Systems and methods for incident detection using inference models

A system comprising:

• one or more processors and one or more computer-readable non-transitory storage media coupled to one or more of the processors, the one or more computer-readable non-transitory storage media comprising instructions operable when executed by one or more of the processors to cause the system to:
• access contextual data captured using one or more sensors associated with an autonomous vehicle while the autonomous vehicle traverses a route, wherein the contextual data includes perception data for an environment external to the autonomous vehicle and associated with the route;
• generate, based on at least a portion of the perception data, one or more representations of the environment external to the autonomous vehicle;
• determine a predicted risk score associated with the environment by processing the one or more representations of the environment using a learned risk model, wherein the learned risk model is generated based at least on risk scores generated from (1) representations of environments in which human-driven vehicles drove and (2) sensor data associated with historical observations of behaviors of the human-driven vehicles in the representations of the environments;
• determine that one or more autonomous vehicle operations are to be performed while the vehicle traverses the route based on a comparison of the predicted risk score to a threshold risk score;
• and
• adjust one or more driving parameters associated with the autonomous vehicle while the autonomous vehicle traverses the route to cause the autonomous vehicle to perform the one or more autonomous vehicle operations for navigating the route based on the predicted risk score satisfying the threshold risk score, wherein the one or more autonomous vehicle operations are based on the predicted risk score and the historical observations of behaviors of the human-driven vehicles.

One or more computer-readable non-transitory storage media including instructions that, when executed by one or more processors of a computing system, are operable to cause the computing system to perform operations comprising:

• accessing contextual data captured using one or more sensors associated with an autonomous vehicle while the autonomous vehicle traverses a route, wherein the contextual data includes perception data for an environment external to the autonomous vehicle and associated with the route;
• generating, based on at least a portion of the perception data, one or more representations of the environment external to the autonomous vehicle;
• determining a predicted risk score associated with the environment by processing the one or more representations of the environment using a learned risk model, wherein the learned risk model is generated based at least on risk scores generated from (1) representations of environments in which human-driven vehicles drove and (2) sensor data associated with historical observations of behaviors of the human-driven vehicles in the representations of the environments;
• determining that one or more autonomous vehicle operations are to be performed while the vehicle traverses the route based on a comparison of the predicted risk score to a threshold risk score;
• and
• adjusting one or more driving parameters associated with the autonomous vehicle while the autonomous vehicle traverses the route to cause the autonomous vehicle to perform the one or more autonomous vehicle operations for navigating the route based on the predicted risk score satisfying the threshold risk score, wherein the one or more autonomous vehicle operations are based on the predicted risk score and the historical observations of behaviors of the human-driven vehicles.