Abstract:
Aspects of the disclosure relate generally to controlling an autonomous vehicle in a variety of unique circumstances. These include adapting control strategies of the vehicle based on discrepancies between map data and sensor data obtained by the vehicle. These further include adapting position and routing strategies for the vehicle based on changes in the environment and traffic conditions. Other aspects of the disclosure relate to using vehicular sensor data to update hazard information on a centralized map database. Other aspects of the disclosure relate to using sensors independent of the vehicle to compensate for blind spots in the field of view of the vehicular sensors. Other aspects of the disclosure involve communication with other vehicles to indicate that the autonomous vehicle is not under human control, or to give signals to other vehicles about the intended behavior of the autonomous vehicle.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
       [0001]    This application claims the benefit under 35 U.S.C. §371 of published PCT Patent Application Number PCT/US2015/64282, filed 7 Dec. 2015 and published as WO2016/126321 on 11 Aug. 2016, which claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Patent Application No. 62/112775, filed 6 Feb. 2015, the entire disclosure of which is hereby incorporated herein by reference. 
     
    
     FIELD OF THE INVENTION 
       [0002]    This disclosure generally relates to autonomous vehicle guidance systems, including methods for positioning an autonomous vehicle on a roadway along with destination-based navigation methods. 
       BACKGROUND 
       [0003]    Autonomous vehicles typically utilize multiple data sources to determine their location, to identify other vehicles, to identify potential hazards, and to develop navigational routing strategies. These data sources can include a central map database that is preloaded with road locations and traffic rules corresponding to areas on the map. Data sources can also include a variety of sensors on the vehicle itself to provide real-time information relating to road conditions, other vehicles and transient hazards of the type not typically included on a central map database. 
         [0004]    In many instances a mismatch can occur between the map information and the real-time information sensed by the vehicle. Various strategies have been proposed for dealing with such a mismatch. For example, U.S. Pat. No. 8,718,861 to Montemerlo et al. teaches detecting deviations between a detailed map and sensor data and alerting the driver to take manual control of the vehicle when the deviations exceed a threshold. U.S. Pub. No. 2014/0297093 to Murai et al. discloses a method of correcting an estimated position of the vehicle by detecting an error in the estimated position, in particular when a perceived mismatch exists between road location information from a map database and from vehicle sensors, and making adjustments to the estimated position. 
         [0005]    A variety of data sources can be used for the central map database. For example, the Waze application provides navigational mapping for vehicles. Such navigational maps include transient information about travel conditions and hazards uploaded by individual users. Such maps can also extract location and speed information from computing devices located within the vehicle, such as a smart phone, and assess traffic congestion by comparing the speed of various vehicles to the posted speed limit for a designated section of roadway. 
         [0006]    Strategies have also been proposed in which the autonomous vehicle will identify hazardous zones relative to other vehicles, such as blind spots. For example, U.S. Pat. No. 8,874,267 to Dolgov et al. discloses such a system. Strategies have also been developed for dealing with areas that are not detectable by the sensors on the vehicle. For example, the area behind a large truck will be mostly invisible to the sensors on an autonomous vehicle. U.S. Pat. No. 8,589,014 to Fairfield et al. teaches a method of calculating the size and shape of an area of sensor diminution caused by an obstruction and developing a new sensor field to adapt to the diminution. 
         [0007]    Navigational strategies for autonomous vehicles typically include both a destination-based strategy and a position-based strategy. Destination strategies involve how to get from point ‘A’ to point ‘B’ on a map using known road location and travel rules. These involve determining a turn-by-turn path to direct the vehicle to the intended destination. Position strategies involve determining optimal locations for the vehicle (or alternatively, locations to avoid) relative to the road surface and to other vehicles. Changes to these strategies are generally made during the operation of the autonomous vehicle in response to changing circumstances, such as changes in the position of surrounding vehicles or changing traffic conditions that trigger a macro-level rerouting evaluation by the autonomous vehicle. 
         [0008]    Position-based strategies have been developed that automatically detect key behaviors of surrounding vehicles. For example, U.S. Pat. No. 8,935,034 to Zhu et al. discloses a method for detecting when a surrounding vehicle has performed one of several pre-defined actions and altering the vehicle control strategy based on that action. 
         [0009]    One of many challenges for controlling autonomous vehicles is managing interactions between autonomous vehicles and human-controlled vehicles in situations that are often handled by customs that are not easily translated into specific driving rules. 
       SUMMARY 
       [0010]    One aspect of the disclosure involves a method comprising controlling by one or more computing devices an autonomous vehicle in accordance with a first control strategy; developing by one or more computing devices said first control strategy based at least in part on data contained on a first map; receiving by one or more computing devices sensor data from said vehicle corresponding to a first set of data contained on said first map; comparing said sensor data to said first set of data on said first map on a periodic basis; developing a first correlation rate between said sensor data and said first set of data on said first map; and adopting a second control strategy when said correlation rate drops below a predetermined value. 
         [0011]    Another aspect of the disclosure involves a method comprising controlling by one or more computing devices an autonomous vehicle in accordance with a first control strategy; receiving by one or more computing devices map data corresponding to a route of said vehicle; developing by one or more computing devices a lane selection strategy; receiving by one or more computing devices sensor data from said vehicle corresponding to objects in the vicinity of said vehicle; and changing said lane selection strategy based on changes to at least one of said sensor data and said map data. 
         [0012]    Another aspect of the disclosure involves a method comprising controlling by one or more computing devices an autonomous vehicle in accordance with a first control strategy; receiving by one or more computing devices sensor data from said vehicle corresponding to moving objects in the vicinity of said vehicle; receiving by one or more computing devices road condition data; determining by one or more computing devices undesirable locations for said vehicle relative to said moving objects; and wherein said step of determining undesirable locations for said vehicle is based at least in part on said road condition data. 
         [0013]    Another aspect of the disclosure involves a method comprising controlling by one or more computing devices an autonomous vehicle in accordance with a first control strategy; developing by one or more computing devices said first control strategy based at least in part on data contained on a first map, wherein said first map is simultaneously accessible by more than one vehicle; receiving by one or more computing devices sensor data from said vehicle corresponding to objects in the vicinity of said vehicle; and updating by said one or more computing devices said first map to include information about at least one of said objects. 
         [0014]    Another aspect of the disclosure involves a method comprising controlling by one or more computing devices an autonomous vehicle; activating a visible signal on said autonomous vehicle when said vehicle is being controlled by said one or more computing devices; and keeping said visible signal activated during the entire time that said vehicle is being controlled by said one or more computing devices. 
         [0015]    Another aspect of the disclosure involves a method comprising controlling by one or more computing devices an autonomous vehicle in accordance with a first control strategy; receiving by one or more computing devices sensor data corresponding to a first location; detecting a first moving object at said first location; changing said first control strategy based on said sensor data relating to said first moving object; and wherein said sensor data is obtained from a first sensor that is not a component of said autonomous vehicle. 
         [0016]    Another aspect of the disclosure involves a method comprising controlling by one or more computing devices an autonomous vehicle in accordance with a first control strategy; approaching an intersection with said vehicle; receiving by one or more computing devices sensor data from said autonomous vehicle corresponding to objects in the vicinity of said vehicle; determining whether another vehicle is at said intersection based on said sensor data; determining by said one or more computing devices whether said other vehicle or said autonomous vehicle has priority to proceed through said intersection; and activating a yield signal to indicate to said other vehicle that said autonomous vehicle is yielding said intersection. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0017]    The foregoing brief description will be understood more completely from the following detailed description of the exemplary drawings, in which: 
           [0018]      FIG. 1  is a functional block diagram illustrating an autonomous vehicle in accordance with an example embodiment. 
           [0019]      FIG. 2  is a diagram of an autonomous vehicle travelling along a highway in accordance with aspects of the disclosure. 
           [0020]      FIG. 3 a    is a diagram illustrating map data received by an autonomous vehicle from an external database. 
           [0021]      FIG. 3 b    is an enlarged view of a portion of the map data illustrated in  FIG. 3 a    including map data sensed by the autonomous vehicle in accordance with aspects of the disclosure. 
           [0022]      FIG. 4  is a flow chart of a first control method for an autonomous vehicle in accordance with aspects of the disclosure. 
           [0023]      FIG. 5  is a flow chart of a second control method for an autonomous vehicle in accordance with aspects of the disclosure. 
           [0024]      FIG. 6 a    is diagram of an autonomous vehicle travelling along a highway with a first traffic density in accordance with aspects of the disclosure. 
           [0025]      FIG. 6 b    is diagram of an autonomous vehicle travelling along a highway with a second traffic density in accordance with aspects of the disclosure. 
           [0026]      FIG. 7  is a top view of an autonomous vehicle in accordance with an example embodiment. 
           [0027]      FIG. 8  is a diagram of an autonomous vehicle travelling along a road that has buildings and obstructions adjacent to the road. 
       
    
    
     DETAILED DESCRIPTION 
       [0028]      FIG. 1  is a functional block diagram of a vehicle  100  in accordance with an example embodiment. Vehicle  100  has an external sensor system  110  that includes cameras  112 , radar  114 , and microphone  116 . Vehicle  100  also includes an internal sensor system  120  that includes speed sensor  122 , compass  124  and operational sensors  126  for measuring parameters such as engine temperature, tire pressure, oil pressure, battery charge, fuel level, and other operating conditions. Control systems  140  are provided to regulate the operation of vehicle  100  regarding speed, braking, turning, lights, wipers, horn, and other functions. A geographic positioning system  150  is provided that enables vehicle  100  to determine its geographic location. Vehicle  100  communicates with a navigational database  160  maintained in a computer system outside the vehicle  100  to obtain information about road locations, road conditions, speed limits, road hazards, and traffic conditions. Computer  170  within vehicle  100  receives data from geographic positioning system  150  and navigational database  160  to determine a turn-based routing strategy for driving the vehicle  100  from its current location to a selected destination. Computer  170  receives data from external sensor system  110  and calculates the movements of the vehicle  100  needed to safely execute each step of the routing strategy. Vehicle  100  can operate in a fully autonomous mode by giving instructions to control systems  140  or can operate in a semi-autonomous mode in which instructions are given to control systems  140  only in emergency situations. Vehicle  100  can also operate in an advisory mode in which vehicle  100  is under full control of a driver but provides recommendations and/or warnings to the driver relating to routing paths, potential hazards, and other items of interest. 
         [0029]      FIG. 2  illustrates vehicle  100  driving along highway  200  including left lane  202 , center lane  204 , and right lane  206 . Other-vehicles  220 ,  230  and  240  are also travelling along highway  200  in the same direction of travel as vehicle  100 . Computer  170  uses data from external sensor system  110  to detect the other-vehicles  220 ,  230  and  240 , to determine their relative positions to vehicle  100  and to identify their blind spots  222 ,  232  and  242 . Other-vehicle  220  and the vehicle  100  are both in the left lane  202  and other-vehicle  220  is in front of vehicle  100 . Computer  170  uses speed information from internal sensor system  120  to calculate a safe following distance  260  from other-vehicle  220 . In the example of  FIG. 2 , the routing strategy calculated by computer  170  requires vehicle  100  to exit the highway  200  at ramp  270 . In preparation for exiting the highway  200 , computer  170  calculates a travel path  280  for vehicle  100  to move from the left lane  202  to the right lane  206  while avoiding the other-vehicles  220 ,  230 , and  240  and their respective blind spots  222 ,  232  and  242 . 
         [0030]      FIG. 3 a    illustrates map  300  received by computer  170  from navigational database  160 . Map  300  includes the location and orientation of road network  310 . In the example shown, vehicle  100  is travelling along route  320  calculated by computer  170  or, alternatively, calculated by a computer (not shown) external to vehicle  100  associated with the navigational database  160 .  FIG. 3 b    illustrates an enlarged view of one portion of road network  310  and route  320 . Fundamental navigational priorities such as direction of travel, target speed and lane selection are made with respect to data received from navigational database  160 . Current global positioning system (GPS) data has a margin of error that does not allow for absolute accuracy of vehicle position and road location. Therefore, referring back to  FIG. 2 , computer  170  uses data from external sensor system  110  to detect instance of road features  330  such as lane lines  332 , navigational markers  334 , and pavement edges  336  to control the fine positioning of vehicle  100 . Computer  170  calculates the GPS coordinates of detected instances of road features  330 , identifies corresponding map elements  340 , and compares the location of road features  330  and map elements  340 .  FIG. 3 b    is an enlarged view of a portion of map  300  from  FIG. 3 a    that shows a map region  350  in which there is a significant discrepancy between road features  330  and map elements  340  as might occur during a temporary detour. As discussed below, significant differences between the calculated position of road features  330  and map elements  340  will cause computer  170  to adjust a routing strategy for vehicle  100 . 
         [0031]    In an alternative embodiment, road features  330  and map elements  340  can relate to characteristics about the road surface such as the surface material (dirt, gravel, concrete, asphalt). In another alternative embodiment, road features  330  and map elements  340  can relate to transient conditions that apply to an area of the road such as traffic congestion or weather conditions (rain, snow, high winds). 
         [0032]      FIG. 4  illustrates an example flow chart  400  in accordance with some aspects of the disclosure discussed above. In block  402 , computer  170  adopts a default control strategy for vehicle  100 . The default control strategy includes a set of rules that will apply when there is a high degree of correlation between road features  330  and map elements  340 . For example, under the default control strategy the computer  170  follows a routing path calculated based on the GPS location of vehicle  100  with respect to road network  310  on map  300 . Vehicle  100  does not cross lane lines  332  or pavement edges  336  except during a lane change operation. Vehicle target speed is set based on speed limit information for road network  310  contained in navigational database  160 , except where user preferences have determined that the vehicle should travel a set interval above or below the speed limit. The minimum spacing between vehicle  100  to surrounding vehicles is set to a standard interval. External sensor system  110  operates in a standard mode in which the sensors scan in a standard pattern and at a standard refresh rate. 
         [0033]    In block  404 , computer  170  selects a preferred road feature  330  (such as lane lines  332 ) and determines its respective location. In block  406 , computer  170  determines the location of the selected instance of the road feature  330  and in block  408  compares this with the location of a corresponding map element  340 . In block  410 , computer  170  determines a correlation rate between the location of road feature  330  and corresponding map element  340 . In block  412 , computer  170  determines whether the correlation rate exceeds a predetermined value. If not, computer  170  adopts an alternative control strategy according to block  414  and reverts to block  404  to repeat the process described above. If the correlation rate is above the predetermined value, computer maintains the default control strategy according to block  416  and reverts to block  404  to repeat the process. 
         [0034]    The correlation rate can be determined based on a wide variety of factors. For example, in reference to  FIG. 3 b    computer  170  can calculate the distance between road feature  330  and map element  340  at data points  370 ,  372 ,  374 ,  376 , and  378  along map  300 . If the distance at each point exceeds a defined value, computer  170  will determine that the correlation rate is below the predetermined value. If this condition is reproduced over successive data points or over a significant number of data points along a defined interval, computer  170  will adopt the alternative control strategy. There may also be locations in which road features  330  are not detectable by the external sensor system  110 . For example, lane lines  332  may be faded or covered with snow. Pavement edges  334  may be also covered with snow or disguised by adjacent debris. Data points at which no correlation can be found between road features  330  and map elements  340  could also be treated as falling below the correlation rate even though a specific calculation cannot be made. 
         [0035]    In one embodiment of the disclosure, only one of the road features  330 , such as lane lines  332 , are used to determine the correlation between road features  330  and map elements  340 . In other embodiments of the disclosure, the correlation rate is determined based on multiple instances of the road features  330  such as lane lines  332  and pavement edges  336 . In yet another embodiment of the disclosure, the individual correlation between one type of road feature  330  and map element  340 , such as lane lines  332 , is weighted differently than the correlation between other road features  330  and map elements  340 , such as pavement edges  334 , when determining an overall correlation rate. This would apply in situations where the favored road feature (in this case, lane lines  332 ) is deemed a more reliable tool for verification of the location of vehicle  100  relative to road network  310 . 
         [0036]      FIG. 5  illustrates an example flow chart  500  for the alternative control strategy, which includes multiple protocols depending upon the situation determined by computer  170 . In block  502 , computer  170  has adopted the alternative control strategy after following the process outlined in  FIG. 4 . In block  504 , computer  170  selects an alternative road feature  330  (such as pavement edges  336 ) and determines its respective location in block  506 . In block  508 , computer  170  compares the location of the selected road feature  330  to a corresponding map element  340  and determines a correlation rate in block  510 . In block  512 , computer  170  determines whether the correlation rate falls above a predetermined value. If so, computer  170  adopts a first protocol for alternative control strategy according to block  514 . If not, computer  170  adopts a second protocol for the alternative control strategy according to block  516 . 
         [0037]    In the first protocol, computer  170  relies on a secondary road feature  330  (such as pavement edges  336 ) for verification of the location of road network  310  relative to the vehicle  100  and for verification of the position of vehicle  100  within a lane on a roadway (such as the left lane  202  in highway  200 , as shown in  FIG. 2 ). In a further embodiment, computer  170  in the first protocol may continue to determine a correlation rate for the preferred road feature  330  selected according to the process outlined in  FIG. 4  and, if the correlation rate exceeds a predetermined value, return to the default control strategy. 
         [0038]    The second protocol is triggered when the computer is unable to reliably use information about alternative road features  330  to verify the position of the vehicle  100 . In this situation, computer  170  may use the position and trajectory of surrounding vehicles to verify the location of road network  310  and to establish the position of vehicle  100 . If adjacent vehicles have a trajectory consistent with road network  310  on map  300 , computer will operate on the assumption that other vehicles are within designated lanes in a roadway. If traffic density is not sufficiently dense (or is non-existent) such that computer  170  cannot reliably use it for lane verification, computer  170  will rely solely on GPS location relative to the road network  310  for navigational control purposes. 
         [0039]    In either control strategy discussed above, computer  170  will rely on typical hazard avoidance protocols to deal with unexpected lane closures, accidents, road hazards, etc. Computer  170  will also take directional cues from surrounding vehicles in situations where the detected road surface does not correlate with road network  310  but surrounding vehicles are following the detected road surface, or in situations where the path along road network  310  is blocked by a detected hazard but surrounding traffic is following a path off of the road network and off of the detected road surface. 
         [0040]    In accordance with another aspect of the disclosure, referring back to  FIG. 2  computer  170  uses data from external sensor system  110  to detect road hazard  650  on highway  600  and to detect shoulder areas  660  and  662  along highway  200 . Computer  170  also uses data from external sensor system  110  to detect hazard  670  in the shoulder area  660  along with structures  680  such as guard rails or bridge supports that interrupt shoulder areas  660 ,  662 . 
         [0041]    Computer  170  communicates with navigational database  160  regarding the location of hazards  650 ,  670  detected by external sensor system  110 . Navigational database  160  is simultaneously accessible by computer  170  and other computers in other vehicles and is updated with hazard-location information received by such computers to provide a real-time map of transient hazards. In a further embodiment, navigational database  160  sends a request to computer  170  to validate the location of hazards  650 ,  670  detected by another vehicle. Computer  170  uses external sensor system  110  to detect the presence or absence of hazards  650 ,  670  and sends a corresponding message to navigational database  160 . 
         [0042]    In accordance with another aspect of the disclosure,  FIG. 6 a    illustrates vehicle  100  driving along highway  600  including left lane  602 , center lane  604 , and right lane  606 . Surrounding vehicles  620  are also travelling along highway  600  in the same direction of travel as vehicle  100 . Computer  170  receives data from geographic positioning system  150  and navigational database  160  to determine a routing strategy for driving the vehicle  100  from its current location to a selected destination  610 . Computer  170  determines a lane-selection strategy based on the number of lanes  602 ,  604 ,  606  on highway  600 , the distance to destination  610 , and the speed of vehicle  100 . The lane-selection strategy gives a preference for the left lane  602  when vehicle  100  remains a significant distance from destination  610 . The lane-selection strategy also disfavors the right lane in areas along highway  600  with significant entrance ramps  622  and exit ramps  624 . The lane selection strategy defines first zone  630  where vehicle  100  should begin to attempt a first lane change maneuver into center lane  604 , and a second zone  632  where vehicle should begin to attempt a second lane change maneuver into right lane  606 . When vehicle  100  reaches first or second zone  630 ,  632 , computer  170  directs vehicle  100  to make a lane change maneuver as soon as a safe path is available, which could include decreasing or increasing the speed of vehicle  100  to put it in a position where a safe path is available. If vehicle passes through a zone  630 ,  632  without being able to successfully make a lane change maneuver, vehicle  100  will continue to attempt a lane change maneuver until it is no longer possible to reach destination  610  at which point the computer  170  will calculate a revised routing strategy for vehicle  100 . 
         [0043]    Computer  170  adapts the lane selection strategy in real time based on information about surrounding vehicles  620 . Computer  170  calculates a traffic density measurement based on the number and spacing of surrounding vehicles  620  in the vicinity of vehicle  100 . Computer  170  also evaluates the number and complexity of potential lane change pathways in the vicinity of vehicle  100  to determine a freedom of movement factor for vehicle  100 . Depending upon the traffic density measurement, the freedom of movement factor, or both, computer  170  evaluates whether to accelerate the lane change maneuver. For example, when traffic density is heavy and freedom of movement limited for vehicle  100 , as shown in  FIG. 7 b   , computer  170  may locate first and second zones  734  and  736  farther from destination  710  to give vehicle  100  more time to identify a safe path to maneuver. This is particularly useful when surrounding vehicles  620  are following each other at a distance that does not allow for a safe lane change between them. 
         [0044]    In another aspect of the disclosure as shown in  FIG. 2 , computer  170  uses data from external sensor system  110  to detect the other-vehicles  220 ,  230 , and  240  and to categorize them based on size and width into categories such as “car”, “passenger truck” and “semi-trailer truck.” In  FIG. 2 , other-vehicles  220  and  230  are passenger cars and other-vehicle  240  is a semi-trailer truck, i.e. a large vehicle. In addition to identifying the blind spots  222 ,  232  and  242 , computer  170  also identifies hazard zones  250  that apply only to particular vehicle categories and only in particular circumstances. For example, in  FIG. 2  computer  170  has identified the hazard zones  250  for other-vehicle  240  that represent areas where significant rain, standing water, and/or snow will be thrown from the tires of a typical semi-trailer truck. Based on information about weather and road conditions from navigational database  160 , road conditions detected by external sensor system  110 , or other sources, computer  170  determines whether the hazard zones  250  are active and should be avoided. 
         [0045]      FIG. 7  illustrates a top view of vehicle  100  including radar sensors  710  and cameras  720 . Because a vehicle that is driven under autonomous control will likely have behavior patterns different from a driver-controlled vehicle, it is important to have a signal visible to other drivers that indicates when vehicle  100  is under autonomous control. This is especially valuable for nighttime driving when it may not be apparent that no one is in the driver&#39;s seat, or for situations in which a person is in the driver&#39;s seat but the vehicle  100  is under autonomous control. For that purpose, warning light  730  is provided and is placed in a location distinct from headlamps  740 , turn signals  750 , or brake lights  760 . Preferably, warning light  730  is of a color other than red, yellow, or white to further distinguish it from normal operating lights/signals  740 ,  750  and  760 . In one embodiment, warning light can comprise an embedded light emitting diode (LED) located within a laminated glass windshield  770  and/or laminated glass backlight  780  of vehicle  100 . 
         [0046]    One of the complexities of autonomous control of vehicle  100  arises in negotiating the right-of-way between vehicles. Driver-controlled vehicles often perceive ambiguity when following the rules for determining which vehicle has the right of way. For example, at a four-way stop two vehicles may each perceive that they arrived at an intersection first. Or one vehicle may believe that all vehicles arrived at the same time but another vehicle perceived that one of the vehicles was actually the first to arrive. These situations are often resolved by drivers giving a visual signal that they are yielding the right of way to another driver, such as with a hand wave. To handle this situation when vehicle  100  is under autonomous control, yield signal  790  is included on vehicle  100 . Computer  170  follows a defined rule set for determining when to yield a right-of-way and activates yield signal  790  when it is waiting for the other vehicle(s) to proceed. Yield signal  790  can be a visual signal such as a light, an electronic signal (such as a radio-frequency signal) that can be detected by other vehicles, or a combination of both. 
         [0047]    In accordance with another aspect of the disclosure,  FIG. 8  illustrates vehicle  100  driving along road  800 . Road  810  crosses road  800  at intersection  820 . Buildings  830  are located along the sides of road  810  and  820 . Computer  170  uses data from external sensor system  110  to detect approaching-vehicle  840 . However, external sensor system  110  cannot detect hidden-vehicle  850  travelling along road  810  due to interference from one or more buildings  830 . Remote-sensor  860  is mounted on a fixed structure  870  (such as a traffic signal  872 ) near intersection  820  and in a position that gives an unobstructed view along roads  800  and  810 . Computer  170  uses data from remote-sensor  860  to determine the position and trajectory of hidden-vehicle  850 . This information is used as needed by computer  170  to control the vehicle  100  and avoid a collision with hidden-vehicle  850 . For example, if vehicle  100  is approaching intersection  820  with a green light on traffic signal  872 , computer  170  will direct the vehicle  100  to proceed through intersection  820 . However, if hidden-vehicle  850  is approaching intersection  820  at a speed or trajectory inconsistent with a slowing or stopping behavior, computer  170  will direct vehicle to stop short of intersection  820  until it is determined that hidden-vehicle  850  will successfully stop at intersection  820  or has passed through intersection  820 . 
         [0048]    The appended claims have been particularly shown and described with reference to the foregoing embodiments, which are merely illustrative of the best modes for carrying out the invention defined by the appended claims. It should be understood by those skilled in the art that various alternatives to the embodiments described herein may be employed in practicing the invention defined by the appended claims without departing from the spirit and scope of the invention as defined in claims. The embodiments should be understood to include all novel and non-obvious combinations of elements described herein, and claims may be presented in this or a later application to any novel and non-obvious combination of these elements. Moreover, the foregoing embodiments are illustrative, and no single feature or element is essential to all possible combinations that may be claimed in this or a later application. 
         [0049]    With regard to the processes, 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 could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. In other words, the descriptions of processes described herein are provided for illustrating certain embodiments and should in no way be construed to limit the appended claims. 
         [0050]    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. 
         [0051]    All terms used in the claims are intended to be given their broadest reasonable constructions and their ordinary meanings as understood by those skilled in the art unless an explicit indication to the contrary is 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.