Autonomous vehicle operational management blocking monitoring

A method for use in traversing a vehicle transportation network, the method comprising:

• traversing, by an autonomous vehicle, a vehicle transportation network, wherein traversing the vehicle transportation network includes:
• identifying operational environment information including information corresponding to an external object within a defined distance of the autonomous vehicle;
• identifying an area of the vehicle transportation network based on a current geospatial location of the autonomous vehicle in the vehicle transportation network and an identified route for the autonomous vehicle;
• determining, by a. blocking monitor instance, a blocking probability for the area of the vehicle transportation network based on the operational environment information, wherein the blocking probability indicates a probability that an expected path for the external object and a first expected path for the autonomous vehicle are geospatially and temporally convergent within the area;
• on a condition that the blocking probability is within a defined threshold, traversing a portion of the vehicle transportation network in accordance with the first expected path for the autonomous vehicle;
• and
• on a condition that the blocking probability is at least the defined threshold:
• generating an updated route for the autonomous vehicle such that a probability that the expected path for the external object and a second expected path for the autonomous vehicle are geospatially and temporally convergent within the area is within the defined threshold, wherein the second expected path is based on the update route;
• and
• traversing a portion of the vehicle transportation network in accordance with the second expected path for the autonomous vehicle.

An autonomous vehicle comprising:

• a processor configured to execute instructions stored on a non-transitory computer readable medium to operate a blocking monitor instance to:
• identify operational environment information including information corresponding to an external object within a defined distance of the autonomous vehicle and a second external object within the defined distance of the autonomous vehicle;
• identify a portion of a vehicle transportation network based on a current geospatial location of the autonomous vehicle in the vehicle transportation network and an identified route for the autonomous vehicle;
• determine a probability of availability for the portion of the vehicle transportation network based on the operational environment information by:
• determining a first expected path for the first external object;
• determining a second expected path for the second external object;
• and
• on a condition that the first expected path intersects spatially and temporally with the second expected path:
• on a condition that the first external object represents a pedestrian and on a condition that the second external object represents a pedestrian, determining that the first expected path spatially and temporally overlaps the second expected path;
• and
• on a condition that the second external object represents a remote vehicle:
• identifying an area of the vehicle transportation network corresponding to a spatial and temporal intersection of the first expected path and the second expected path;
• determining that the first expected path includes the first external object traversing a second portion of the vehicle transportation network corresponding to the area at a first temporal location;
• and
• determining that the second expected path includes the second external object traversing the second portion at a second temporal location subsequent to the first temporal location;
• and
• control the autonomous vehicle to traverse the portion of the vehicle transportation network based on the probability of availability.