended to be limiting, but merely as a basis for the claims and as a representative basis for teaching those skilled in the art how to variously employ the aspects of the present specification in substantially any appropriately detailed structure. Furthermore, the terms and phrases used herein are not intended to be limiting, but rather to provide an understandable description of possible embodiments. While various embodiments are shown in FIGS. 1-6, the embodiments are not limited to the illustrated structures or applications.

[0065] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various implementations. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of code, constituting 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 noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved.

[0066] The systems, components, and / or methods described above can be implemented in hardware or a combination of hardware and software, either centralized in one processing system or in a distributed fashion with different elements spread across multiple interconnected processing systems. Any type of processing system or other device adapted for carrying out the methods described herein is suitable. A typical combination of hardware and software is a processing system having computer-usable program code that, when loaded and executed, controls the processing system to perform the methods described herein. The systems, components, and / or methods can also be embodied in a computer-readable storage device, such as a machine-readable computer program product or other data program storage device, containing a program of instructions executable by the machine to perform the methods and methods described herein. These elements can also be embodied in an application product that contains all functionality enabling the implementation of the methods described herein and that, when loaded into a processing system, can execute these methods.

[0067] Furthermore, the configurations described herein may take the form of a computer program product embodied in one or more computer-readable medium(s) having computer-readable program code embodied or embedded therein as stored thereon. Any combination of one or more computer-readable medium(s) may be utilized. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The phrase "computer-readable storage medium" refers to 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, apparatus, or device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer-readable storage media include an electrical connection having one or more wires, a portable computer disk, a hard disk drive (HDD), a solid-state drive (SSD), RAM, ROM, EPROM, or flash memory, optical fiber, a portable compact disk read-only memory (CD-ROM), a digital multifunction disk (DVD), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. As used herein, a computer-readable storage medium is any tangible medium that can store a program for use in connection with an instruction execution system, apparatus, or device.

[0068] The program code embodied on the computer readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wired, fiber optic, cable, RF, or the like, or any suitable combination of the foregoing. The computer program code for performing operations for aspects of the present arrangements may be implemented in Java. TMThe program code may be written in any combination of one or more programming languages, including conventional procedural programming languages ​​such as , Smalltalk, C++ or similar object-oriented programming languages, and "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, part of the user's computer as a stand-alone software package, partly on the user's computer, partly on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a LAN or WAN, and the connection may be to an external computer (e.g., via the Internet using an Internet Service Provider).

[0069] In the foregoing description, certain specific details have been outlined to provide a thorough understanding of various embodiments. However, those skilled in the art will understand that the present invention may be practiced without these details. In other instances, well-known structures have not been shown or described in detail to avoid unnecessarily obscuring the description of the embodiments. Unless otherwise required by context, throughout this specification and the claims that follow, the word "comprises" and variations thereof, such as "comprises" and "comprising," are intended to be interpreted in an open, inclusive sense, i.e., as meaning "including but not limited to." Additionally, headings provided herein are for convenience only and do not interpret the scope or meaning of the claimed invention.

[0070] References throughout this specification to "one or more implementations" or "implementations" mean that a particular feature, structure, or characteristic described in connection with an implementation is included in at least one or more implementations. Thus, the appearances of the phrase "in one or more implementations" or "in an implementation" in various places throughout this specification are not necessarily all referring to the same implementations. Moreover, particular features, structures, or characteristics may be combined in any suitable manner in one or more implementations. Also, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the content clearly dictates otherwise. It should also be noted that the term "or" is generally employed in its sense to include "and / or" unless the content clearly dictates otherwise.

[0071] Headings (such as "Background" and "Summary") and subheadings used herein are intended solely for general organization of topics within the disclosure and are not intended to limit the disclosure of the technology or any aspects thereof. The description of multiple implementations having described features is not intended to exclude other implementations having additional features or other implementations incorporating different combinations of the described features. As used herein, the terms "comprises" and "includes" and variations thereof are intended to be open-ended, such that the recitation of consecutive items or lists does not exclude other similar items that are also useful in the devices and methods of the technology. Similarly, the terms "can" and "may" and variations thereof are intended to be open-ended, such that the recitation of an implementation that can or may comprise certain elements or features does not exclude other implementations of the technology that do not include those elements or features.

[0072] The broad teachings of the present disclosure can be embodied in a variety of forms. Thus, while the present disclosure includes specific examples, the true scope of the present disclosure should not be so limited, as other variations will become apparent to those skilled in the art upon review of the specification and the following claims. Reference herein to an aspect, or various aspects, means that a particular feature, structure, or characteristic described in connection with an implementation or a particular system is included in at least one or more implementations or aspects. The appearance of the phrase "in one aspect" (or variations thereof) does not necessarily refer to the same aspect or implementation. It should also be understood that the various method steps discussed herein need not be performed in the same order as depicted, and that not every method step is required in every aspect or implementation.

[0073] Generally, as used herein, a "module" includes a routine, program, object, component, data structure, etc. that performs a particular task or implements a particular data type. In a further aspect, memory generally stores the designated module. The memory associated with a module may be a buffer or cache embedded within a processor, RAM, ROM, flash memory, or other suitable electronic storage medium. In still further aspects, modules contemplated by the present disclosure are implemented as an application-specific integrated circuit (ASIC), a hardware component of a system-on-chip (SoC), a programmable logic array (PLA), or another suitable hardware component embedded with a defined configuration set (e.g., instructions) to perform the disclosed functions.

[0074] As used herein, the terms "a" and "an" are defined as one or more than one. As used herein, the term "plurality" is defined as two or more than two. As used herein, the term "another" is defined as at least a second or more. As used herein, the terms "comprises" and / or "has" are defined as including (i.e., open language). As used herein, the phrase "and at least one of" refers to and encompasses any possible combination of one or more of the associated listed items. As an example, the phrase "at least one of A, B, and C" includes A only, B only, C only, or any combination thereof (e.g., AB, AC, BC, or ABC).

[0075] The foregoing description of the embodiments has been provided for purposes of illustration and description. It is not intended to be exhaustive or to limit the present disclosure. Individual elements or features of a particular implementation are generally not limited to that particular implementation, but, where applicable, are interchangeable and can be used in selected implementations even if not specifically shown or described. The same can also be varied in many ways. Such variations should not be considered a departure from the present disclosure, and all such modifications are intended to be included within the scope of the present disclosure.

[0076] While the foregoing description is directed to implementations of the disclosed devices, systems, and methods, other and further implementations of the disclosed devices, systems, and methods may be devised without departing from the basic scope thereof, which scope is determined by the claims that follow.

Claims

1. A system for controlling an autonomous vehicle, the system comprising: one or more processors; a memory communicatively coupled to the one or more processors, the memory comprising: a path planning module comprising instructions that, when executed by the one or more processors, cause the one or more processors to determine a reference path for the autonomous vehicle along a road segment; a feedback generation module that, when executed by the one or more processors, causes the one or more processors to steer the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment, and to provide feedback to an occupant of the autonomous vehicle that indicates the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly; 1. A system for controlling an autonomous vehicle, wherein the feedback generation module includes instructions to make the repeated controlled lateral deviation larger when the road segment is straight than when the road segment is curved.

2. A system for controlling an autonomous vehicle, the system comprising: one or more processors; a memory communicatively coupled to the one or more processors, the memory comprising: a path planning module comprising instructions that, when executed by the one or more processors, cause the one or more processors to determine a reference path for the autonomous vehicle along a road segment; a feedback generation module that, when executed by the one or more processors, causes the one or more processors to steer the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment, and to provide feedback to an occupant of the autonomous vehicle that indicates the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly; 1. A system for controlling an autonomous vehicle, wherein the feedback generation module includes instructions to direct the controlled repeatable lateral deviation from an observed driving pattern of a particular human driver.

3. A system for controlling an autonomous vehicle, the system comprising: one or more processors; a memory communicatively coupled to the one or more processors, the memory comprising: a path planning module comprising instructions that, when executed by the one or more processors, cause the one or more processors to determine a reference path for the autonomous vehicle along a road segment; a feedback generation module that, when executed by the one or more processors, causes the one or more processors to steer the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment, and to provide feedback to an occupant of the autonomous vehicle that indicates the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly; The system for controlling an autonomous vehicle, wherein the feedback generation module includes instructions for selecting a direction at which the repetitive controlled lateral deviation is initiated that avoids bringing the autonomous vehicle closer to an object detected in an area adjacent to a lane in which the autonomous vehicle is traveling.

4. A system for controlling an autonomous vehicle, the system comprising: one or more processors; a memory communicatively coupled to the one or more processors, the memory comprising: a path planning module comprising instructions that, when executed by the one or more processors, cause the one or more processors to determine a reference path for the autonomous vehicle along a road segment; a feedback generation module that, when executed by the one or more processors, causes the one or more processors to steer the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment, and to provide feedback to an occupant of the autonomous vehicle that indicates the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly; 1. A system for controlling an autonomous vehicle, wherein the feedback generation module includes instructions for randomly varying the frequency of occurrence of the controlled lateral deviation within a predetermined range.

5. A system for controlling an autonomous vehicle, comprising: one or more processors; a memory communicatively coupled to the one or more processors, the memory comprising: a path planning module comprising instructions that, when executed by the one or more processors, cause the one or more processors to determine a reference path for the autonomous vehicle along a road segment; a feedback generation module that, when executed by the one or more processors, causes the one or more processors to steer the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment, and to provide feedback to an occupant of the autonomous vehicle that indicates the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly; 1. A system for controlling an autonomous vehicle, wherein the feedback generation module includes instructions for allowing a user to set the frequency and magnitude of the repeated controlled lateral deviation within predetermined ranges.

6. The system of any of claims 1 to 5, wherein the feedback generation module includes instructions for selecting the controlled repetitive lateral deviation to, at least in part, avoid road obstacles.

7. The feedback generation module: connecting a steering wheel of the autonomous vehicle to move in accordance with the controlled repeatable lateral deflection; decoupling the steering wheel from the controlled lateral repeatable deflection; The system according to any one of claims 1 to 5, further comprising instructions for performing any one of the following:

8. 6. The system of claim 1, wherein the feedback generation module includes further instructions for combining controlled variations in the autonomous vehicle's speed with the controlled repeatable lateral deviation.

9. A non-transitory computer-readable medium for controlling an autonomous vehicle, which, when executed by one or more processors, causes the one or more processors to: determining a reference path for the autonomous vehicle along a road segment; steering the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment to provide feedback to an occupant of the autonomous vehicle indicating that the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly; and and storing an instruction to perform the The non-transitory computer-readable medium, wherein the instructions cause the controlled lateral repetitive deviation to be greater when the road segment is straight than when the road segment is curved.

10. A method for controlling an autonomous vehicle, comprising: determining a reference path for the autonomous vehicle along a road segment; steering the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment to provide feedback to an occupant of the autonomous vehicle indicating that the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly; Equipped with A method wherein the controlled repeated lateral deviation is greater when the road segment is straight than when the road segment is curved.

11. A method for controlling an autonomous vehicle, comprising: determining a reference path for the autonomous vehicle along a road segment; steering the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment to provide feedback to an occupant of the autonomous vehicle indicating that the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly; Equipped with A method wherein the controlled repeatable lateral deflection is derived from observed driving patterns of a particular human driver.

12. A method for controlling an autonomous vehicle, comprising: determining a reference path for the autonomous vehicle along a road segment; steering the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment to provide feedback to an occupant of the autonomous vehicle indicating that the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly; Equipped with wherein the direction in which the repetitive controlled lateral deviation is initiated is selected to avoid bringing the autonomous vehicle closer to an object detected in an area adjacent to a lane in which the autonomous vehicle is traveling.

13. A method for controlling an autonomous vehicle, comprising: determining a reference path for the autonomous vehicle along a road segment; steering the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment to provide feedback to an occupant of the autonomous vehicle indicating that the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly; Equipped with Randomly varying the frequency of occurrence of the controlled repeatable lateral deflection within a predetermined range.

14. A method for controlling an autonomous vehicle, comprising: determining a reference path for the autonomous vehicle along a road segment; steering the autonomous vehicle along a path that includes repeated controlled lateral deviations from the reference path along the road segment to provide feedback to an occupant of the autonomous vehicle indicating that the autonomous vehicle is in an autonomous driving mode and that the autonomous driving mode is operating correctly; Equipped with A method in which a user can set the frequency and magnitude of the controlled repeatable lateral deviation within predetermined ranges.

15. A method according to any one of claims 10 to 14, wherein the controlled repetitive lateral deflection is selected at least in part to avoid road obstacles.

16. 15. The method of any of claims 10 to 14, wherein a steering wheel of the autonomous vehicle is connected to move in accordance with the controlled repetitive lateral deviation and is decoupled from the controlled repetitive lateral deviation.

17. The method of any of claims 10 to 14, further comprising combining the controlled variation in the autonomous vehicle's speed with the controlled repeatable lateral deviation.

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