Map-based prediction and mitigation of performance limitations for autonomous vehicles

Abstract

A method and system for controlling a vehicle. The system includes a positioning system, a memory storing a digital map, and an electronic processor. The electronic processor is configured to receive a current position of the vehicle from the positioning system and determine a future driving segment of the vehicle based on the current position of the vehicle. The electronic processor is also configured to determine at least one performance limit based on the future driving segment of the vehicle and modify the driving behavior of the vehicle based on the determined at least one performance limit.

Description

Technical Field

[0001] Embodiments relate to map-based prediction and mitigation actions when sensor limitations and challenging driving situations arise in autonomous vehicles. Background Art

[0002] Behavior planning for autonomous vehicles already includes handling uncertainty about the state and dynamics of the driving environment, such as aleatory or stochastic uncertainty in sensor measurements (or loss of one or more sensor functionality) or epistemic uncertainty associated with models used in algorithms used to create a model of the vehicle's driving environment. If the uncertainty in these measurements reaches a certain level, actions are taken to ensure the safety of the vehicle by reducing the uncertainty or preventing hazards associated with the uncertainty.

[0003] Additionally, challenging driving situations may arise that increase the uncertainty of sensor measurements or present complex decisions for the autonomous vehicle. For example, the vehicle may be driving in an area with a high vehicle accident statistics, or approaching a large gathering place that may have high pedestrian traffic. Just as with human drivers, these driving situations may present more challenging driving situations for the autonomous vehicle. A solution is needed to help control the autonomous vehicle when the sensor data is uncertain or the vehicle is presented with a challenging driving situation. Summary of the invention

[0004] Thus, among other purposes, one object of some embodiments is to control a vehicle.

[0005] One embodiment provides a system for controlling a vehicle. The system includes a positioning system, a memory storing a digital map, and an electronic processor. The electronic processor is configured to receive a current position of the vehicle from the positioning system, and determine a future driving segment of the vehicle based on the current position of the vehicle. The electronic processor is also configured to determine at least one performance limit based on the future driving segment of the vehicle, and modify the driving behavior of the vehicle based on the determined at least one performance limit.

[0006] Another embodiment provides a method for controlling a vehicle. The method includes receiving, with an electronic processor, a current position of the vehicle from a positioning system, and determining, with the electronic processor, a future driving segment of the vehicle based on the current position of the vehicle. The method also includes determining, with the electronic processor, at least one performance limit based on the future driving segment of the vehicle, and modifying, with the electronic processor, a driving behavior of the vehicle based on the determined at least one performance limit.

[0007] Another embodiment provides a non-transitory computer-readable medium containing instructions, which when executed by an electronic processor are configured to perform a set of functions, the set of functions including receiving a current position of a vehicle from a positioning system, and determining a future driving segment of the vehicle based on the current position of the vehicle. The set of functions also includes determining at least one performance limit based on the future driving segment of the vehicle, and modifying the driving behavior of the vehicle based on the determined at least one performance limit.

[0008] These and other features, aspects and advantages will be apparent by reading the following detailed description and reviewing the associated drawings.It is to be understood that both the foregoing general description and the following detailed description are illustrative and are not restrictive of the claimed aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A system for controlling a vehicle according to one embodiment is shown;

[0010] Figure 2 An electronic controller according to one embodiment is shown;

[0011] Figure 3 A method for controlling a vehicle according to one embodiment is shown;

[0012] Figure 4 A method for controlling a vehicle according to a second embodiment is shown. DETAILED DESCRIPTION

[0013] One or more embodiments are described and illustrated in the following description and accompanying drawings. These embodiments are not limited to the specific details provided herein and may be modified in various ways. In addition, there may be other embodiments not described herein. Also, the functions described herein as being performed by one component may be performed by multiple components in a distributed manner. Similarly, the functions performed by multiple components may be merged and performed by a single component. Similarly, the components described as performing specific functions may also perform additional functions not described herein. For example, a device or structure "configured" in a certain way is configured at least in that way, but may also be configured in an unlisted manner. In addition, some embodiments described herein may include one or more electronic processors configured to perform the described functions by executing instructions stored in a non-transitory computer-readable medium. Similarly, the embodiments described herein may be implemented as a non-transitory computer-readable medium storing instructions, which may be executed by one or more electronic processors to perform the described functions. As used in this application, "non-transitory computer-readable medium" includes all computer-readable media, but does not include temporary propagation signals. Thus, a non-transitory computer readable medium may include, for example, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (read only memory), a RAM (random access memory), a register memory, a processor cache, or any combination thereof.

[0014] In addition, the wording and terminology used herein are for descriptive purposes and should not be considered as limiting. For example, "including", "containing", "comprising", "having" and variations thereof used herein are meant to cover the items listed thereafter and their equivalents and additional items. The terms "connect" and "couple" are widely used and include both direct and indirect connections and couplings. In addition, "connect" and "couple" are not limited to physical or mechanical connections or couplings, and may include electrical connections or couplings, whether direct or indirect. In addition, electronic communications and notifications may be performed using wired connections, wireless connections, or a combination thereof, and may be transmitted directly or through one or more intermediate devices over various types of networks, communication channels, and connections. In addition, relational terms such as first and second, top and bottom, etc., are used herein only to distinguish one entity or action from another entity or action, without requiring or implying any actual such relationship or order between such entities or actions.

[0015] Figure 1 A system 100 for controlling a vehicle 105 is shown according to one embodiment. The vehicle 105 is shown as having four wheels 110-113 (e.g., a car, truck, or other vehicle), but may also include more or fewer wheels than shown. For example, the vehicle 100 may be a motorcycle and may have only two wheels. In some embodiments, the vehicle 100 is an autonomous vehicle.

[0016] The system 100 also includes a positioning system 115, a plurality of sensors 120, an electronic controller 125, and (optionally) a separate control system 130. The positioning system 115 is configured to determine the real-world position of the vehicle 100. This is accomplished, for example, by using a global positioning system ("GPS") or other system to determine the real-world position of the vehicle 100.

[0017] The plurality of sensors 120 include various sensors commonly found on a vehicle, such as cameras, LIDAR sensors, radar sensors, UV sensors, microphones, etc. The plurality of sensors 120 are located at various locations on the vehicle 100. For example, the cameras may be placed such that one camera is positioned to capture video data of the driving environment in front of the vehicle 100, and one camera is positioned to capture video data of the driving environment behind the vehicle 100. However, it should be understood that any type of sensor may be placed at any location on the vehicle 100.

[0018] Electronic controller 125 is communicatively coupled to, among other things, positioning system 115 and plurality of sensors 120 . Figure 2 An example of an electronic controller 125 is shown in . The electronic controller 125 includes a communication interface 205, an electronic processor 210, and a memory 215. The communication interface 205 allows the electronic controller 125 to communicate with external hardware, such as a positioning system 115 and a plurality of sensors 120. The communication interface 205 also allows the electronic controller 125 to communicate with a remote server to access data for operation of the system 100. For example, a remote server may include map data accessible to the electronic controller 125 to perform the methods and functions described herein, particularly when executed by the electronic processor 210. In some embodiments, the communication interface 205 includes wired interface hardware (e.g., one or more ports, pins, etc.), wireless interface hardware (e.g., one or more transceivers, antennas, etc.), or a combination thereof.

[0019] The electronic processor is communicatively coupled to the communication interface 205 and the memory 215. The electronic processor 210 is configured to access the memory 215 and, among other things, execute instructions for the system 100. The electronic processor 210 may be a microprocessor, an application specific integrated circuit, or a similar processing circuit. The memory 215 is a non-transitory computer-readable medium and contains data and instructions that, when executed by the electronic processor 210, perform the methods and functions described herein.

[0020] In one embodiment, the memory 215 includes a digital map 220. The digital map 220 includes data about various driving routes for the vehicle 105. The data may include road geometry data, driving lane data, traffic data, weather data, etc. In some embodiments, the digital map 220 is periodically updated (e.g., based on communication with one or more of the remote server, sensor 120, and positioning system 115 via the communication interface 205) so that the data accurately reflects the current driving conditions of the vehicle 10. In some embodiments, the digital map 220 may be stored on a remote server accessible via the communication interface 205, rather than being stored in the memory 215.

[0021] The data contained in the digital map 220 can be classified as static data or dynamic data. Static data includes properties of the driving environment that are unlikely to change quickly, such as the number and orientation of driving lanes for a driving segment, the location of radar-reflecting guardrails or metal plates, the location of LIDAR reflectors or windows, the location of billboards that may cause the camera to operate incorrectly, the location of other camera-interfering objects, etc. Dynamic data includes various environmental conditions, such as adverse environmental conditions (e.g., sun glare, snow, fog, rain, darkness, all of which may inhibit the proper function of the sensor) and traffic scenes.

[0022] This data may be collected by the electronic processor 210 through the use of other autonomous vehicles, crowdsourcing, crowdsourcing, or from various other sources, such as weather services, traffic services, etc. Additionally, offline analysis of the digital map 220, either manually or using artificial intelligence, may identify various characteristics of driving segments that may present these various issues to the vehicle 105. The data is then stored as map attributes for each driving segment.

[0023] The driving segment can then be classified by the electronic processor 210 or by a remote processing system such as a remote server based on these different map attributes. The classification can be performed by using a lookup table that associates specific map attributes with specific classifications, machine learning that uses map attributes as input and provides classifications as output, or a rule set that associates different sets of map attributes with different classifications. The classification can be a binary classification (e.g., "limitations to one or more sensors are possible" versus "no limitations"), a quality level classification (e.g., "very high," "high," "medium," and "low"), a separate sensor quality level classification (e.g., "quality level of the right front LIDAR sensor: medium"), or a redundant classification (e.g., "only one sensor can be used for radar functions." These classifications are also stored with the digital map 220 for each driving segment. In some embodiments, the classification can be extended to classify performance limitations depending on specific environmental conditions. For example, some sensors may be affected by the time of day (e.g., the camera does not work effectively at night or the glare from sunrise or sunset obscures the camera) or weather (e.g., the LIDAR sensor may not work effectively in the rain or In some embodiments, under certain conditions, even if the plurality of sensors 120 provide accurate data, the planning algorithms, prediction algorithms, or perception algorithms (e.g., algorithms executed by the electronic controller 125 to control the vehicle 105, plan a driving route for the vehicle 105, and perceive objects in the driving environment of the vehicle 105) may have limitations. For example, some planning algorithms may not be suitable for controlling the vehicle 105 under certain weather conditions and may output driving behavior plans that are not suitable for controlling the vehicle 105 under these weather conditions. In another example, some algorithms may perform poorly when multiple pedestrians are in the driving environment of the vehicle 105 (e.g., when multiple pedestrians are crossing the street in different directions).

[0024] In some embodiments, a probability may also be assigned to each classification based on one or more environmental factors. For example, on a sunny day during the daytime period, the driving segment presents no performance limitations. However, as the daylight fades, there may be a higher probability that the sensor data (such as from a camera) or the algorithm is inaccurate and therefore presents performance limitations. Therefore, in some embodiments, classifications may be performed and then assigned different probabilities of existence based on, for example, time of day or weather factors.

[0025] In some embodiments, map attributes may also include performance limitations caused by traffic conditions, such as long lines of large vehicles (which may obscure radar, LIDAR, and cameras) or special events, such as smoke from wildfires or other occasional performance limitations. These attributes may be identified based on querying a remote server for information or via one or more other sensors of the vehicle 105 that "look ahead" of the vehicle 105.

[0026] The digital map 220 may also have attributes indicating challenging driving situations. These challenging driving situations may be static or dynamic. For example, static (or "a priori") challenging driving situations may include areas with high vehicle accident rates or areas with complex road geometry, such as multi-lane roundabouts or intersections. Dynamic challenging driving situations may include areas with high pedestrian traffic only during certain events (e.g., roads outside a stadium) or changing traffic situations, such as traveling at high speeds.

[0027] Back to Figure 1 , the system 100 also optionally includes a separate control system 130. In some embodiments, the electronic controller 125 is the overall control system for the vehicle 105 and performs both the methods and functions described herein and also performs general control functions for the vehicle 105, such as controlling speed, direction, and other vehicle functions. For example, the electronic controller 125 transmits control signals to one or more of the acceleration system, braking system, and steering system of the vehicle 105 via the communication interface 205 and receives feedback from them. In this embodiment, since the functions of the separate control system 130 are incorporated into the electronic controller 125, a separate control system 130 is not provided. In other embodiments, the electronic controller 125 is tasked only to perform the methods and functions described herein and is communicatively coupled to the separate control system 130 to provide sensor data (e.g., determined performance limits (discussed below), raw sensor data, processed sensor data, etc.) to the separate control system 130, which in turn uses the sensor data to control the vehicle 105.

[0028] Figure 3 A method 300 for controlling a vehicle 105 is shown according to one embodiment. The method 300 includes receiving, using the electronic processor 210, a current location of the vehicle 105 from the positioning system 115 (block 305). For example, the positioning system 115 receives signals via an antenna from global positioning satellites, land-based radio towers, etc.; processes those signals (e.g., using trilateration) to determine the current location; and provides the current location to the electronic processor 210.

[0029] The electronic processor 210 then determines a future driving segment for the vehicle 105 based on the current position of the vehicle 105 (block 310). For example, the electronic processor 210 may determine the future driving segment by selecting a driving segment ahead of the current position of the vehicle 105 on the same road. In some embodiments, instead of viewing future driving segments on the same road, the electronic processor 210 may alternatively access the planned driving route of the vehicle 105 from the memory 215 and select an upcoming driving segment on the planned driving route as the future driving segment. The upcoming driving segment selected on the planned driving route may be on the same road on which the vehicle 105 is currently traveling, or may be on a different road.

[0030] Whether selecting a future driving segment on a planned driving route or a future driving segment ahead of the vehicle on the same road without a planned route, the future driving segment can be selected based on a series of factors. For example, in some embodiments, the electronic processor 210 selects the future driving segment based on the current vehicle, the time of day, weather conditions, etc. With respect to vehicle speed, generally, the faster the vehicle is traveling, the farther ahead of the vehicle along the driving route or the farther ahead of the vehicle on the current road that the electronic processor selects as the future driving segment. In some embodiments, the future driving segment is selected so that any performance limitations that may exist in the future driving segment are considered before encountering the limitations.

[0031] As part of determining the future driving segment in block 310, the electronic processor 210 also determines a classification for the future driving segment. For example, the electronic processor 210 may access the digital map 220 (e.g., using the future driving segment as input to access the digital map 220) and obtain a classification for the future driving segment from the digital map 220. The future driving segment may be determined and analyzed for classification before the vehicle 105 begins on the planned driving route, while the vehicle 150 is in operation, or both, to obtain an initial classification and then obtain updates that may occur in the intervening time before the vehicle reaches the future driving segment.

[0032] Based on the classification assigned to the future driving segment, the electronic processor 210 determines the performance constraints for the future driving segment (block 315). For example, as described above, on certain driving segments, large billboards or metal guardrails may cause a particular sensor such as a radar or LIDAR to return inaccurate data, or may cause a particular algorithm to perform poorly. In some embodiments, the electronic processor 210 determines that the future driving segment has only one performance constraint. In other embodiments, the electronic processor 210 determines that the future driving segment has multiple performance constraints, for example, for various sensors in the plurality of sensors 120.

[0033] The method 300 also includes modifying the driving behavior of the vehicle 105 based on the performance limit using the electronic processor 210 (block 320). The driving behavior of the vehicle 105 may include steering direction, vehicle speed, and other components, such as rules governing where and how the vehicle 105 operates. When a performance limit exists, the driving behavior of the vehicle 105 is modified to, for example, maintain safety standards regardless of the limit. Thus, mitigation measures (e.g., modifications to the driving behavior) are implemented to account for the limit.

[0034] Potential driving behavior modifications that may be implemented to mitigate performance limitations may include setting a preference for the lateral position in the driving lane of the vehicle 105. For example, the electronic processor 210 may set a preference for staying on the right side of the driving lane (in a position where right-hand driving is required by law) to allow the vehicle 105 to more efficiently leave the current driving lane when necessary (e.g., exit ramps or right turns). Other modifications may include setting a preference for a specific lane (e.g., the right lane) on a multi-lane road, changing the driving angle of the vehicle 105 to overcome performance limitations (e.g., changing the angle of approaching a stop light to mitigate or overcome glare on a camera), and limiting complex driving maneuvers of the vehicle 105 (e.g., not allowing lane changes, three-point turns, and other difficult maneuvers). Speed, acceleration, and steering direction thresholds may be used to classify whether a driving maneuver is complex. For example, complex driving maneuvers include driving maneuvers that are performed while the vehicle is traveling at a high rate of speed, that include high rates of acceleration, that involve multiple changes in maneuver direction (e.g., a multi-point U-turn), that involve changes in maneuver direction that exceed a particular angle (e.g., indicating a lane change or U-turn, as opposed to smaller lateral adjustments within a lane), that use data from multiple sensors that exceed a particular threshold (e.g., the more sensors involved, the more complex the maneuver), or a combination thereof.

[0035] Further driving behavior modifications may include adjusting or reducing the speed of the vehicle 105 to provide the vehicle 105 with more time to react to possible obstacles or situations when performance limitations exist. Driving behavior modifications may also include planning and selecting an alternative driving route for the vehicle 105. The alternative driving route may be selected based on a number of factors, such as possible performance limitations of the alternative driving route, the probability of a performance limitation occurring on the alternative driving route, modified travel time, and other factors.

[0036] As mentioned above about Figure 1As described, in some embodiments, the electronic controller 125 directly controls vehicle systems, such as braking systems, acceleration systems, and steering systems. In other embodiments, the electronic controller 125 may not have direct control over the driving behavior of the vehicle 105. More specifically, for example, the system 100 may be a safety system that communicates with a separate control system 130 that is responsible for controlling the vehicle 105. In such an embodiment, the electronic controller 125 may indirectly control the driving behavior of the vehicle 105 through communication with the separate control system 130. For example, safety limits may be output to the control system 130, which then controls the vehicle systems to modify the vehicle driving behavior. Therefore, in order to ensure that the separate control system 130 properly handles performance limits, the system 100 may be configured to take other measures, such as the following regarding Figure 4 Those measures described to modify the driving behavior of vehicle 105.

[0037] Figure 4 A method 400 for controlling a vehicle 105 according to a second embodiment is shown. In this embodiment, the electronic controller 125 does not directly control the motion of the vehicle 105, but instead communicates with and provides sensor data from the plurality of sensors 120 to a separate control system 130.

[0038] Much like the method 300 described above, the method 400 includes receiving, using the electronic processor 210, the current location of the vehicle 105 from the positioning system 115 (block 405), and determining, using the electronic processor 210, a future driving segment of the vehicle 105 based on the current location of the vehicle 105 (block 410). The method 400 also includes determining, using the electronic processor 210, one or more performance limitations of the plurality of sensors 120 based on the future driving segment (block 415). The descriptions provided above with respect to blocks 305, 310, and 315 are similarly applicable to blocks 405, 410, and 415, respectively, and are therefore not repeated.

[0039] The method 400 also includes artificially falsifying sensor data from at least one of the plurality of sensors 120 to initiate a safety action (block 420). During operation of the vehicle 105, the electronic controller 125 provides sensor data to the separate control system 130, and the separate control system 130 controls the vehicle 105 based on the sensor data. Typically, the separate control system 130 operates by receiving sensor data from the electronic controller 125, performing data fusion, determining whether the result is above a threshold, and then controlling the vehicle 105 based on the determination (e.g., if the result data fusion is above the threshold, no safety action is initiated). As previously described, the separate control system 130 may control one or more vehicle systems of the vehicle 105, including an acceleration system, a braking system, a steering system, and other systems. During operation when performance limitations are not detected, the sensor data provided to the separate control system 130 may affect the control of the vehicle system. For example, in response to sensor data indicating that the vehicle 105 is approaching an obstacle, the separate control system 130 may control the braking system to brake the vehicle 105. However, in response to the electronic processor 210 determining in block 415 that one or more performance limitations exist, the electronic processor 210 proceeds to artificially falsify sensor data from at least one sensor in the plurality of sensors 120 to initiate a safety action (block 420). In other words, the electronic processor 210 is configured to intentionally falsify sensor quality or integrity data of at least one sensor in the plurality of sensors 105 and provide the falsified sensor quality or integrity data to the separate control system 130. The falsified sensor quality or integrity data triggers or initiates the separate control system 130 to perform a safety action for the vehicle 105 because the sensor data no longer meets the safety threshold, such as performing the safety action referenced above. Figure 3 Intentionally falsifying sensor quality or integrity data includes providing intentionally falsified values ​​to a model used by the separate control system 130 for sensor data fusion. The intentionally falsified values ​​may cause the model to fail to meet a predetermined integrity level for normal operation of the vehicle 105, which may cause the separate control system 130 to operate the vehicle 105 in a "degraded" safety mode. In the degraded safety mode, the separate control system 130 may perform the above-referenced Figure 3 The predetermined integrity level may be set based on, for example, known industry safety standards, such as safety standards from the Institute of Electrical and Electronics Engineers ("IEEE").

[0040] The intentionally false value can be a quality value, such as a value selected from a range of 0-100%. In this embodiment, the intentionally false value will be set to 0% or 100%. In another embodiment, a non-numeric value can be provided to the separate control system 130, wherein the separate control system 130 expects a numeric value to plan a driving movement for the vehicle 105. In this case, since no number is received, the separate control system 130 will initiate a safety action. In yet another embodiment, the intentionally false value is a discrete value or array, such as an industry standard or a value selected from 0-5, which indicates, for example, how many sensors are confirming data. In this case, a value such as 0 or 5 is provided to the separate control system 130.

[0041] In some embodiments, the electronic processor 210 is configured to intentionally modify certain metadata values ​​of the sensor data based on performance limitations. For example, the confidence, uncertainty, or integrity values ​​of the sensor data provided to the individual control systems 130 may be modified to initiate a safety action.

[0042] The following examples illustrate exemplary systems and methods described herein.

[0043] Example 1: A system for controlling a vehicle, the system comprising: a positioning system; a memory storing a digital map; and an electronic processor configured to: receive a current position of the vehicle from the positioning system; determine a future driving segment of the vehicle based on the current position of the vehicle; determine at least one performance limitation based on the future driving segment of the vehicle; and modify the driving behavior of the vehicle based on the determined at least one performance limitation when the vehicle is operating in the future driving segment.

[0044] Example 2: A system according to Example 1, wherein modifying the driving behavior includes at least an action selected from a group of actions, the group of actions including: setting a preference for a lateral position in a driving lane of the vehicle, limiting complex driving maneuvers for the vehicle, setting a preference for a specific lane on a multi-lane road, changing the speed of the vehicle, and selecting an alternative driving route for the vehicle.

[0045] Example 3: A system according to example 1 or 2, wherein the future driving segment includes a classification indicating a specific performance limitation.

[0046] Example 4: The system of Example 3, wherein the classification includes a probability that the particular performance limitation will occur based on one or more environmental conditions.

[0047] Example 5: The system of Example 3 or 4, wherein the classification is a classification selected from the group consisting of binary classification, quality grade classification, individual sensor quality grade classification, and redundant classification.

[0048] Example 6: A system according to any of Examples 3-5, wherein the classification includes at least one of a static map attribute and a dynamic map attribute.

[0049] Example 7: The system of any one of Examples 1-6, wherein the future driving segment is also determined based on a planned driving route of the vehicle.

[0050] Example 8: A method for controlling a vehicle, the method comprising: using an electronic processor to receive a current position of the vehicle from a positioning system; using the electronic processor to determine a future driving segment of the vehicle based on the current position of the vehicle; using the electronic processor to determine at least one performance limitation based on the future driving segment of the vehicle; and using the electronic processor to modify the driving behavior of the vehicle based on the determined at least one performance limitation.

[0051] Example 9: A method according to Example 8, wherein modifying the driving behavior includes at least one action selected from a group of actions, the group of actions including: setting a preference for a lateral position in a driving lane of the vehicle, limiting complex driving maneuvers for the vehicle, setting a preference for a specific lane on a multi-lane road, changing the speed of the vehicle, and selecting an alternative driving route for the vehicle.

[0052] Example 10: A method according to example 8 or 9, wherein the future driving segment includes a classification indicating a specific performance limitation.

[0053] Example 11: The method of Example 10, wherein the classification includes a probability that the particular performance limitation will occur based on one or more environmental conditions.

[0054] Example 12: The method according to Example 10 or 11, wherein the classification is a classification selected from the group of classifications consisting of binary classification, quality level classification, individual sensor quality level classification, and redundant classification.

[0055] Example 13: A method according to any of Examples 10-13, wherein the classification includes at least one of a static map attribute and a dynamic map attribute.

[0056] Example 14: A method according to any one of Examples 8-13, wherein the future driving segment of the vehicle is also determined based on a planned driving route of the vehicle.

[0057] Example 15: A non-transitory computer-readable medium containing instructions that, when executed by an electronic processor, are configured to perform a set of functions, the set of functions comprising: receiving a current position of a vehicle from a positioning system; determining a future driving segment of the vehicle based on the current position of the vehicle; determining at least one performance limitation based on the future driving segment of the vehicle; and modifying the driving behavior of the vehicle based on at least one determined limitation.

[0058] Example 16: The non-transitory computer readable medium of Example 15, wherein the future driving segment includes a classification indicating a specific performance limitation.

[0059] Example 17: The non-transitory computer-readable medium of Example 16, wherein the classification includes a probability that the particular performance limitation will occur based on one or more environmental conditions.

[0060] Example 18: The non-transitory computer readable medium of Example 16 or 17, wherein the classification is a classification selected from the group of classifications consisting of a binary classification, a quality grade classification, a separate sensor quality grade classification, and a redundant classification.

[0061] Example 19: The non-transitory computer-readable medium of any of Examples 16-18, wherein the classification comprises at least one of a static map attribute and a dynamic map attribute.

[0062] Example 20: A non-transitory computer-readable medium according to any one of Examples 15-19, wherein modifying the driving behavior includes at least an action selected from a group of actions, the group of actions including: setting a preference for a lateral position in a driving lane of the vehicle, limiting complex driving maneuvers for the vehicle, setting a preference for a specific lane on a multi-lane road, changing the speed of the vehicle, and selecting an alternative driving route for the vehicle.

[0063] Thus, the embodiments described herein provide, among other things, systems and methods for controlling a vehicle. Various features, advantages and embodiments are set forth in the following claims.

Claims

1. A system for controlling a vehicle, the system comprising: Positioning system; A memory for storing digital maps; as well as An electronic processor configured to: receiving a current position of the vehicle from the positioning system; determining a future driving segment of the vehicle based on a current position of the vehicle; determining at least one performance limit for the future driving segment of at least one sensor of the vehicle based on the classification of the future driving segment of the vehicle; determining a driving maneuver category of the vehicle that overcomes the determined at least one performance limitation of the future driving segment, wherein a plurality of driving maneuver categories including the driving maneuver category are based on speed, acceleration, a plurality of utilized sensors, and a steering direction threshold; determining a mitigation measure for overcoming at least one of the determined performance limitations; wherein the mitigation measure comprises limiting a complex driving maneuver for the vehicle based on the determined driving maneuver category of the vehicle, and wherein the complex driving maneuver is determined based on the driving maneuver category; and When the vehicle is operated in the future driving segment, operation of the vehicle is controlled based on the determined at least one performance limitation and the determined mitigation measure.

2. The system according to claim 1, wherein: The mitigation measures also include at least one of setting a preference for a lateral position in a driving lane of the vehicle, setting a preference for a particular lane on a multi-lane road, and altering a speed of the vehicle.

3. The system according to claim 1, wherein: The classification indicates certain performance limitations.

4. The system according to claim 3, wherein: The classification includes a probability that the particular performance limitation will occur based on one or more environmental conditions.

5. The system according to claim 3, wherein: The classification is a classification selected from the group of classifications consisting of binary classification, quality level classification, individual sensor quality level classification, and redundant classification.

6. The system according to claim 3, wherein: The classification includes at least one of a static map attribute and a dynamic map attribute.

7. The system according to claim 1, wherein: The future driving segment is also determined based on a planned driving route of the vehicle.

8. A method for controlling a vehicle, the method comprising: receiving, using an electronic processor, a current location of the vehicle from a positioning system; determining, with the electronic processor, a future driving segment of the vehicle based on a current position of the vehicle; determining, with the electronic processor, at least one performance limit for the future driving segment of at least one sensor of the vehicle based on the classification of the future driving segment of the vehicle; determining, using the electronic processor, a driving maneuver category of the vehicle that overcomes the determined at least one performance limitation of the future driving segment, wherein a plurality of driving maneuver categories including the driving maneuver category are based on speed, acceleration, a plurality of utilized sensors, and a steering direction threshold; determining, using the electronic processor, a mitigation measure for overcoming the determined at least one performance limitation; wherein the mitigation measure comprises limiting a complex driving maneuver for the vehicle based on the determined driving maneuver category of the vehicle, and wherein the complex driving maneuver is determined based on the driving maneuver category; and Operation of the vehicle with the electronic processor is controlled while the vehicle is operating in the future driving segment based on the determined at least one performance limitation and the determined mitigation measure.

9. The method according to claim 8, wherein: The mitigation measures also include at least one of setting a preference for a lateral position in a driving lane of the vehicle, setting a preference for a particular lane on a multi-lane road, and altering a speed of the vehicle.

10. The method according to claim 8, wherein: The classification indicates certain performance limitations.

11. The method according to claim 10, wherein: The classification includes a probability that the particular performance limitation will occur based on one or more environmental conditions.

12. The method according to claim 10, wherein: The classification is a classification selected from the group of classifications consisting of binary classification, quality level classification, individual sensor quality level classification, and redundant classification.

13. The method according to claim 10, wherein: The classification includes at least one of a static map attribute and a dynamic map attribute.

14. The method according to claim 8, wherein: The future driving segment of the vehicle is also determined based on a planned driving route of the vehicle.

15. A non-transitory computer readable medium containing instructions which, when executed by an electronic processor, are configured to perform a set of functions comprising: receiving the current position of the vehicle from the positioning system; determining a future driving segment of the vehicle based on the current position of the vehicle; determining at least one performance limit for the future driving segment of at least one sensor of the vehicle based on the classification of the future driving segment of the vehicle; determining, using the electronic processor, a driving maneuver category of the vehicle that overcomes the determined at least one performance limitation of the future driving segment, wherein a plurality of driving maneuver categories including the driving maneuver category are based on speed, acceleration, a plurality of utilized sensors, and a steering direction threshold; determining a mitigation measure for overcoming at least one of the determined performance limitations; wherein the mitigation measure comprises limiting a complex driving maneuver for the vehicle based on the determined driving maneuver category of the vehicle, and wherein the complex driving maneuver is determined based on the driving maneuver category; and When the vehicle is operated in the future driving segment, operation of the vehicle is controlled based on the determined at least one performance limitation and the determined mitigation measure.

16. The non-transitory computer readable medium of claim 15, wherein: The classification indicates certain performance limitations.

17. The non-transitory computer readable medium of claim 16, wherein: The classification includes a probability that the particular performance limitation will occur based on one or more environmental conditions.

18. The non-transitory computer readable medium of claim 16, wherein: The classification is a classification selected from the group of classifications consisting of binary classification, quality level classification, individual sensor quality level classification, and redundant classification.

19. The non-transitory computer readable medium of claim 16, wherein: The classification includes at least one of a static map attribute and a dynamic map attribute.

20. The non-transitory computer readable medium of claim 15, wherein: The mitigation measures also include at least one of setting a preference for a lateral position in a driving lane of the vehicle, setting a preference for a particular lane on a multi-lane road, and altering a speed of the vehicle.

Images