Patent Publication Number: US-11639173-B2

Title: Vehicle planned path signal

Description:
BACKGROUND 
     A vehicle can be equipped with electronic and electro-mechanical components, e.g., computing devices, networks, sensors and controllers, etc. A vehicle computer can acquire data regarding the vehicle&#39;s environment and can operate the vehicle or at least some components thereof based on the data. Vehicle sensors can provide data concerning routes to be traveled and objects to be avoided in the vehicle&#39;s environment. Operation of the vehicle can rely upon acquiring accurate and timely data regarding objects in a vehicle&#39;s environment while the vehicle is being operated. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram illustrating an example vehicle control system for a host vehicle. 
         FIG.  2    is a diagram illustrating operating a host vehicle according to the system of  FIG.  1   . 
         FIGS.  3 A- 3 B  are diagrams of a movable object intersecting a planned path of a host vehicle. 
         FIG.  3 C  is a diagram of a movable object in a travel route intersecting the planned path of the host vehicle. 
         FIG.  4    is a flowchart of an example process for operating the host vehicle. 
         FIG.  5    is a flowchart of an example process for operating the movable object. 
     
    
    
     DETAILED DESCRIPTION 
     A system includes a first computer including a processor and a memory, the memory storing instructions executable by the processor to predict that a future location of a movable object and a planned path of a host vehicle will intersect. The instructions further include instructions to actuate a host vehicle component to output a signal indicating to move the movable object. The instructions further include instructions to then, at least one of (a) determine that the movable object has moved, or (b) update the planned path of the host vehicle. The instructions further include instructions to then, operate the host vehicle along the planned path or the updated planned path. 
     The first computer may be included on the host vehicle. The system may further include a second computer on the movable object, the second computer including a second processor and a second memory, the second memory storing instructions executable by the second processor to predict that the host vehicle will move along the planned path. The instructions may further include then, at least one of (a) determine that the host vehicle has moved along the updated path or (b) update the future location of the movable object. The instructions may further include then, operate the movable object to the future location or the updated future location. 
     The instructions can further include instructions to determine the future location of the movable object is in a travel route based on at least one of sensor data and location data. 
     The instructions can further include instructions to determine the updated future location will avoid intersecting the planned path. 
     The instructions can further include instructions to determine the updated future location based on a distance from the future location to the updated future location being within a specified distance. 
     The movable object can be a vehicle. 
     The instructions can further include instructions to operate the movable object to a parking space based on moving the movable object to a threshold number of updated future locations. 
     The host vehicle component can be at least one of a horn, exterior lights, and a propulsion component. 
     The instructions can further include instructions to actuate the propulsion component to operate the host vehicle along a portion of the planned path based on a distance between the host vehicle and the movable object. 
     The instructions can further include instructions to input host vehicle sensor data into a machine learning program that identifies the movable object. 
     The future location can be defined in part by a path of the movable object. 
     The instructions can further include instructions to determine the host vehicle is within a distance threshold of the movable object. 
     The instructions can further include instructions to determine at least one of the movable object and the host vehicle is moving at a speed below a speed threshold. 
     The instructions can further include instructions to, upon identifying a parking space for the host vehicle, actuate a turn signal of the host vehicle. 
     The instructions can further include instructions to, upon determining a planned path into a parking space, perform a parking maneuver along the planned path. 
     The movable object can be one of a vehicle or a pedestrian. 
     A method includes predicting that a future location of a movable object and a planned path of a host vehicle will intersect. The method further includes actuating a host vehicle component to output a signal indicating to move the movable object. The method further includes then, at least one of (a) determining that the movable object has moved, or (b) updating the planned path of the host vehicle. The method further includes then, operating the host vehicle along the planned path or the updated planned path. 
     The method can further include predicting that the host vehicle will move along the planned path. The method can further include then, at least one of (a) determining that the host vehicle has moved along the updated path or (b) updating the future location of the movable object. The method can further include then, operating the movable object to the future location or the updated future location. 
     The future location can be defined in part by a path of the movable object. 
     The movable object can be one of a vehicle or a pedestrian. 
     Further disclosed herein is a computing device programmed to execute any of the above method steps. Yet further disclosed herein is a computer program product, including a computer readable medium storing instructions executable by a computer processor, to execute an of the above method steps. 
     With initial reference to  FIGS.  1 - 3 C , an example vehicle control system  100  includes a first computer  110  that is programmed to predict that a future location of a movable object  140  and a planned path P of a host vehicle  105  will intersect. The first computer  110  is further programmed to actuate a host vehicle component  125  to output a signal indicating to move the movable object  140 . The first computer  110  is further programmed to then at least one of (a) determine that the movable object  140  has moved or (b) update the planned path P of the host vehicle  105 . The first computer  110  is further programmed to then operate the host vehicle  105  along the planned path P or the updated planned path P. 
     The first computer  110  operates a host vehicle  105  along a planned path P in an area  200 . Typically, the host vehicle  105  changes a direction of travel, i.e., turns/and or changes from forward to reverse, while operating along the planned path P to, e.g., perform a parking maneuver, search for available parking spaces, etc. However, a movable object  140  may be unable to predict or determine a change of direction along the planned path P of the host vehicle  105 , increasing a risk that the movable object  140  will intersect the planned path P of the host vehicle  105 . Advantageously, the first computer  110  can actuate one or more vehicle components  125  to output a signal to move the movable object  140 , which alerts the movable object  140  to the planned path P of the host vehicle  105 . The signal indicates the movable object  140  is obstructing the planned path P of the host vehicle  105 , which can prompt the movable object  140  to move away from the planned path P. 
     Turning now to  FIG.  1   , the host vehicle  105  includes the first computer  110 , sensors  115 , actuators  120  to actuate various host vehicle components  125 , and a vehicle communications module  130 . The communications module  130  allows the first computer  110  to communicate with a server  150  and/or the second vehicle  106 , e.g., via a messaging or broadcast protocol such as Dedicated Short Range Communications (DSRC), cellular, and/or other protocol that can support vehicle-to-vehicle, vehicle-to infrastructure, vehicle-to-cloud communications, or the like, and/or via a packet network  135 . 
     The first computer  110  includes a processor and a memory such as are known. The memory includes one or more forms of computer-readable media, and stores instructions executable by the first computer  110  for performing various operations, including as disclosed herein. 
     The first computer  110  may operate the host vehicle  105  in an autonomous, a semi-autonomous mode, or a non-autonomous (or manual) mode. For purposes of this disclosure, an autonomous mode is defined as one in which each of vehicle  105  propulsion, braking, and steering are controlled by the first computer  110 ; in a semi-autonomous mode the first computer  110  controls one or two of vehicle  105  propulsion, braking, and steering; in a non-autonomous mode a human operator controls each of vehicle  105  propulsion, braking, and steering. 
     The first computer  110  may include programming to operate one or more of host vehicle  105  brakes, propulsion (e.g., control of acceleration in the host vehicle  105  by controlling one or more of an internal combustion engine, electric motor, hybrid engine, etc.), steering, transmission, climate control, interior and/or exterior lights, horn, doors, etc., as well as to determine whether and when the first computer  110 , as opposed to a human operator, is to control such operations. 
     The first computer  110  may include or be communicatively coupled to, e.g., via a vehicle communications network such as a communications bus as described further below, more than one processor, e.g., included in electronic controller units (ECUs) or the like included in the host vehicle  105  for monitoring and/or controlling various host vehicle components  125 , e.g., a transmission controller, a brake controller, a steering controller, etc. The first computer  110  is generally arranged for communications on a vehicle communication network that can include a bus in the host vehicle  105  such as a controller area network (CAN) or the like, and/or other wired and/or wireless mechanisms. 
     Via the vehicle  105  network, the first computer  110  may transmit messages to various devices in the host vehicle  105  and/or receive messages (e.g., CAN messages) from the various devices, e.g., sensors  115 , an actuator  120 , ECUs, etc. Alternatively, or additionally, in cases where the first computer  110  actually comprises a plurality of devices, the vehicle communication network may be used for communications between devices represented as the first computer  110  in this disclosure. Further, as mentioned below, various controllers and/or sensors  115  may provide data to the first computer  110  via the vehicle communication network. 
     Vehicle  105  sensors  115  may include a variety of devices such as are known to provide data to the first computer  110 . For example, the sensors  115  may include Light Detection And Ranging (LIDAR) sensor(s)  115 , etc., disposed on a top of the host vehicle  105 , behind a vehicle  105  front windshield, around the host vehicle  105 , etc., that provide relative locations, sizes, and shapes of objects surrounding the host vehicle  105 . As another example, one or more radar sensors  115  fixed to vehicle  105  bumpers may provide data to provide locations of the objects, second vehicles  106 , etc., relative to the location of the host vehicle  105 . The sensors  115  may further alternatively or additionally, for example, include camera sensor(s)  115 , e.g. front view, side view, etc., providing images from an area surrounding the host vehicle  105 . In the context of this disclosure, an object is a physical, i.e., material, item that has mass and that can be represented by physical phenomena (e.g., light or other electromagnetic waves, or sound, etc.) detectable by sensors  115 . Thus, the host vehicle  105  and the movable object  140 , as well as other items including as discussed below, fall within the definition of “object” herein. 
     The first computer  110  is programmed to receive data from one or more sensors  115  substantially continuously, periodically, and/or when instructed by a server  150 , etc. The data may, for example, include a location of the host vehicle  105 . Location data may be in a known form, e.g., geo-coordinates such as latitude and longitude coordinates obtained via a navigation system, as is known, that uses the Global Positioning System (GPS). Additionally, or alternatively, the data can include a position of an object, e.g., a vehicle, a pole, a curb, a bicycle, a tree, a shrub, etc., relative to the vehicle  105 . As one example, the data may be image data of the environment around the vehicle  105 . In such an example, the image data may include one or more objects in the area  200 . Image data is digital image data, e.g., comprising pixels with intensity and color values, that can be acquired by camera sensors  115 . The sensors  115  can be mounted to any suitable location in or on the host vehicle  105 , e.g., on a vehicle  105  bumper, on a vehicle  105  roof, etc., to collect images of the environment around the host vehicle  105 . 
     The first computer  110  can be programmed to classify and/or identify the object(s) included in the image data. For example, conventional object classification techniques can be used, e.g., in the first computer  110  based on lidar sensor  115  data, camera sensor  115  data, etc., to classify a detected object as movable or non-movable. Additionally, or alternatively, conventional object identification techniques can be used, e.g., in the first computer  110  based on lidar sensor  115  data, camera sensor  115  data, etc., to identify a type of movable object  140 , e.g., a vehicle (see  FIGS.  3 A and  3 C ), a pedestrian (see  FIG.  3 B ), a drone, etc., as well as physical features of objects. 
     Various techniques such as are known may be used to interpret sensor  115  data. For example, camera and/or lidar image data can be provided to a classifier that comprises programming to utilize one or more conventional image classification techniques. For example, the classifier can use a machine learning technique in which data known to represent various objects, is provided to a machine learning program for training the classifier. Once trained, the classifier can accept as input host vehicle sensor  115  data, e.g., an image, and then provide as output, for each of one or more respective regions of interest in the image, an identification and/or a classification (i.e., movable or non-movable) of one or more objects or an indication that no object is present in the respective region of interest. Further, a coordinate system (e.g., polar or cartesian) applied to an area proximate to the host vehicle  105  can be applied to specify locations and/or areas (e.g., according to the host vehicle  105  coordinate system, translated to global latitude and longitude geo-coordinates, etc.) of objects identified from sensor  115  data. Yet further, the first computer  110  could employ various techniques for fusing (i.e., incorporating into a common coordinate system or frame of reference) data from different sensors  115  and/or types of sensors  115 , e.g., lidar, radar, and/or optical camera data. 
     The vehicle  105  actuators  120  are implemented via circuits, chips, or other electronic and or mechanical components that can actuate various vehicle subsystems in accordance with appropriate control signals as is known. The actuators  120  may be used to control components  125 , including braking, acceleration, and steering of a host vehicle  105 . 
     In the context of the present disclosure, a vehicle component  125  is one or more hardware components adapted to perform a mechanical or electro-mechanical function or operation—such as moving the host vehicle  105 , slowing or stopping the vehicle  105 , steering the host vehicle  105 , etc. Non-limiting examples of components  125  include a propulsion component (that includes, e.g., an internal combustion engine and/or an electric motor, etc.), a transmission component, a steering component (e.g., that may include one or more of a steering wheel, a steering rack, etc.), a suspension component  125  (e.g., that may include one or more of a damper, e.g., a shock or a strut, a bushing, a spring, a control arm, a ball joint, a linkage, etc.), a brake component, a park assist component, an adaptive cruise control component, an adaptive steering component, one or more passive restraint systems (e.g., airbags), a movable seat, etc. 
     In addition, the first computer  110  may be configured for communicating via a vehicle-to-vehicle communication module  130  or interface with devices outside of the host vehicle  105 , e.g., through a vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2X) wireless communications (cellular and/or DSRC, etc.) to another vehicle, to a server  150  (typically via direct radio frequency communications), and/or (typically via the network  135 ) to a second vehicle  106 . The communications module  130  could include one or more mechanisms, such as a transceiver, by which the computers  110  of vehicles  105  may communicate, including any desired combination of wireless (e.g., cellular, wireless, satellite, microwave and radio frequency) communication mechanisms and any desired network topology (or topologies when a plurality of communication mechanisms are utilized). Exemplary communications provided via the communications module  130  include cellular, Bluetooth, IEEE 802.11, dedicated short range communications (DSRC), and/or wide area networks (WAN), including the Internet, providing data communication services. 
     The network  135  represents one or more mechanisms by which a first computer  110  may communicate with remote computing devices, e.g., the server  150 , another first computer, etc. Accordingly, the network  135  can be one or more of various wired or wireless communication mechanisms, including any desired combination of wired (e.g., cable and fiber) and/or wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or topologies when multiple communication mechanisms are utilized). Exemplary communication networks include wireless communication networks (e.g., using Bluetooth®, Bluetooth® Low Energy (BLE), IEEE 802.11, vehicle-to-vehicle (V2V) such as Dedicated Short Range Communications (DSRC), etc.), local area networks (LAN) and/or wide area networks (WAN), including the Internet, providing data communication services. 
     The server  150  can be a conventional computing device, i.e., including one or more processors and one or more memories, programmed to provide operations such as disclosed herein. Further, the server  150  can be accessed via the network  135 , e.g., the Internet or some other wide area network. 
     As one example, the movable object  140  may be a pedestrian (see  FIG.  3 B ). As another example, the movable object  140  may be a vehicle (see  FIG.  3 A ). In such an example, the vehicle  140  includes a second computer  145 . The second computer  145  includes a second processor and a second memory such as are known. The second memory includes one or more forms of computer-readable media, and stores instructions executable by the second computer  110  for performing various operations, including as disclosed herein. 
     Additionally, the vehicle  140  may include sensors, actuators to actuate various vehicle components, and a vehicle communications module. The sensors, actuators to actuate various vehicle components, and the vehicle communications module typically have features in common with the sensors  115 , actuators  120  to actuate various host vehicle components  125 , and the vehicle communications module  130 , and therefore will not be described further to avoid redundancy. 
       FIG.  2    is a diagram illustrating a host vehicle  105  operating in an example ground surface area  200  that includes marked sub-areas  210  for vehicles. An area  200  may be on a street or road, e.g., alongside a curb or an edge of the street, a parking lot or structure or portion thereof, etc. The first computer  110  may be programmed to determine the host vehicle  105  is within the area  200  by, e.g., GPS-based geo-fencing. In such an example, the GPS geo-fence specifies a perimeter of the area  200 . The first computer  110  can then determine the host vehicle  105  is within the area  200  based on the location data of the host vehicle  105  indicating the host vehicle  105  is within the geo-fence. 
     The first computer  110  can, for example, generate a planned path P to operate the host vehicle  105  in the area  200 . Alternatively, the server  150  can generate the planned path P and provide the planned path P to the first computer  110 , e.g., via the network  135 . As used herein, a “planned path” is a set of points, e.g., that be specified as coordinates with respect to a host vehicle coordinate system and/or geo-coordinates, that the first computer  110  is programmed to determine with a conventional navigation and/or path planning algorithm. The planned plan P can, for example, direct the host vehicle  105  along each aisle along which sub-areas  210  are located in the area  200  to search for available sub-areas  210  (see  FIG.  2   ). In such an example, the first computer  110  is programmed to identify a potentially available sub-area  210  for parking the host vehicle  105  based on image data received from camera sensors  115 , e.g., via the vehicle network. For example, a sub-area  210  can be a parking space indicated by conventional markings, e.g., painted lines on a ground surface, and conventional image recognition techniques can be employed by the first computer  110  to identify the sub-area  210  and a potentially available parking space. 
     Upon identifying the potentially available sub-area  210 , the first computer  110  can generate a planned path P for parking the host vehicle  105  in the potentially available sub-area  210  (see  FIGS.  3 A- 3 B ). The first computer  110  can determine to move the host vehicle  105  forward (see  FIG.  3 A ) or backward (see  FIG.  3 B ) into the sub-area  210  according to the planned path P. The first computer  110  may determine forward or backward based on, e.g., a rule for positioning the vehicle  105  (e.g., a rear of the vehicle must be facing outward toward a path for vehicle travel from a parking space sub-area, or that a front of the vehicle  105  must be so facing, etc.) in the area  200 . For example, one or more rules can be stored in a memory of the computer  110 , received from a remote computer (not shown) via the network  135 , etc., received via a user input to a vehicle  105  human machine interface such as a touchscreen, knob, button, dial, etc. The first computer  110  can then provide a signal that a planned path P of the host vehicle  105  includes moving into the sub-area  210 , For example, the computer  110  can actuate a turn signal on the same side of the host vehicle  105  as the sub-area  210 , e.g., to indicate that the vehicle  105  may park in the sub-area  210 . 
     The first computer  110  may be programmed to actuate one or more host vehicle components  125  to operate the host vehicle  105  along the planned path P into the sub-area  210 , e.g., perform a parking maneuver. For example, the first computer  110  can actuate one or more host vehicle components  125 , e.g., a propulsion component, a steering component, etc., to move the host vehicle  105  forward into the sub-area  210  along a forward path. As another example, based on planning a backward path, the first computer  110  can be programmed to actuate one or more host vehicle components  125  to operate the host vehicle  105  to a stop position past the potentially available sub-area  210 , such that the first computer  110  can move the host vehicle  105  backward into the sub-area  210 . The stop position is a forward distance measured in a forward direction of travel of the host vehicle  105  from the sub-area  210 , e.g., a line of the sub-area  210 , to a rear of the host vehicle  105  and a lateral distance measured in a direction transverse to the forward direction from the sub-area  210 , e.g., an end of the line of the sub-area  210 , to a side of the host vehicle  105 . The distances are stored in the memory of the first computer  110 . The preset distances are chosen to be the shortest possible distances that still permit the host vehicle  105  to travel in reverse to the sub-area  210 , based on a turning radius of the host vehicle  105  while traveling in reverse. 
     While the host vehicle  105  is performing a maneuver to move into a sub-area  210 , the first computer  110  can receive sensor  115  data, e.g., image data, of the potentially available sub-area  210 . The image data can include an object in the potentially available sub-area  210  that was previously occluded by other objects between the host vehicle  105  and the potentially available sub-area  210 . The first computer  110  can then verify an availability of the sub-area  210  based on the sensor  115  data indicating a presence or absence of an object in the sub-area  210 . For example, the first computer  110  can employ conventional image analysis or pattern recognition techniques to analyze the image data and identify a stationary, i.e., non-movable, object, e.g., a parked vehicle, a pole, a shopping cart rack, etc., in the sub-area  210 . If a stationary object is in the sub-area  210 , e.g., between the markings of the parking space, the first computer  110  determines that the sub-area  210  is unavailable. If a stationary object is not in the sub-area  210 , e.g., between the markings of the parking space, then the first computer  110  can determine that the sub-area  210  is potentially available. 
     Additionally, the first computer  110  can analyze the sensor  115  data to determine access to the sub-area  210  for the host vehicle  105  is prohibited. For example, the first computer  110  can determine access is prohibited based on detecting a condition or conditions specified to prohibit access to the sub-area  210 , e.g., a sign, a barrier, a marking, etc., via image data, that specifies one or more conditions, e.g., vehicle characteristics, user characteristics, time of day, special event, etc., prohibiting access the sub-area  210 . The first computer  110  can identify the condition(s) prohibiting access based on comparing the detected condition(s) to conditions stored in the memory of the first computer  110 . The first computer  110  can then compare the host vehicle  105  data, e.g., based on host vehicle data, such as vehicle dimensions, a type of vehicle, a vehicle identifier, vehicle user characteristics, etc., to the prohibition condition(s) to determine whether the host vehicle  105  is prohibited from accessing the sub-area  210 . In the case that the host vehicle  105  is not prohibited from accessing the sub-area  210 , the first computer  110  determines the sub-area  210  is available. In the case that the host vehicle  105  is prohibited from accessing the sub-area  210 , the first computer  110  can determine the sub-area  210  is unavailable. If the sub-area  210  is available, the first computer  110  may operate the host vehicle  105  along the planned path P into the sub-area  210 . If the sub-area  210  is unavailable, the first computer  110  updates the planned path P to search for available sub-areas  210  and actuates one or more host vehicle components to operate the host vehicle  105  along the updated path. 
     While operating along a planned path P, the host vehicle  105  can identify a movable object  140  as discussed above. The first computer  110  can be programmed to predict whether a future location of the movable object  140  will intersect the planned path P of the host vehicle  105 . The first computer  110  can predict the future location of the movable object based on sensor  115  data. The future location of the movable object  140  is defined at least in part by a predicted path of the movable object  140 . For example, the first computer  110  can predict a path of the moving object  140  based on identifying a direction of movement of the movable object  140  via sensor  115  data, e.g., sequential frames of image data. The first computer  110  can then compare the predicted path of the movable object  140  to the planned path P of the host vehicle  105 . 
     As another example, upon determining the movable object  140  is stationary based on image data, the first computer  110  can predict whether the movable object  140  will remain stationary or move based on physical characteristics of the movable object  140 . For example, when the movable object  140  is a vehicle, the first computer  110  can predict the movable object  140  will move based on detecting one or more of sound from the movable object  140  indicating the engine is running, exterior lights in an “on” state, doors in a closed position, etc. Conversely, the first computer  110  can predict the movable object  140  will remain stationary based on detecting one or more of a lack of sound from the movable object  140  indicating the engine is not running, exterior lights in an “off” state, doors in an open position, etc. 
     If the first computer  110  determines the movable object  140  will remain stationary, then the first computer  110  can predict that the future location will be a current location of the movable object  140 . The first computer  110  can then compare the future location to the planned path P of the host vehicle  105 . If the first computer  110  determines the movable object  140  will move, then the first computer  110  can predict a path of the movable object  140  based on, e.g., physical characteristics of the movable object  140  (such as an activated turn signal, turned vehicle wheels, activated backup lights, a direction of travel, etc.). The first computer  110  can then compare the predicted path of the movable object  140  to the planned path P of the host vehicle  105  to determine whether the predicted path of the movable object  140  will intersect the planned path P of the host vehicle  105 . 
     The first computer  110  can be programmed to actuate one or more host vehicle components  125  based on the prediction. In the case that the first computer  110  predicts the future location of the movable object will not intersect the planned path P, the first computer  110  can operate the host vehicle  105  along the planned path P. In the case that the first computer  110  predicts that the future location of the movable object  140  will intersect the planned path P, the first computer  110  can actuate one or more host vehicle components  125  to output a signal to move the movable object  140 . 
     For example, the first computer  110  may actuate a propulsion component to move the host vehicle  105  along a selected portion (i.e., segment that is some but less than all) of the planned path P. In this example, the first computer  110  can select the portion of the planned path P based on a first distance between the host vehicle  105  and the movable object  140 . The first distance may be a linear distance, a radial distance, or some other distance. For example, the first computer  110  can compare the first distance to a first distance threshold. The first distance threshold specifies a maximum distance between the host vehicle  105  and the movable object above which the first computer  110  can move the host vehicle  105 . The first distance threshold may be determined based on, e.g., empirical testing to determine the minimum distance at which the first computer  110  can prevent the host vehicle  105  from impacting the movable object  140  (e.g., based on size of the host vehicle  105 , speed of the host vehicle  105 , type of movable object  140 , etc.). For example, the first distance threshold may be greater when the movable object  140  is a pedestrian as compared to when the movable object  140  is a vehicle. The first distance threshold may be stored in the memory of the first computer  110 . 
     In the case that the first distance is above the first distance threshold, the first computer  110  can determine that the selected portion of the planned path P is the path from the location of the host vehicle  105  to the location at which the first distance between the host vehicle  105  and the movable object  140  equals the first distance threshold. In the case that the first distance is equal to or less than the first distance threshold, the first computer  110  determines that the selected portion of the planned path P is a nudge. In this context, a “nudge” is a non-zero movement of the host vehicle  105  along the planned path P. The nudge may be determined by, e.g., empirical testing to determine a minimum amount of movement that indicates the host vehicle  105  is moving along the planned path P. The nudge may be specified by, e.g., a linear displacement of the host vehicle  105  or a rotational displacement of the vehicle wheels of the host vehicle  105 . The nudge may be stored in the memory of the first computer  110 . 
     Additionally, or alternatively, the first computer  110  can actuate a horn, exterior lights (e.g., to flash headlamps, to activate backup lights, etc.), etc. to output the signal. The first computer  110  can, for example, actuate different host vehicle components  125  after respective predetermined time periods. The predetermined time periods may be determined by, e.g., empirical testing to determine an amount of time for movable objects  140 , such as a pedestrian or a vehicle, to detect and react to the signal. The time periods may be stored in the memory of the first computer  110 . 
     Upon outputting the signal to move the movable object  140 , the first computer  110  can determine whether the movable object  105  has moved, or changed paths, after an abort time. The abort time may be determined by, e.g., empirical testing indicating a minimum amount of time to determine the movable object  140  will not move. The first computer  110  can, for example, compare a location of the movable object  140 , e.g., obtained via image data, to the planned path P of the host vehicle  105 . In the case that the movable object  140  has moved and is not intersecting the planned path P, the first computer  110  can operate the host vehicle  105  along the planned path P. In the case that the movable object  140  has not moved, i.e., is intersecting the planned path P, the first computer  110  can update the planned path P, e.g., to search for available sub-areas  210 , to move around the movable object  140 , etc., for the host vehicle  105 . The first computer  110  can then actuate one or more host vehicle components  125  to operate the host vehicle  105  along the updated planned path P. 
     The second computer  145  may be programmed to determine a second distance between the vehicle  140  and the host vehicle  105 . For example, the second computer  145  can receive and analyze sensor data to determine the second distance between the vehicle  140  and the host vehicle  105 . The second computer  145  can then compare the second distance to a second distance threshold. The second distance threshold specifies a maximum distance within which the vehicle  140  may be judged to be obstructing movement of the host vehicle  105  along the planned path P. The second distance threshold may be determined by, e.g., empirical testing that indicates the minimum distance between the vehicle  140  and the host vehicle  105  such that the host vehicle  105  can maneuver around the vehicle  140 . The second distance threshold may be stored, e.g., in the second memory of the second computer  145 . 
     The second computer  145  may be programmed to determine a speed of the host vehicle  105 . For example, the second computer  145  can receive and analyze sensor data to determine a change in the second distance between the host vehicle  105  and the vehicle  140  during a specified time. The second computer  145  can then compare the speed of the host vehicle  105  to a speed threshold. The speed threshold specifies a maximum speed below which the host vehicle  105  can be judged as stopped. The speed threshold may be determined by, e.g., empirical testing to determine a minimum speed at which vehicles typically operate around each other in an area  200 . The speed threshold may be stored, e.g., in the second memory of the second computer  145 . 
     The second computer  145  may be programmed to determine a width of a path around the vehicle  140 . The width of the path around the vehicle is a distance from a side of the vehicle  140  to the detected objects. The second computer  145  can, for example, receive and analyze sensor data to detect objects laterally spaced from the vehicle  140 . The second computer  145  can then determine the width of the path around the vehicle  140 . For example, the second computer  145  can employ free space computation techniques to image data that identifies a range of pixel coordinates associated with and an object laterally spaced from the vehicle  140  and free space (i.e., space in which no object is detected) between the vehicle  140  and the object. By identifying a set of pixel coordinates in an image associated with the free space and the object and determining a distance (in pixel coordinates) from an image sensor lens, e.g., across the free space, to the identified object pixel coordinates, the second computer  145  can then determine a distance, e.g., across the free space, of an image sensor lens from the object. That is, according to conventional techniques, the second computer  145  can determine a distance from the lens to the identified coordinates (in pixel coordinates) and can further determine, from the image, an angle between a line from the sensor lens to the identified object and an axis extending from the lens parallel to a lateral axis of the vehicle  140 . Then, using trigonometric functions, the second computer  145  can determine a length of a line drawn along the vehicle  140  lateral axis from the sensor  115  lens to a point on the object. The width of the path may be determined from the length of the line drawn along the vehicle  140  lateral axis. 
     As another example, the second computer  145  can generate a radar tracklet, i.e., a partial trajectory, of an object based on radar sensor  115  data about the object received during a time period. The second computer  145  can then predict a location of the object from the radar tracklet relative to the vehicle  140 . By identifying a predicted location of the object relative to the vehicle  140 , the second computer  145  can determine a distance of the image sensor lens from the object. That is, according to conventional techniques, the second computer  145  can determine a distance from the sensor lens to the predicted location and can further determine, from the image, an angle between a line from the sensor lens to the predicted location and an axis extending from the lens parallel to the vehicle  140  lateral axis. Then, using trigonometric functions, the second computer  145  can determine the width of the path, as discussed above. 
     The second computer  145  can then compare the width to a width threshold. The width threshold specifies a minimum distance between the vehicle  140  and an object such that the host vehicle  105  can operate between the vehicle  140  and the object. The width threshold may be equal to the width of the host vehicle  105 . In such an example, the second computer  145  can determine the width of the host vehicle  105  based on image data, e.g., pixels used to depict the host vehicle  105 . As another example, the width threshold may be determined by a rule specifying a maximum width of a vehicle that can operate in the area  200  and/or on a roadway. In such an example, the width threshold may be stored in the second memory of the second computer  145 . 
     The second computer  145  may be programmed to predict the future location of the vehicle  140 . The future location can be the same as or different than a current location of the vehicle  140 . For example, in the case that the vehicle  140  is parked, or in an “off” state, the second computer  145  can predict the future location is a current location of the vehicle  140 . As another example, the future location can be defined, at least in part, by a predicted path of the vehicle  140 . That is, the second computer  145  can predict one or more future locations of the vehicle  140  (e.g., determined at different points in time) based on a predicted path of the vehicle  140 . 
     The second computer  145  may be programmed to determine whether the future location of the vehicle  140  is in a travel route. As used herein, a “travel route” specifies an area of the ground surface designated for vehicle movement in the area  200 , e.g., aisles between sub-areas  210 . The second computer  145  can, for example, identify a travel route based on sensor data. In such an example, the second computer  145  can detect one or more indicators of a travel route, e.g., via image data. The indicators can include signs, markings on the ground surface, movement of vehicles along the route, etc. As another example, the second computer  145  can identify a travel route based on map data of the area  200  (e.g., stored in the second memory or received from a remote computer). The second computer  145  can then compare the future location of the vehicle  140  to the travel route. For example, the second computer  145  can compare location data of the future location to a travel route indicated in the map data of the area  200 . As another example, the second computer  145  can detect an indicator at the future location based on sensor data, e.g., image data of the future location. 
     The second computer  145  may be programmed to predict that the host vehicle  105  will move along the planned path P. For example, the second computer  145  can receive and analyze sensor data to detect a signal from the host vehicle  105 , e.g., actuation of one or more host vehicle components  125  such as activation of headlamps or backup lights, activation of a horn, movement of the host vehicle  105  along a portion of the planned path P, doors opened etc., from the host vehicle  105 . The second computer  145  can, for example, predict the planned path P of the host vehicle  105  based on physical characteristics of the host vehicle  105  (e.g., turned vehicle wheels, a direction of travel, etc.). Alternatively, the host vehicle  105  can transmit the planned path P to the second computer  145 , e.g., through V2V wireless communications (cellular and/or DSRC, etc.). If the second computer  145  detects the signal from the host vehicle  105 , the second computer  145  predicts that the host vehicle  105  will move along the planned path P. If the second computer  145  does not detect the signal from the host vehicle  105 , the second computer  145  can predict that the host vehicle  105  will not move along the planned path P. 
     The second computer  145  can determine an updated predicted future location for the vehicle  140 . For example, the second computer  145  determines the updated predicted future location based on a third distance. The third distance is a maximum distance that the vehicle  140  can move from the future location to the updated predicted future location. The third distance may be stored in the second memory of the second computer  145 . The third distance may be determined, e.g., based on the third distance being equal to a distance to the closest available sub-area  210  to the vehicle  140 . Additionally, the second computer  145  can determine the updated predicted future location based further on determining that the updated predicted future location will avoid intersecting the planned path P of the host vehicle  105 . For example, upon determining the planned path P of the host vehicle  105 , the second computer  145  can compare the updated predicted future location to the planned path P of the host vehicle  105 . If the updated predicted future location intersects the planned path P, then the updated predicted future location is invalid. If the updated future location does not intersect the planned path P, then the updated future location is valid. 
     The second computer  145  may be programmed to determine the host vehicle  105  has moved, i.e., is not obstructed by the vehicle  140 , after a move time based on sensor data. The move time may be determined, e.g., by empirical testing indicating a maximum amount of time to determine the host vehicle  105  will not move. The second computer  145  can, for example, detect the host vehicle  105  moved around the vehicle  140  within the abort time. As another example, the second computer  145  can detect the host vehicle  105  moving in an opposite direction, i.e., away from the vehicle  140 , within the abort time. In the case that the host vehicle  105  has moved, the second computer  145  can actuate one or more vehicle components to operate the vehicle  140  to the future location. In the case that the host vehicle  105  has not moved, the second computer  145  can actuate one or more vehicle components to operate the vehicle  140  to the updated future location. 
     The second computer  145  may be programmed to count a number of occurrences of the vehicle  140  moving to an updated future position. The second computer  145  can store the number of occurrences, e.g., in the second memory. The second computer  145  can then compare the number of occurrences to a threshold number. The threshold number specifies a maximum number of instances the vehicle  140  can be moved to an updated future position. The threshold number may be determined by, e.g., empirical testing to determine the vehicle  140  is impeding traffic. The threshold number may be an integer greater than 0. The threshold number may be stored in the second memory of the second computer  145 . 
     The second computer  145  may be programmed to move the vehicle  140  to a sub-area  210 , e.g., a parking space. For example, in the case that the second computer  145  cannot determine a valid updated future location, the second computer  145  can operate the vehicle  140  to a sub-area  210 . As another example, the second computer  145  can operate the vehicle  140  to a sub-area  210  based on moving the vehicle  140  to the threshold number of updated future locations. In these circumstances, the second computer  145  can actuate one or more vehicle components to move the vehicle  140  to the parking space. 
       FIG.  4    is a diagram of an example process  400  for operating a host vehicle  105  in an area  200 . The process  400  begins in a block  405 . 
     In the block  405 , the first computer  110  receives sensor  115  data, e.g., image data, from one or more sensors  115 , e.g., via the vehicle network, while the host vehicle  105  is operating in the area  200  (e.g., to search for a sub-area  210  such as a parking space). The image data includes the environment around the host vehicle  105 , e.g., the area  200 , one or more sub-areas  210 , one or more movable objects  140 , one or more stationary objects, etc. The process  400  continues in a block  410 . 
     In the block  410 , the first computer  110  identifies a potentially available sub-area  210  based on the image data. For example, the first computer  110  can identify the sub-area  210  using conventional image recognition techniques, as discussed above. If the first computer  110  identifies a potentially available sub-area  210 , e.g., a parking space, the process  400  continues in a block  415 . Otherwise, the process  400  continues in the block  405 . 
     In the block  415 , the first computer  110  activates a turn signal. Specifically, the first computer  110  activates the turn signal on the same side of the host vehicle  105  as the sub-area  210 , e.g., to indicate the host vehicle  105  may park in the sub-area  210 . Additionally, the first computer  110  determines a planned path P to operate the host vehicle  105  to the sub-area  210 , e.g., via a conventional navigation and/or path planning algorithm that receives a destination, e.g., a point in the sub-area  210 , and uses map and/or sensor  115  data for an area such as the area  200 . For example, the first computer  110  can determine to pull the host vehicle  105  forward into the sub-area  210  or back the host vehicle  105  into the sub-area  210 , as discussed above. The process  400  continues in a block  420 . 
     In the block  420 , the first computer  110  determines whether the sub-area  210  is available based on sensor  115  data, e.g., image data, received while the host vehicle  105  is maneuvering into the sub-area  210 . For example, in the case that the first computer  110  determines to back the host vehicle  105  into the sub-area  210  (as discussed above), the first computer  110  can receive sensor  115  data while the host vehicle  105  moves past the sub-area  210  to a stop position (as discussed above). As another example, the first computer  110  can receive sensor  115  data while the host vehicle  105  moves forward into the sub-area  210 . The first computer  110  can analyze image data, e.g., using conventional image analysis or pattern recognition techniques, to identify a stationary object in the sub-area  210 , e.g., between markings of a parking space. 
     Additionally, the first computer  110  can determine whether the host vehicle  105  is prohibited from accessing the sub-area  210 . For example, the first computer  110  can determine access is prohibited based on detecting a condition or conditions specified to prohibit access to the sub-area  210 , e.g., signs, markings, barriers, etc., via the image data. The first computer  110  can then compare host vehicle  105  data to the prohibition condition(s) to determine whether the host vehicle  105  is prohibited from accessing the sub-area  210 . If the host vehicle  105  is not prohibited from accessing the sub-area  210  and the first computer  110  determines the sub-area  210  is free of stationary objects, the first computer  110  determines the sub-area  210  is available. If the host vehicle  105  is prohibited from accessing the sub-area  210  or the first computer  110  identifies a stationary object in the sub-area  210 , the first computer determines the sub-area  210  is unavailable. In the case that the sub-area  210  is available, the process  400  continues in a block  430 . Otherwise, the process  400  continues in a block  425 . 
     In the block  425 , the first computer  110  deactivates the turn signal, e.g., to indicate the host vehicle  105  will not park in the sub-area  210 . The first computer  110  then operates the host vehicle  105  through the area  200 , e.g., to continue searching for an available sub-area  210 . For example, the first computer  110  can actuate one or more host vehicle components  125  to move the host vehicle  105 , e.g., according to conventional techniques for autonomous or semi-autonomous operation, along the planned path P to search the area  200 . The process  400  returns to the block  405 . 
     In the block  430 , the first computer  110  operates the host vehicle  105  along the planned path P into the sub-area  210 . That is, the first computer  110  actuates one or more host vehicle  105  components  125 , e.g., a steering component, a propulsion component, etc., to move the host vehicle  105  along the planned path P. As the host vehicle  105  moves along the planned path P, the first computer  110  receives sensor  115  data, e.g., image data, from sensors  115  facing along the planned path P. The process  400  continues in a block  435 . 
     In the block  435 , the first computer  110  predicts whether a movable object  140  will intersect a planned path P of the host vehicle  10 . For example, the first computer  110  can identify and determine a trajectory (i.e., a path and speed(s) along the path) for a movable object  140  based on the sensor  115  data received in the block  430 , e.g., lidar data, radar data, and/or visual image data, etc. The first computer  110  can then predict whether a future location of the movable object  140  will intersect the planned path P of the host vehicle  105 . For example, in the case that the movable object  140  is moving, the first computer  110  can predict a path of the moving object  140  based on identifying a direction of movement of the movable object  140  via sensor  115  data, e.g., sequential frames of image data. The first computer  110  can then compare the predicted path of the movable object  140  to the planned path P of the host vehicle  105 . 
     As another example, in the case that the movable object  140  is stationary, the first computer  110  can predict whether the movable object  140  will remain stationary or move based on physical characteristics of the movable object  140 , as discussed above. If the first computer  110  predicts the movable object  140  will move, the first computer  110  can then compare the future location to the planned path P of the host vehicle  105 . If the first computer  110  predicts the movable object  140  will remain stationary, the first computer  110  can then compare a current location of the movable object  140  to the planned path P. If the predicted future location of the movable object  140  intersects the planned path P of the host vehicle  105 , the process  400  continues in a block  440 . Otherwise, the process  400  continues in a block  465 . 
     In the block  440 , the first computer  110  compares a first distance between the movable object  140  and the host vehicle  105  to a first distance threshold. For example, the first computer  110  determines the first distance between the movable object  140  and the host vehicle  105  via image data, e.g., using various image processing techniques. The process  400  continues in a block  445 . 
     In the block  445 , the first computer  110  actuates one or more host vehicle components  125  to output a signal to move the movable object  140 . For example, the first computer  110  can actuate a propulsion component and a steering component to operate the host vehicle  105  along a selected portion of the planned path P, as described above. The first computer  110  can determine the portion of the planned path P based on the first distance. If the first distance is greater than the first distance threshold, the first computer  110  selects the portion of the planned path P as a segment of the path P from the current location of the host vehicle  105  to the location at which the first distance between the host vehicle  105  and the movable object  140  equals the first distance threshold. If the first distance is equal to or less than the first distance threshold, the first computer  110  determines the portion of the planned path P is a nudge (as described above). 
     Additionally, the first computer  110  can actuate exterior lights (e.g., flashing headlamps, activating backup lights, etc.) a horn, and other suitable host vehicle components  125 . The first computer  110  can, for example, actuate different host vehicle components  125  after predetermined time periods, as discussed above. The process  400  continues in a block  450 . 
     In the block  450 , the first computer  110  determines whether the movable object  140  is intersecting the planned path P. For example, the first computer  110  can detect that the movable object  140  has moved, or changed paths, after an abort time (as discussed above). For example, the first computer  110  can compare a location of the movable object  140 , e.g., obtained via image data, to the planned path P of the host vehicle  105 . In the case that movable object  140  has moved and is not intersecting the planned path P, the process  400  continues in a block  455 . In the case that movable object  140  has not moved, i.e., is intersecting the planned path P, the process  400  continues in a block  460 . 
     In the block  455 , the first computer  110  operates the host vehicle  105  along the planned path P into the sub-area  210 , e.g., a parking space. For example, the first computer  110  actuates one or more host vehicle components  125 , e.g., a steering component and/or a propulsion component, to move the host vehicle  105  along the planned path P. The process  400  continues in a block  465 . 
     In the block  460 , the first computer  110  determines an updated planned path, e.g., using conventional navigation and/or path planning algorithms. For example, the first computer  110  can determine an updated planned path to move the host vehicle  105  out of the sub-area  210  and to continue searching the area  200  for an available sub-area  210 . The first computer  110  can then operate the host vehicle  105  along the updated planned path. For example, the first computer  110  actuates one or more host vehicle components  125 , e.g., a steering component and/or a propulsion component, to move the host vehicle  105  along the updated planned path, e.g., according to conventional techniques for autonomous or semi-autonomous operation. The process  400  ends following the block  460 . 
     In the block  465 , the first computer  110  determines whether the host vehicle  105  is fully in the sub-area  210 . The host vehicle  105  is fully in the sub-area  210  when the entire host vehicle  105  is within a boundary of the sub-area  210 , e.g., between conventional markings of a parking space. For example, the first computer  110  compares a location of the host vehicle  105 , e.g., based on image data, GPS data, etc., to the sub-area  210 . As another example, the first computer  110  can analyze sensor  115  data, e.g., image data, to determine the host vehicle  105  is approximately centered laterally and longitudinally relative to the sub-area  210 , e.g., based on conventional markings indicating a parking space. Upon determining the host vehicle  105  is fully in the sub-area  210 , the first computer  110  can actuate one or more host vehicle components  125 , e.g., to stop or park the host vehicle  105  in the sub-area  210 . In the case that the host vehicle  105  is fully in the sub-area  210 , the process  400  ends. Otherwise, the process  400  continues in the block  430 . 
       FIG.  5    is a diagram of an example process  500  for operating a movable object  140 , in this example a vehicle  140  including a second computer  145 , in the area  200 . The process  500  beings in a block  505 . 
     In the block  505 , the second computer  145  receives image data from one or more image sensors  115 , e.g., camera(s), while the vehicle  140  is in the area  200 . The image data includes the environment around the vehicle  140 , e.g., the area  200 , one or more sub-areas  210 , other vehicles including the host vehicle  105 , etc. The process  500  continues in a block  510 . 
     In the block  510 , the second computer  145  determines whether a second distance from the host vehicle  105  to the vehicle  140  is within a second distance threshold (as described above). For example, the second computer  145  can identify the host vehicle  105  approaching the vehicle  140 , e.g., based on lidar data, radar data, and/or visual image data, etc. The second computer  145  can then determine the second distance between the vehicle  140  and the host vehicle  105  via image data, e.g., using various image processing techniques. The second computer  145  can then compare the second distance to the second threshold. 
     Additionally, the second computer  145  can determine whether the host vehicle  105  is moving at a speed within a speed threshold (as described above). For example, the second computer  145  can determine the speed of the host vehicle  105  based on image data. In such an example, the second computer  145  can determine a change in the second distance between the vehicle  140  and the host vehicle  105  during a time period (e.g., as determined by sequential frames of image data). In the case that the second distance from host vehicle  105  to the vehicle  140  is within the threshold and the host vehicle  105  is operating at a speed within the speed threshold, the process  500  continues in a block  515 . Otherwise, the process  500  returns to the block  505 . 
     In the block  515 , the second computer  145  predicts a future location of the vehicle  140 . For example, in the case that the vehicle  140  is parked or in an “off” state, the second computer  145  can determine the future location is the current location of the vehicle  140 . As another example, the future location can be defined, at least in part, by a predicted path of the vehicle  140 . That is, the second computer  145  can predict one or more future locations of the vehicle  140  (e.g., determined at different points in time) based on analyzing a predicted path of the vehicle  140 . The process  500  continues in a block  520 . 
     In the block  520 , the second computer  145  determines whether the predicted future location of the vehicle  140  is in a travel route (as described above). The second computer  145  can, for example, identify a travel route based on sensor data, as discussed above. As another example, the second computer  145  can identify a travel route based on map data of the area  200 , e.g., stored in the second memory or received from a remote computer. The second computer  145  can then compare the predicted future location of the vehicle  140  to the travel route. In the case that the future location is in a travel route, the process  500  continues in a block  525 . Otherwise, the process  500  continues in a block  560 . 
     In the block  525 , the second computer  145  predicts whether the host vehicle  105  will move along a planned path P. The second computer  145  can predict the planned path P of the host vehicle  105  based on physical characteristics of the host vehicle  105  (e.g., turned vehicle wheels, a direction travel, etc.) detected via image data. As another example, the second computer  145  can receive the planned path P from the host vehicle  105 , e.g., via V2V communication. 
     Additionally, the second computer  145  can, for example, receive and analyze sensor data to detect the signal to move the vehicle  140 , e.g., activation of headlamps or backup lights, activation of a horn, movement of the host vehicle  105  along a portion of the planned path P, closed doors, etc., from the host vehicle  105 . If the second computer  145  detects the signal from the host vehicle  105 , the second computer  145  predicts that the host vehicle  105  will move along the planned path P, and the process  500  continues in a block  530 . If the second computer  145  does not detect the signal from the host vehicle  105 , the second computer  145  can predict that the host vehicle  105  will not move, and the process  500  continues in a block  560 . 
     In the block  530 , the second computer  145  predicts whether the future location of the vehicle  140  will intersect a planned path P of the host vehicle  105 . For example, the second computer  145  can compare the planned path P to the predicted future location of the vehicle  140 . Additionally, the second computer  145  can determine a width of a path around the vehicle  140  based on image data, e.g., using image processing techniques. The second computer  145  can then compare the width to a width threshold. If the width is equal to or greater than the width threshold, the second computer  145  predicts the predicted future location will not intersect the planned path P of the host vehicle  105 . If the width is less than the width threshold, the second computer  145  predicts that the predicted future location will intersect the planned path P of the host vehicle  105 . In the case that the second computer  145  predicts that the predicted future location will intersect the planned path P of the host vehicle  105 , the process  500  continues in a block  535 . Otherwise, the process  500  ends. 
     In the block  535 , the second computer  145  determines an updated predicted future location for the vehicle  140 . For example, the second computer  145  determines the updated predicted future location based on a third distance (as discussed above). The second computer  145  then compares the third distance to a third distance threshold. If the third distance is less than or equal to the third distance threshold, then the second computer  145  determines the updated predicted future location is valid. If the third distance is greater than the third distance threshold, then the second computer  145  determines the updated predicted future location is invalid. 
     Additionally, the second computer  145  determines the updated predicted future location based on determining that the updated predicted future location will avoid intersecting the planned path P of the host vehicle  105 . For example, the second computer  145  can compare the updated predicted future location to the planned path P of the host vehicle  105 . Additionally, the second computer  145  can determine a width of the path around the vehicle  140  at the updated predicted future location and compare the width to the width threshold. If the updated predicted future location intersects the planned path P, then the second computer  145  determines the updated predicted future location is invalid. If the updated future location does not intersect the planned path P, then the second computer  145  determines the updated predicted future location is valid. The process  500  continues in a block  540 . 
     In the block  540 , the second computer  145  determines whether the host vehicle  105  moved such that the predicted future location of the vehicle  140  does not intersect the planned path P of the host vehicle  105 . For example, the second computer  145  can detect that the host vehicle  105  has moved, or changed paths, within an abort time based on sensor data, e.g., image data, indicating a location of the host vehicle  105 . Alternatively, the second computer  145  can detect that the host vehicle  105  has not moved within the abort time based on sensor data. In the case that the host vehicle  105  has not moved, the process  500  continues in a block  545 . Otherwise, the process  500  continues in the block  560 . 
     In the block  545 , the second computer  145  determines whether the number of occurrences of the vehicle  140  moving to an updated future location is less than a threshold number (as discussed above). For example, the second computer  145  can compare the number of occurrences to the threshold number. If the number of occurrences is less than the threshold number, then the process  500  continues in a block  550 . Otherwise, the process  500  continues in a block  555 . 
     In the block  550 , the second computer  145  moves the vehicle  140 . For example, the second computer  145  can actuate one or more vehicle components to move the vehicle  140  to the updated future location. The process  500  ends following the block  550 . 
     In the block  555 , the second computer  145  moves the vehicle  140 . For example, the second computer  145  can actuate one or more vehicle components to move the vehicle  140 , e.g., according to conventional techniques for autonomous or semi-autonomous operation, to a sub-area  210 , e.g., a parking space. The process  500  ends following the block  555 . 
     In the block  560 , the second computer  145  operates the vehicle  140  to the future location, e.g., according to conventional techniques for autonomous or semi-autonomous operation. For example, the second computer  145  can actuate one or more vehicle components  125  to move the vehicle  140  to the future location, e.g., when the vehicle  140  is moving along a path. As another example, the second computer  145  can maintain the vehicle  140  at a current location, e.g., when the vehicle  140  is stationary. The process  500  returns to the block  505  following the block  560 . 
     As used herein, the adverb “substantially” means that a shape, structure, measurement, quantity, time, etc. may deviate from an exact described geometry, distance, measurement, quantity, time, etc., because of imperfections in materials, machining, manufacturing, transmission of data, computational speed, etc. 
     In general, the computing systems and/or devices described may employ any of a number of computer operating systems, including, but by no means limited to, versions and/or varieties of the Ford Sync® application, AppLink/Smart Device Link middleware, the Microsoft Automotive® operating system, the Microsoft Windows® operating system, the Unix operating system (e.g., the Solaris® operating system distributed by Oracle Corporation of Redwood Shores, Calif.), the AIX UNIX operating system distributed by International Business Machines of Armonk, N.Y., the Linux operating system, the Mac OSX and iOS operating systems distributed by Apple Inc. of Cupertino, Calif., the BlackBerry OS distributed by Blackberry, Ltd. of Waterloo, Canada, and the Android operating system developed by Google, Inc. and the Open Handset Alliance, or the QNX® CAR Platform for Infotainment offered by QNX Software Systems. Examples of computing devices include, without limitation, an on-board first computer, a computer workstation, a server, a desktop, notebook, laptop, or handheld computer, or some other computing system and/or device. 
     Computers and computing devices generally include computer-executable instructions, where the instructions may be executable by one or more computing devices such as those listed above. Computer executable instructions may be compiled or interpreted from computer programs created using a variety of programming languages and/or technologies, including, without limitation, and either alone or in combination, Java™, C, C++, Matlab, Simulink, Stateflow, Visual Basic, Java Script, Perl, HTML, etc. Some of these applications may be compiled and executed on a virtual machine, such as the Java Virtual Machine, the Dalvik virtual machine, or the like. In general, a processor (e.g., a microprocessor) receives instructions, e.g., from a memory, a computer readable medium, etc., and executes these instructions, thereby performing one or more processes, including one or more of the processes described herein. Such instructions and other data may be stored and transmitted using a variety of computer readable media. A file in a computing device is generally a collection of data stored on a computer readable medium, such as a storage medium, a random access memory, etc. 
     Memory may include a computer-readable medium (also referred to as a processor-readable medium) that includes any non-transitory (e.g., tangible) medium that participates in providing data (e.g., instructions) that may be read by a computer (e.g., by a processor of a computer). Such a medium may take many forms, including, but not limited to, non-volatile media and volatile media. Non-volatile media may include, for example, optical or magnetic disks and other persistent memory. Volatile media may include, for example, dynamic random access memory (DRAM), which typically constitutes a main memory. Such instructions may be transmitted by one or more transmission media, including coaxial cables, copper wire and fiber optics, including the wires that comprise a system bus coupled to a processor of an ECU. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read. 
     Databases, data repositories or other data stores described herein may include various kinds of mechanisms for storing, accessing, and retrieving various kinds of data, including a hierarchical database, a set of files in a file system, an application database in a proprietary format, a relational database management system (RDBMS), etc. Each such data store is generally included within a computing device employing a computer operating system such as one of those mentioned above, and are accessed via a network in any one or more of a variety of manners. A file system may be accessible from a computer operating system, and may include files stored in various formats. An RDBMS generally employs the Structured Query Language (SQL) in addition to a language for creating, storing, editing, and executing stored procedures, such as the PL/SQL language mentioned above. 
     In some examples, system elements may be implemented as computer-readable instructions (e.g., software) on one or more computing devices (e.g., servers, personal computers, etc.), stored on computer readable media associated therewith (e.g., disks, memories, etc.). A computer program product may comprise such instructions stored on computer readable media for carrying out the functions described herein. 
     With regard to the media, processes, systems, methods, heuristics, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes may be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps may be performed simultaneously, that other steps may be added, or that certain steps described herein may be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating certain embodiments and should in no way be construed so as to limit the claims. 
     Accordingly, it is to be understood that the above description is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the invention should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the arts discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the invention is capable of modification and variation and is limited only by the following claims. 
     All terms used in the claims are intended to be given their plain and ordinary meanings as understood by those skilled in the art unless an explicit indication to the contrary in made herein. In particular, use of the singular articles such as “a,” “the,” “said,” etc. should be read to recite one or more of the indicated elements unless a claim recites an explicit limitation to the contrary.