Patent Publication Number: US-2023152813-A1

Title: Autonomous vehicle routing based upon spatiotemporal factors

Description:
RELATED APPLICATION 
     This application is a continuation of U.S. Pat. Application No. 16/280,415, filed on Feb. 20, 2019, and entitled “AUTONOMOUS VEHICLE ROUTING BASED UPON SPATIOTEMPORAL FACTORS”, the entirety of which is incorporated herein by reference. 
    
    
     BACKGROUND 
     An autonomous vehicle is a motorized vehicle that can operate for extended distances and/or periods of time without a human operator (i.e., a driver). An exemplary autonomous vehicle includes a plurality of sensor systems, such as but not limited to, a lidar sensor system, a camera sensor system, and a radar sensor system, amongst others. The autonomous vehicle operates based upon sensor data output by the sensor systems. 
     Typically, an autonomous vehicle operates autonomously (i.e., without conduction by the human operator) along a route from an origin location to a destination location. However, in certain driving scenarios, the autonomous vehicle may be caused to cease operating autonomously and instead be controlled by a human. Further, in some situations, the autonomous vehicle may be caused to perform an unplanned maneuver along the route in order to arrive at the destination location. Furthermore, the autonomous vehicle may also be subjected to events along the route that, while not altering behavior of the autonomous vehicle itself, may still be undesirable. 
     Conventionally, autonomous vehicles tend to be routed from an origin location to a destination location based upon predefined metrics, such as travel time between the origin location and the destination location, travel distance between the origin location and the destination location, etc. Autonomous vehicles may also be routed based on pre-defined operational factors such as distance to or distance from a charging station or a gas station. Using only these predefined metrics and factors to determine a route, however, may result in a suboptimal experience for a passenger in the autonomous vehicle. 
     SUMMARY 
     The following is a brief summary of subject matter that is described in greater detail herein. This summary is not intended to be limiting as to the scope of the claims. 
     Described herein are various technologies that pertain to routing of an autonomous vehicle based upon a risk of the autonomous vehicle undergoing an operation-influencing event. More specifically, described herein is a computer-implemented spatiotemporal statistical model that is configured to output a score that is indicative of a likelihood of the autonomous vehicle underdoing an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor along a route. The autonomous vehicle (or a separate computing system) may identify the route for the autonomous vehicle to follow from an origin location to a destination location based upon the score. The autonomous vehicle may then follow the route from the origin location to the destination location. 
     In operation, a computing system receives an origin location of an autonomous vehicle and a destination location of the autonomous vehicle. In one embodiment, the computing system is comprised by the autonomous vehicle. In another embodiment, the computing system is separate from the autonomous vehicle (e.g., a server computing device). 
     The computing system then identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a computer-implemented spatiotemporal statistical model. The spatiotemporal statistical model is generated based upon historical data from autonomous vehicles when the autonomous vehicles undergo operation-influencing events while the autonomous vehicles encounter spatiotemporal factors in driving environments. The historical data comprises indications of geographic locations traversed by the autonomous vehicles, indications of spatiotemporal factors in the geographic locations, and times at which the autonomous vehicles encountered the spatiotemporal factors. 
     Exemplary operation-influencing events include a human operator taking control of the autonomous vehicle (either from within the autonomous vehicle or from a remote station), deceleration of an autonomous vehicle exceeding a threshold, a change in deceleration (“jerk”) of an autonomous vehicle exceeding a threshold, a change in yaw rate of an autonomous vehicle exceeding a threshold, an unplanned maneuver of an autonomous vehicle while traversing a route, an object being within a threshold distance from of an autonomous vehicle, amongst others. 
     Exemplary spatiotemporal factors include an incidence of an emergency vehicle (e.g., a fire trucks, a police car, etc.), an incidence of steam (whether natural or human generated, for instance, by manhole covers), an incidence of double-parked vehicles, an incidence of a high volume of pedestrians, an incidence of a high volumes of cyclists, an incidence of a high volume of vehicular traffic, an incidence of an inclement weather condition (e.g., rain, strong wind, fog), an incidence of debris in a road (e.g., traffic cones, garbage), an incidence of lighting conditions primarily determined by a time of day (e.g., bright sunlight, low sunlight, no sunlight, diffuse sunlight), incidence of lighting conditions primarily determined by made-made objects (e.g., high volumes of fluorescent light relative to ambient light), an incidence of vegetation occluding a roadway (e.g., overhanging trees), an incidence of roadway features obstructing the sight lines of the autonomous vehicle (e.g., signs in the median of the road, garbage cans, etc.), an incidence of roadway features that cannot be navigated through without accommodation (e.g., potholes, road construction, sharp turns, intersections with high curvatures), an incidence of high volumes of other autonomous vehicles, etc., wherein such factors occur at certain locations and times. 
     The computer-implemented spatiotemporal statistical model takes, as input, a location, a time, and a direction of travel of the autonomous vehicle along a candidate route from the origin location to the destination location. The spatiotemporal statistical model outputs a score that is indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor along the candidate route. In an example, the computing system may select the route such that the expected travel time to the destination location is balanced against a likelihood of the autonomous vehicle undergoing an operation-influencing event during the route. The computing system identifies the route based upon the score. 
     When the computing system is separate from the autonomous vehicle, the computing system transmits the route to the autonomous vehicle over a network. The autonomous vehicle then controls at least one of a vehicle propulsion system of the autonomous vehicle, a braking system of the autonomous vehicle, or a steering system of the autonomous vehicle such that the autonomous vehicle follows the route from the origin location to the destination location responsive to the computing system identifying the route. 
     The above-described technologies present various advantages over conventional computer-implemented routing approaches for autonomous vehicles. First, an autonomous vehicle may employ the above-described technologies in order to balance a likelihood of the autonomous vehicle undergoing an operation-influencing event with spatiotemporal factors, as well as other factors (e.g., travel time, travel distance, etc.) in a driving environment of the autonomous vehicle. Second, even when a human operator is not present in the autonomous vehicle, the autonomous vehicle may utilize the above-described technologies in order to avoid potentially challenging driving scenarios. Third, the above-described technologies may also be employed to increase exposure of the autonomous vehicle to spatiotemporal factors that cause the autonomous vehicle to undergo operation-influencing events in order to assist in training the autonomous vehicle to better navigate driving environments. 
     The above summary presents a simplified summary in order to provide a basic understanding of some aspects of the systems and/or methods discussed herein. This summary is not an extensive overview of the systems and/or methods discussed herein. It is not intended to identify key/critical elements or to delineate the scope of such systems and/or methods. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a functional block diagram of an exemplary computing system that is configured to construct a spatiotemporal statistical model. 
         FIG.  2    illustrates a functional block diagram of an exemplary computing system that is configured to identify a route between an origin location and a destination location based upon output of a spatiotemporal statistical model. 
         FIG.  3    illustrates a functional block diagram of an exemplary autonomous vehicle that is configured to identify a route between an origin location and a destination location based upon output of a spatiotemporal statistical model. 
         FIG.  4    is a schematic that illustrates potential routes between an origin location and a destination location. 
         FIG.  5    is a schematic that illustrates a weighted directed graph that corresponds to the potential routes depicted in  FIG.  7   . 
         FIG.  6    is a schematic that illustrates another weighted directed graph that corresponds to the potential routes depicted in  FIG.  7   . 
         FIG.  7    is a schematic that illustrates selection of a route from amongst a plurality of potential routes based upon weights assigned to edges of a weighted directed graph. 
         FIG.  8    is a schematic that illustrates the autonomous vehicle travelling along one of the potential routes depicted in  FIG.  7   . 
         FIG.  9    is a flow diagram that illustrates an exemplary methodology for generating a spatiotemporal statistical model that, when generated, is configured to output a score that is indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event. 
         FIG.  10    is a flow diagram that illustrates an exemplary methodology for identifying a route for an autonomous vehicle to follow from an origin location to a destination location. 
         FIG.  11    is a flow diagram that illustrates an exemplary methodology for identifying and following a route from an origin location to a destination location. 
         FIG.  12    illustrates an exemplary computing device. 
     
    
    
     DETAILED DESCRIPTION 
     Various technologies pertaining to routing an autonomous vehicle based upon a risk of the autonomous vehicle undergoing an operation-influencing event due to the autonomous vehicle encountering spatiotemporal factors are now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. It may be evident, however, that such aspect(s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more aspects. Further, it is to be understood that functionality that is described as being carried out by certain system components may be performed by multiple components. Similarly, for instance, a component may be configured to perform functionality that is described as being carried out by multiple components. 
     Moreover, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise, or clear from the context, the phrase “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, the phrase “X employs A or B” is satisfied by any of the following instances: X employs A; X employs B; or X employs both A and B. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from the context to be directed to a singular form. 
     As used herein, the terms “component,” “application,” and “system” are intended to encompass computer-readable data storage that is configured with computer-executable instructions that cause certain functionality to be performed when executed by a processor. The computer-executable instructions may include a routine, a function, or the like. It is also to be understood that a component, application, or system may be localized on a single device or distributed across several devices. Further, as used herein, the term “exemplary” is intended to mean “serving as an illustration or example of something.” 
     With reference now to  FIG.  1   , an exemplary computing system  100  is illustrated. The computing system  100  includes a processor  102  and memory  104 , wherein the memory  104  has a model generation application  106  loaded therein. As will be described in greater detail below, the model generation application  106  (when executed by the processor  102 ) is configured to generate computer-implemented spatiotemporal statistical models (described below). 
     The computing system  100  also includes a data store  108 . The data store  108  includes historical data  110  from autonomous vehicles when the autonomous vehicles undergo operation-influencing events. As such, the historical data  110  comprises indications of geographic locations traversed by the autonomous vehicles, indications of spatiotemporal factors in the geographic locations, and times at which the autonomous vehicles encountered the spatiotemporal factors. 
     The operation-influencing events may take many forms. Exemplary operation-influencing events include, but are not limited to: 1) a human operator in an autonomous vehicle taking control of the autonomous vehicle; 2) a remote human operator in an autonomous vehicle taking control of the autonomous vehicle; 3) the autonomous vehicle decelerating rapidly (e.g., the autonomous vehicle has a sensed deceleration that is above a threshold); 4) rapid changes in acceleration or deceleration (such that the autonomous vehicle “jerks”); 5) rapid changes in yaw rates (e.g., a yaw rate of the autonomous vehicle is detected as exceeding a threshold); 6) an autonomous vehicle performing an unplanned maneuver along a route (such as swerving to avoid a large puddle of water along the route when the planned maneuver was to continue straight along an area in which the large puddle of water was located); and 7) the autonomous vehicle coming within a threshold distance of an object. These operation-influencing events correspond to one or more predefined spatiotemporal factors, examples of which are described below. 
     Exemplary spatiotemporal factors may take many different forms, in terms of how the spatiotemporal factors are modeled as correlating to a likelihood of an operation-influencing event and how the spatiotemporal factors causally relate to a variable of interest. 
     In terms of how the spatiotemporal factors may enter into a computer-implemented spatiotemporal statistical model, the spatiotemporal factors may be modeled as fixed effects (if the effects of the levels of the spatiotemporal factor are calculated so as to maximize the likelihood of the model) or modeled as random effects (if the effect of the levels of the spatiotemporal factor are calculated so as to maximize the likelihood of the model with shrinkage, i.e., where a predicted effect of a given level of a factor is a weighted sum of the predicted effect of that level, individually, and that of all other levels of that spatiotemporal factor, according to a covariance matrix). These spatiotemporal factors can be related to each other and to the operation-influencing events with other types of models besides a mixed model, e.g. a Gaussian process; the description above in terms of fixed and random effects is intended merely to demonstrate that the spatiotemporal factors may enter into a model in different forms. 
     In terms of how the spatiotemporal factors causally relate to the predicted variable of interest, the spatiotemporal factors may, for example, interfere with recognition of objects in driving environments of autonomous vehicles, or may interfere with physical progress of the vehicle, for example, by inhibiting freedom of movement of the autonomous vehicles about driving environments. The spatiotemporal factors may occur at or between intersections of roads or other areas in the driving environments of the autonomous vehicles. 
     Exemplary spatiotemporal factors include but are not limited to the following: 1) existence of emergency vehicles (e.g., fire trucks, police cars, etc.), where the emergency vehicles may be found at a time and location due to a location of a fire station, a police station, etc., due to repeated requests for emergency services at the location, due to established patterns of driving by such vehicles, etc.; 2) existence of steam (whether natural or human-generated, for example, steam emitted from manhole covers); 3) existence double parked vehicles; 4) existence of pedestrians (e.g., pedestrians may be clustered together and may be associated with an increased likelihood of an operation-influencing event because of, for example, presenting an especially challenging visual image to perception systems of the autonomous vehicle in such a way as to be associated with an increased likelihood of possible misidentification by the autonomous vehicle); 5) existence of a high volume of cyclists; 6) existence of a high volume of vehicular traffic; 7) existence of an inclement weather condition (e.g., rain, strong wind, fog) which may affect perception of sensor systems of the autonomous vehicle; 8) existence of debris in a road, such as traffic cones or garbage; 9) lighting conditions primarily caused by a time of day (e.g., bright sunlight, low sunlight, no sunlight, diffuse sunlight), where a lighting condition may affect perception of sensor systems of the autonomous vehicle; 10) lighting conditions primarily caused by man-made objects, such as fluorescent lights from street-illuminating devices and/or artificial signage, where such lighting conditions may affect perception of sensor systems of the autonomous vehicle; 11) vegetation occluding a roadway (e.g., overhanging trees, overhanging vines, etc.); 12) roadway features obstructing sight lines of the autonomous vehicle (e.g., signs in a median of the road, garbage cans, etc.); 13) roadway features that cannot be navigated through without accommodation (e.g., potholes, road construction, sharp turns, intersections with high curvatures, narrow roads, roads without marked center lines, etc.); and 14) high volumes of other autonomous vehicles, which may affect perception of sensor systems of the autonomous vehicle, for example, by lidar systems of the autonomous vehicles interfering with one another. In the historical data  110 , the spatiotemporal factors correspond to operation-influencing events. 
     The times in the historical data  110  may take many forms. In a first example, a time may be a time of day (e.g., 11:00 A.M.). In a second example, the time may be a time of a day of a week (e.g., 11:00 A.M. on a Thursday). In a third example, the time may be a time of day during a month (e.g., 11:00 A.M. on a day in January). 
     The data store  108  also includes a computer-implemented spatiotemporal statistical model  112  generated by the model generation application  106 . The spatiotemporal statistical model  112  may be learned by way of computer-implemented machine learning systems. In general, the spatiotemporal statistical model  112  is configured to take, as input, a location, a time, and a direction of travel of an autonomous vehicle along a candidate route from an origin location to a destination location. The time may be a time of day, a time of a day of a week, or a time of day during a month. The spatiotemporal statistical model  112  is configured to output a score that is indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor (such as one of the spatiotemporal factors identified above) along the candidate route. 
     In an embodiment, the computer-implemented spatiotemporal statistical model  112  may be or include a random effects model or a mixed effects model. In another embodiment, the spatiotemporal statistical model  112  may be or include a Bayesian hierarchical model, a random forest model, a Gaussian process model, or a neural network. When the spatiotemporal statistical model  112  comprises a neural network, the neural network may be or include an artificial neural network (ANN), a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), or another suitable neural network. 
     In an example, the spatiotemporal statistical model  112  may comprise nodes and edges, wherein the edges couple nodes in the spatiotemporal statistical model  112 . Each edge is assigned a learned weight, wherein the learned weight can be learned using a supervised or semi-supervised learning procedure. Accordingly, for instance, a learned weight assigned to an edge can be influenced by the historical data  110 . 
     Operation of the computing system  100  is now set forth. The model generation application  106 , utilizing any suitable machine-learning technologies, accesses the historical data  110  and generates the computer-implemented spatiotemporal statistical model  112  based upon the historical data  110 . In effect, then, the spatiotemporal statistical model is configured to model causal relationships between operation-influencing events and spatiotemporal factors in a geographic area. Subsequent to generating the spatiotemporal statistical model  112 , the model generation application  106  may update the spatiotemporal statistical model  112  when the historical data  110  is updated. Although the model generation application  106  has been described as generating a single spatiotemporal statistical model, it is to be understood that the model generation application  106  may generate many different spatiotemporal statistical models. 
     Referring now to  FIG.  2   , an exemplary computing system  200  that is configured to determine a route between an origin location and a destination location is illustrated. The computing system  200  includes a processor  202  and memory  204 , wherein the memory  204  has a routing application  206  loaded therein. As will be described in greater detail below, the routing application  206  (when executed by the processor  202 ) is generally configured to identify a route for an autonomous vehicle to follow from an origin location to a destination location. The routing application  206  may also be configured to identify a route that balances travel time or travel distance of the autonomous vehicle along the route with a risk of the autonomous vehicle undergoing an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor. 
     The computing system  200  may also include a data store  208 . The data store  208  comprises map data  210 . In general, the map data  210  comprises relatively highly detailed maps of driving environments of autonomous vehicles. For instance, the map data  210  can include locations of roadways, locations of lane boundaries of roadways, locations of stop signs and stop lights along roadways, locations of curbs, locations of storm grates, and so forth. The data store  208  further comprises the spatiotemporal statistical model  112 . As will be described in greater detail below, the routing application  206  identifies candidate routes for an autonomous vehicle to follow from an origin location to a destination location based upon the map data  210 , a time, and output of the spatiotemporal statistical model. 
     Turning now to  FIG.  3   , an exemplary autonomous vehicle  300  is illustrated, wherein the autonomous vehicle  300  is configured to identify a route to travel between an origin location and a destination location. The autonomous vehicle  300  can navigate about roadways without a human operator based upon sensor data (i.e., sensor signals) outputted by sensor systems of the autonomous vehicle  300 . The autonomous vehicle  300  includes sensor systems, namely, a first sensor system 1  302  through an Nth sensor system  304 . The sensor systems  302 - 304  are of different types and are arranged about the autonomous vehicle  300 . For example, the sensor systems  302 - 304  can include a lidar sensor system, a camera sensor (image) system, a radar sensor system, a satellite-based radio navigation sensor system (e.g., global positioning system (GPS) sensor systems), a sonar sensor system, an infrared sensor system, an accelerometer, a microphone sensor, and the like. The sensor systems  302 - 304  generate (i.e., output) sensor data. For instance, the radar sensor system can generate radar sensor data, the lidar sensor system can generate lidar sensor data, the camera sensor system can generate camera sensor data, etc. 
     The autonomous vehicle  300  further includes several mechanical systems that are used to effectuate appropriate motion of the autonomous vehicle  300 . For instance, the mechanical systems can include, but are not limited to, a vehicle propulsion system  306 , a braking system  308 , and a steering system  310 . The vehicle propulsion system  306  may be an electric motor, an internal combustion engine, or a combination thereof. The braking system  308  can include an engine brake, brake pads, actuators, and/or any other suitable componentry that is configured to assist in decelerating the autonomous vehicle  300 . The steering system  310  includes suitable componentry that is configured to control the direction of movement of the autonomous vehicle  300 . 
     The autonomous vehicle  300  further comprises the computing system  200 . While the computing system  200  is illustrated as being included in the autonomous vehicle  300 , it is to be understood that portions of the computing system  200  may reside on a remote computing device that is in communication with the autonomous vehicle. For instance, the routing application  206  and the spatiotemporal statistical model  112  can optionally be executed be a computing system that is remote from the autonomous vehicle  300 . In the example illustrated in  FIG.  3   , the memory  204  of the computing system  200  further includes an object detection application  312 , which is configured to receive sensor data output by the sensor system  302 - 304  and detect when an object in a driving environment of the autonomous vehicle  300  is within a threshold distance of the autonomous vehicle  300  based upon the sensor data. For instance, the object detection system  312  may detect that an object is within the threshold distance from the autonomous vehicle  300  based upon a lidar point cloud generated by a lidar sensor system of the autonomous vehicle  300 . 
     The memory  204  of the computing system can further include an object recognition application  314 , wherein the object recognition system  314  is configured to receive the sensor data output by the sensor systems  302  and recognize a type of object from amongst predefined types based upon the sensor data. For instance, the predefined types may include car, truck, bus, bicycle, pedestrian, static object, or unknown. The object recognition system  314  may also be configured to identify empty space in the driving environment of the autonomous vehicle  300 . 
     As illustrated in  FIG.  3   , the autonomous vehicle  300  may have a human operator  326  that rides in the autonomous vehicle  300 . It is contemplated that the autonomous vehicle  300  typically operates autonomously. However, in certain driving scenarios, the human operator  326  may take control over the autonomous vehicle  300 . Further, the human operator  326  may release control of the autonomous vehicle  300 , such that the autonomous vehicle operates autonomously. In an embodiment, the autonomous vehicle  300  may also include various components (not shown) that enable the autonomous vehicle  300  to be (optionally) operated by the human operator  326 . For instance, the components may include a driver seat, a steering wheel, a brake pedal, an acceleration pedal, a gear selector, mirrors, a speedometer, etc. In another exemplary embodiment, a computing system (not shown) can be in remote communication with the autonomous vehicle  300 , wherein an operator of the remote computing system can remotely take control of the autonomous vehicle  300  and can additionally release control of the autonomous vehicle  300  (such that the autonomous vehicle  300  returns to operating autonomously). 
     Operation of the autonomous vehicle  300  is now set forth. The computing system  200  receives an origin location of the autonomous vehicle  300  and a destination location of the autonomous vehicle  300 . For instance, the computing system  200  may receive the origin location and the destination location from a computing device operated by a passenger (or future passenger) of the autonomous vehicle  300 . Thus, the origin location and/or the destination location may be GPS coordinates. In another example, the origin location and/or the destination location may be street addresses. 
     Responsive to receiving the origin location and the destination location, the routing application  206  of the computing system  200  may generate candidate routes from the origin location to the destination location based upon the map data  210 . The routing application  206 , for example, can output a weighted directed graph based upon the candidate routes. The weighted directed graph comprises nodes and directed edges that couple at least some of the nodes. The nodes represent intersections that are traversed by the autonomous vehicle  300  when the autonomous vehicle  300  travels the candidate routes. The directed edges represent roads that connect intersections along the candidate routes. The routing application  206  assigns weights to the directed edges, wherein the weights may be indicative of costs to the autonomous vehicle  300  for traversing the roads and intersections. For instance, a weight assigned to a directed edge in the directed edges can be indicative of an amount of time required for the autonomous vehicle  300  to travel through an intersection. In another example, a weight assigned to a directed edge in the directed edges can be indicative of a distance between intersections. 
     The routing application  206  may input a location, a time, and direction of travel of the autonomous vehicle  300  along a candidate route between the origin location destination and the destination location to the computer-implemented spatiotemporal statistical model  112 . The spatiotemporal statistical model  112  outputs a score based upon the location, the time, and the direction of travel of the autonomous vehicle  300  along the candidate route, wherein the score is indicative of a likelihood that the autonomous vehicle  300  will undergo an operation-influencing event due the autonomous vehicle  300  encountering a spatiotemporal factor (e.g., from amongst the spatiotemporal factors identified above). The routing application  206  may repeat this process for different locations, times, and directions of travel of the autonomous vehicle  300  along candidate routes to generate a score for each location, time, and direction of travel along the candidate route. Further, the routing application  206  may repeat this process as the autonomous vehicle  300  travels, such that candidate routes can be updated, and scores can be updated. 
     Responsive to the spatiotemporal statistical model  112  outputting a score for an intersection, the routing application  206  updates a weight assigned to a directed edge in the weighted directed graph based upon the score. The routing application  206  can repeat this process for each score generated by the computer-implemented spatiotemporal statistical model  112 . In an example, the routing application  206  may increase a weight of an edge that is coupled to a node that represents an intersection when the score for the intersection output by the spatiotemporal statistical model  112  indicates a relatively high likelihood of occurrence an operation-influencing event at the intersection if the autonomous vehicle  300  were to follow the candidate route through the intersection. In another example, the routing application  206  may decrease a weight of an edge that is coupled to a node that represents an intersection when the score for the intersection output by the spatiotemporal statistical model  112  indicates a relatively low likelihood of occurrence of an operation-influencing event at the intersection if the autonomous vehicle  300  were to follow the candidate route through the intersection. Thus, the weights of the directed edges in the weighted directed graph are further based upon the likelihood that the autonomous vehicle  300  will undergo an operation-influencing event due to the autonomous vehicle  300  encountering spatiotemporal factors along the candidate routes. 
     In an exemplary embodiment, the routing application  206  may identify a route that the autonomous vehicle  300  is to follow from the origin location to the destination location by applying a shortest path algorithm to the weighted directed graph. For instance, the shortest path algorithm may be one of a Dijkstra’s algorithm, a Bellman-Ford algorithm, or a Floyd-Warshall algorithm. In an example, the route identified by the routing application  206  between the origin location and the destination location may minimize a likelihood that the autonomous vehicle  300  will undergo an operation-influencing event, while a candidate route that is not selected by the routing application  206  may minimize the travel time from the origin location to the destination location. 
     When the routing application  206  is executed on a remote computing system, the remote computing system transmits the identified route to the autonomous vehicle  300 . The computing system  200  of the autonomous vehicle  300  then controls at least one of the vehicle propulsion system  306 , the braking system  308 , or the steering system  310  such that the autonomous vehicle  300  follows the route from the origin location to the destination location. Additionally, the autonomous vehicle  300  may additionally utilize sensor data generated by the sensor systems  302 - 304  in order to follow the route from the origin location to the destination location. 
     In an embodiment, the routing application  206  can be configured to identify a route that maximizes a likelihood that the autonomous vehicle  300  will undergo an operation-influencing event due to the autonomous vehicle  300  encountering a spatiotemporal factor along the route. This may be advantageous in scenarios in which testing of autonomous vehicles is desirable. The routing application  206  can invert weights assigned to directed edges in the weighted directed graph in order to identify a route that maximizes a likelihood that the autonomous vehicle  300  will undergo the operation-influencing event due to the autonomous vehicle  300  encountering the spatiotemporal factor along the route. 
     The routing application  206  can overlay the scores output by the computer-implemented spatiotemporal statistical model  112  with the map data  210  in order to generate a heat map. The heat map may comprise representations of a driving environment of the autonomous vehicle  300 . A portion of the representation may be marked to indicate the likelihood of the operation-influencing event occurring at an area of the driving environment represented by the heat map. 
     Further, the routing application  206  can utilize the scores (described above) in order to optimize a loss function (i.e., an objective function). The loss function may be a linear or nonlinear function. The routing application  206  may utilize the loss function in order to identify the route. 
     In addition, the routing application  206  can specify a loss function that incorporates a business objective into a set of constraints. For example, the routing application  206  can utilize the loss function to identify a route that minimizes a likelihood of the autonomous vehicle  300  undergoing an operation-influencing event while increasing the travel time from the origin location to the destination location by no more than 5 minutes. A business objective may be incorporated into a set of constraints utilized by the routing application  206 . 
     The routing application  206  may specify a loss function that directly incorporates a business objective (i.e., a business priority). For example, the routing application  206  can utilize the loss function to identify a route that minimizes a weighted sum of a likelihood of the autonomous vehicle  300  undergoing an operation-influencing event in addition to the estimated time to arrival to the destination location. A business objective may be incorporated directly into a loss function utilized by the routing application  206 . 
     Still further, the routing application  206  can generate parametric tradeoff curves for each of the candidate routes based upon the scores and costs assigned to the candidate routes. The routing application  206  may generate the parametric tradeoff curves based upon one or more business objectives as well. 
       FIGS.  4 - 8    illustrate a specific example of operation of the systems and methods described above. Referring now to  FIG.  4   , an exemplary driving environment  400  is illustrated. The driving environment  400  includes the autonomous vehicle  300  described above. As illustrated in  FIG.  4   , the autonomous vehicle  300  is initially located at an origin location  402  in the driving environment  400 . In this example, the autonomous vehicle  300  is to traverse the driving environment  400  such that the autonomous vehicle  300  arrives at the destination location  404 . 
     The driving environment  400  includes a first candidate route  418  from the origin location  402  to the destination location  404 , a second candidate route  420  from the origin location  402  to the destination location  404 , and a third candidate route  422  from the origin location  402  to the destination location  404  (collectively, “the candidate routes  418 - 422 ”). The first candidate route  418  includes a first intersection  406  and a second intersection  408  that are traversed by the autonomous vehicle  300  when the autonomous vehicle  300  follows the first candidate route  418 . The second candidate route  420  includes a third intersection  410  and a fourth intersection  412  that are traversed by the autonomous vehicle  300  when the autonomous vehicle  300  follows the second candidate route  420 . The third candidate route  422  includes a fifth intersection  414  and a sixth intersection  416  that are traversed by the autonomous vehicle  300  when the autonomous vehicle  300  follows the third candidate route  422 . As shown in  FIG.  4   , the candidate routes  418 - 422  may vary in length. For instance, the first candidate route  418  may be a longer distance from the origin location  402  to the destination location  404  than the second candidate route  422 . 
     The driving environment  400  includes a plurality of pedestrians  424 -  426  (e.g., a spatiotemporal factor). The plurality of pedestrians  424 - 426  are closely clustered around the fourth intersection  412 . 
     Turning now to  FIG.  5   , an exemplary weighted directed graph  500  is illustrated. The routing application  206  can generate the weighted directed graph  500  based upon the map data  210 . The weighted directed graph  500  is based upon the driving environment  400  illustrated in  FIG.  4   . 
     As such, the weighted directed graph  500  includes an origin node  502  assigned to the origin location  402  of the autonomous vehicle  300  and a destination node  504  assigned to the destination location  404  of the autonomous vehicle  300 . The weighted directed graph  500  additionally includes a first node  506 , a second node  508 , a third node  510 , a fourth node  512 , a fifth node  514 , and a sixth node  516  (collectively, “the nodes  506 - 516 ”). Each node in the nodes  506 - 516  represents an intersection in the intersections  406 - 416 . For instance, the first node  506  represents the first intersection  406 , the second node  508  represents the second intersection  408 , and so forth. 
     The weighted directed graph  500  further includes directed edges (indicated by arrows in  FIG.  5   ), wherein each directed edge couples two nodes in the graph  500 . Each directed edge in the directed edges represents a path (i.e., a road) that connects an intersection in the driving environment  400  to another intersection in the driving environment  400 , the origin location  402  to an intersection in the driving environment  400 , or an intersection in the driving environment  400  to the destination location  404 . Each directed edge is assigned a weight in weights w 1  to w 9 , wherein the weight is indicative of the cost (e.g., a time cost, a distance cost, etc.) to the autonomous vehicle  300  in selecting a route that passes through an intersection in the intersections  406 - 416  of the driving environment. For instance, the weights w 1  to w 3  are indicative of a cost to the autonomous vehicle  300  in selecting the first candidate route  418  for the autonomous vehicle  300  to traverse from the origin location  402  to the destination location  404 . 
     With reference now to  FIG.  6   , an exemplary weighted directed graph  600  is illustrated. The routing application  206  generates the weighted directed graph  600 . The weighted directed graph  600  includes the origin node  502 , the destination node  504 , the nodes  506 - 516 , and the directed edges depicted in  FIG.  5   . However, as shown in  FIG.  6   , the routing application  206  has modified each of the weights w 1  to w 9  to generate modified weights w′ 1  to w′ 9 , respectively, thereby generating the weighted directed graph  600 . Each of the modified weights w′ 1  to w′ 9  is based upon one or more scores (described above) output by the spatiotemporal statistical model  112 . For instance, the weight w′ 5  may be based upon both a travel time (or a travel distance) cost assigned to the fourth intersection  412  as well as a likelihood that the autonomous vehicle  300  will undergo an operation-influencing event near the fourth intersection  412  due to spatiotemporal factors (e.g., the plurality of pedestrians  424 - 428 ). 
     Referring now to  FIG.  7   , a shortest path  702  (illustrated via dotted lines in  FIG.  7   ) through the weighted directed graph  600  shown in  FIG.  6    is illustrated. The routing application  206  determines the shortest path  702  through the graph by applying a shortest path algorithm to the weighted directed graph  600 . For instance, the shortest path algorithm may be one of a Dijkstra’s algorithm, a Bellman-Ford algorithm, or a Floyd-Warshall algorithm. 
     As shown in  FIG.  7   , the shortest path  702  includes the first node  506  and the second node  508  (as well as the origin node  502  and the destination node  504 ). Thus, the shortest path  702  may correspond to the first candidate route  418  depicted in  FIG.  4   . Notably, the first candidate route  418  may not be the shortest route (from a time or a distance perspective) in the driving environment  400 . Rather, the routing application  206  can identify the first candidate route  418  as the route to follow as the first candidate route  418  has the lowest cost to the autonomous vehicle  300  when likelihood of the autonomous vehicle  300  undergoing an operation-influencing event due to the autonomous vehicle  300  encountering a spatiotemporal factor (e.g., the plurality of pedestrians  724 - 728 ) is additionally considered along with travel time or travel distance of the autonomous vehicle  300 . 
     Turning now to  FIG.  8   , an updated view of the driving environment  400  depicted in  FIG.  4    is illustrated. After the routing application  206  determines the shortest path  702  through the weighted directed graph  600 , the autonomous vehicle  300  may base its operation on the shortest path  702 . More specifically, as the shortest path  702  includes the first node  506  (which represents the first intersection  406 ) and the second node  508  (which represents the second intersection  408 ), the autonomous vehicle  300  may control at least one of the vehicle propulsion system  306 , the braking system  308 , or the steering system  310  such that the autonomous vehicle  300  follows the first candidate route  418  (now referred to as “the route 802”) from the origin location  402  through the first intersection  406  and the second intersection  408  in order to arrive at the destination location  404 . 
       FIGS.  9 - 11    illustrate exemplary methodologies relating to routing an autonomous vehicle based upon a risk of the autonomous vehicle undergoing an operation-influencing event due to the autonomous vehicle encountering spatiotemporal factors along a route. While the methodologies are shown and described as being a series of acts that are performed in a sequence, it is to be understood and appreciated that the methodologies are not limited by the order of the sequence. For example, some acts can occur in a different order than what is described herein. In addition, an act can occur concurrently with another act. Further, in some instances, not all acts may be required to implement a methodology described herein. 
     Moreover, the acts described herein may be computer-executable instructions that can be implemented by one or more processors and/or stored on a computer-readable medium or media. The computer-executable instructions can include a routine, a sub-routine, programs, a thread of execution, and/or the like. Still further, results of acts of the methodologies can be stored in a computer-readable medium, displayed on a display device, and/or the like. 
     With reference to  FIG.  9   , a methodology  900  performed by a computing system for generating a spatiotemporal statistical model that outputs a score indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event is illustrated. The methodology  900  begins at  902 , and at  904  the computing system accesses historical data from a data store. The historical data comprises indications of geographic locations traversed by autonomous vehicles, indications of spatiotemporal factors in the geographic locations when the autonomous vehicles undergo operation-influencing events, and times at which the autonomous vehicles encountered the spatiotemporal factors. 
     At  906 , the computing system generates a computer-implemented spatiotemporal statistical model based upon the historical data. The spatiotemporal statistical model takes, as input, a location, a time, and a direction of travel of an autonomous vehicle along a candidate route. The spatiotemporal statistical model is configured to output a score that is indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor along the candidate route. The methodology  900  concludes at  908 . 
     Turning to  FIG.  10   , a methodology  1000  performed by a computing system for identifying a route for an autonomous vehicle to follow from an origin location to a destination location is illustrated. The methodology  1000  begins at  1002 , and at  1004 , the computing system receives an origin location of an autonomous vehicle and a destination location of the autonomous vehicle. 
     At  1006 , the computing system identifies a route for the autonomous vehicle to follow based upon output of a spatiotemporal statistical model. The spatiotemporal statistical model takes, as input, a location, a time, and a direction of travel of an autonomous vehicle along a candidate route. The output of the spatiotemporal statistical model is a score that is indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor along the candidate route. The computing system identifies the route based in part on the score. 
     At  1008 , the computing system transmits the route to the autonomous vehicle over a network. The autonomous vehicle then follows the route from the origin location to the destination location. The methodology  1000  concludes at  1010 . 
     With reference to  FIG.  11   , a methodology  1100  performed by an autonomous vehicle for identifying and following a route from an origin location to a destination location is illustrated. The methodology  1100  begins at  1102 , and at  1104 , the autonomous vehicle determines an origin location of the autonomous vehicle and a destination location of the autonomous vehicle. 
     At  1106 , the autonomous vehicle identifies a route for the autonomous vehicle to follow based upon output of a spatiotemporal statistical model. The spatiotemporal statistical model takes, as input, a location, a time, and a direction of travel of the autonomous vehicle along a candidate route. The output of the spatiotemporal statistical model is a score that is indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor along the candidate route. The autonomous vehicle identifies the route based in part on the score. 
     At  1108 , the autonomous vehicle controls at least one of a vehicle propulsion system of the autonomous vehicle, a braking system of the autonomous vehicle, or a steering system of the autonomous vehicle such that the autonomous vehicle follows the route from the origin location to the destination location. The methodology  1100  concludes at  1110 . 
     Referring now to  FIG.  12   , a high-level illustration of an exemplary computing device  1200  that can be used in accordance with the systems and methodologies disclosed herein is illustrated. For instance, the computing device  1200  may be or include the computing system  100  or the computing system  200 . The computing device  1200  includes at least one processor  1202  that executes instructions that are stored in a memory  1204 . The instructions may be, for instance, instructions for implementing functionality described as being carried out by one or more systems discussed above or instructions for implementing one or more of the methods described above. The processor  1202  may be a graphics processing unit (GPU), a plurality of GPUs, a central processing unit (CPU), a plurality of CPUs, a multi-core processor, etc. The processor  1202  may access the memory  1204  by way of a system bus  1206 . In addition to storing executable instructions, the memory  1204  may also store historical data, computer-implemented spatiotemporal statistical models, weighted directed graphs, etc. 
     The computing device  1200  additionally includes a data store  1208  that is accessible by the processor  1202  by way of the system bus  1206 . The data store  1208  may include executable instructions, historical data, computer-implemented spatiotemporal statistical models, weighted directed graphs, etc. 
     The computing device  1200  also includes an input interface  1210  that allows external devices to communicate with the computing device  1200 . For instance, the input interface  1210  may be used to receive instructions from an external computer device, etc. The computing device  1200  also includes an output interface  1212  that interfaces the computing device  1200  with one or more external devices. For example, the computing device  1200  may transmit control signals to the vehicle propulsion system  306 , the braking system  308 , and/or the steering system  310  by way of the output interface  1212 . 
     Additionally, while illustrated as a single system, it is to be understood that the computing device  1200  may be a distributed system. Thus, for instance, several devices may be in communication by way of a network connection and may collectively perform tasks described as being performed by the computing device  1200 . 
     Various functions described herein can be implemented in hardware, software, or any combination thereof. If implemented in software, the functions can be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer-readable storage media. A computer-readable storage media can be any available storage media that can be accessed by a computer. By way of example, and not limitation, such computer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. Disk and disc, as used herein, include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc (BD), where disks usually reproduce data magnetically and discs usually reproduce data optically with lasers. Further, a propagated signal is not included within the scope of computer-readable storage media. Computer-readable media also includes communication media including any medium that facilitates transfer of a computer program from one place to another. A connection, for instance, can be a communication medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, cellular, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, cellular, and microwave are included in the definition of communication medium. Combinations of the above should also be included within the scope of computer-readable media. 
     Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc. 
     What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable modification and alteration of the above devices or methodologies for purposes of describing the aforementioned aspects, but one of ordinary skill in the art can recognize that many further modifications and permutations of various aspects are possible. Accordingly, the described aspects are intended to embrace all such alterations, modifications, and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the details description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.