BACKGROUND

An autonomous vehicle is a motorized vehicle that can operate without a human driver. Conventionally, autonomous vehicles tend to be routed from an origin location to a destination location based upon ad-hoc factors that minimize travel time and/or travel distance from the origin location to the destination location. Autonomous vehicles may also be routed based on operational factors such as distance to or from a charging station and/or a gas station. However, if such operational factors are the only ones considered, the resulting route might not be optimal from other perspectives, as other factors may also be important in generating a route for an autonomous vehicle to follow from the origin location to the destination location.

For instance, an autonomous vehicle conventionally may be routed without consideration of whether it may encounter a vehicle on the road that can detrimentally impact travel of the autonomous vehicle. Thus, a route may be selected for an autonomous vehicle such that the autonomous vehicle is to be in a geographic region at a particular time; during the particular time, another vehicle may also be in the geographic region and may detrimentally impact the autonomous vehicle (e.g., by making frequent stops, speeding, driving slowly, driving erratically, etc.).

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 scope of the claims.

Described herein are various technologies pertaining to routing an autonomous vehicle based upon a likelihood of interacting with a disruptive third-party vehicle potentially traveling through a geographic location at a certain time. A disruptive third-party vehicle can be a vehicle on a road that can detrimentally impact other vehicles on the road (e.g., a disruptive third-party vehicle can be a slow moving vehicle relative to a speed limit, a fast moving vehicle relative to a speed limit, a vehicle that frequently swerves or changes lanes, a vehicle that makes frequent stops at locations other than at stop signs, stop lights, or the like). More specifically, described herein is a computer-implemented model that is configured to output a score that is indicative of a likelihood of the disruptive third-party vehicle traveling through a geographic region during a given period of time. The geographic region can be along a candidate route (e.g., for the autonomous vehicle) from an origin location to a destination location. Moreover, the given period of time can be a period of time during which the autonomous vehicle may travel through the geographic region if following the candidate route. The autonomous vehicle (or a separate computing system) may identify a route for the autonomous vehicle to follow from the origin location to the destination location based in part upon the score. Further, the autonomous vehicle may follow the route from the origin location to the destination location.

A computing system can receive information specifying an origin location of an autonomous vehicle and a destination location of the autonomous vehicle. In one example, the autonomous vehicle can comprise the computing system. In another example, the computing system is separate from the autonomous vehicle (e.g., a server computing system).

According to various embodiments, a plurality of different candidate routes from the origin location to the destination location may be generated and the computing system can identify the route from the plurality of candidate routes. The computing system can identify the route for the autonomous vehicle to follow based upon output of the computer-implemented model. The computing system (or another computing system) can generate the computer-implemented model based upon labeled data indicative of observed instances of the disruptive third-party vehicle exhibiting behavior that impacts operation of an autonomous vehicle. The instances can be observed by one or more autonomous vehicles that are part of a fleet of autonomous vehicles. According to an example, the computing system can further generate a predicted route of the disruptive third-party vehicle based on the observed instances of the disruptive third-party vehicle.

The autonomous vehicle can include a vehicle propulsion system, a braking system, and a steering system. According to the example where the autonomous vehicle comprises the computing system, the computing system can be in communication with the vehicle propulsion system, the braking system, and/or the steering system to move the autonomous vehicle along the route. Alternatively, pursuant to the example where the computing system is separate from the autonomous vehicle, the identified route can be transmitted to the autonomous vehicle and a computing system within the autonomous vehicle can control the vehicle propulsion system, the braking system, and/or the steering system to move the autonomous vehicle along the route.

The above-described technologies present various advantages over conventional computer-implemented routing approaches for autonomous vehicles. For instance, unlike conventional approaches of routing an autonomous vehicle based on time or distance factors regardless of the presence of disruptive third-party vehicles, the technologies set forth herein enable balancing presence of a disruptive third-party vehicle with other routing factors (e.g., travel time, travel distance, etc.); thus, a route for an autonomous vehicle can be selected based at least in part on a predicted schedule of a disruptive third-party vehicle (e.g., the route for the autonomous vehicle can avoid times and locations at which the disruptive third-party vehicle is more likely to be encountered). Moreover, the approaches described herein can be used to predict a route of a disruptive third-party vehicle based on autonomous vehicle interaction with the disruptive third-party vehicle.

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.

