Abstract: Disclosed herein are system, method, and computer program product aspects for enabling an autonomous vehicle (AV) to react to objects posing a risk to the AV. The system can monitor an object within a vicinity of the AV. A plurality of trajectories predicting paths the object will take can be generated, the plurality of trajectories being based on a plurality of inputs indicating current and past characteristics of the object. Using a learned model, a forecasted position of the object at an instance in time can be generated. An error value representing how accurate the forecasted position is versus an observed position of the object can be stored. Error values can be accumulated over a period of time. A risk factor can be assigned for the object based on the accumulated error values. A maneuver for the AV can be performed based on the risk factor.

Abstract: A system includes an on-board electronic device of an autonomous vehicle, and a computer-readable medium having one or more programming instructions. The system receives one or more forecast messages pertaining to a track, where each of the forecast messages includes a unique identifier associated with the track, and receives one or more inference messages pertaining to the track, where each of the inference messages includes the unique identifier. The system aggregates the one or more forecast messages and the one or more inference messages to generate a message set, and applies a set of processing operations to the message set to generate a feedback message. The system identifies one or more events from the feedback message, automatically generates an annotation for one or more of the events that is identified, and embeds the generated annotations in an event log for the autonomous vehicle.

Abstract: Disclosed herein are system, method, and computer program product aspects for enabling an autonomous vehicle (AV) to react to objects posing a risk to the AV. The system can monitor an object within a vicinity of the AV. A plurality of trajectories predicting paths the object will take can be generated, the plurality of trajectories being based on a plurality of inputs indicating current and past characteristics of the object. Using a learned model, a forecasted position of the object at an instance in time can be generated. An error value representing how accurate the forecasted position is versus an observed position of the object can be stored. Error values can be accumulated over a period of time. A risk factor can be assigned for the object based on the accumulated error values. A maneuver for the AV can be performed based on the risk factor.

Abstract: A system includes an on-board electronic device of an autonomous vehicle, and a computer-readable medium having one or more programming instructions. The system receives one or more forecast messages pertaining to a track, where each of the forecast messages includes a unique identifier associated with the track, and receives one or more inference messages pertaining to the track, where each of the inference messages includes the unique identifier. The system aggregates the one or more forecast messages and the one or more inference messages to generate a message set, and applies a set of processing operations to the message set to generate a feedback message. The system identifies one or more events from the feedback message, automatically generates an annotation for one or more of the events that is identified, and embeds the generated annotations in an event log for the autonomous vehicle.