Motional Ad LLC

Kevin C Gall

Bence Cserna

Juraj Kabzan

Alexander Brown

Henggang Cui

Gianmarco Alessandro Bernasconi

Scott D Pendleton

Sammy Omari

Hans Andersen

Thomas Kølbæk Jespersen

Provided are methods and systems for semantic behavior filtering for prediction improvement. A method for operating an autonomous vehicle, is provided. The method includes obtaining, by at least one processor, semantic image data associated with an environment where an autonomous vehicle is operating. The method includes determining, by the at least one processor, a set of agents in the environment based on the semantic image data. The method includes determining a set of predicted actions for at least one agent of the set of agents. The method includes determining, from the set of predicted actions, a set of secondary predicted actions for the at least one primary agent using semantic data. The method includes determining, from the set of predicted actions, a set of primary predicted actions other than secondary predicted actions The method includes generating a path for the autonomous vehicle based on the set of primary predicted actions.

Path generation based on predicted actions

Provided are methods for post drop-off passenger assistance, which can include obtaining location data indicative of an environment where an autonomous vehicle is operating, determining a completion parameter indicative of a completion of a ride of the autonomous vehicle, determining, an assistance parameter indicative of an assistance of the autonomous vehicle to a passenger of the autonomous vehicle to be provided after the completion of the ride, and controlling an operation of the autonomous vehicle for assisting the passenger. Some methods described also include obtaining sensor data and controlling operation of the autonomous vehicle based on the sensor data. Systems and computer program products are also provided.

Post drop-off passenger assistance

Provided are methods for completing an errand request prior to fulfilling a ride request. Some methods described include obtaining a ride request from a passenger; obtaining an errand request from the passenger, wherein the errand request comprises performing an activity at a third location prior to pickup of the passenger at the first location; planning a route comprising the third location, the first location, and the second location; navigating the planned route to complete the errand request from the passenger at the third location; and navigating the planned route to transport the passenger from the first location to the second location upon completion of the errand request. Systems and computer program products are also provided.

Errand service associated with ride request

Provided are methods for agent prioritization, which can include determining a primary agent set and generating, based on the primary agent set, a trajectory for the autonomous vehicle. Some methods described also include determining an interaction parameter of agents in the environment. Systems and computer program products are also provided.

Methods and systems for agent prioritization

Disclosed is a method including obtaining, using at least one processor, a user destination; obtaining, using the at least one processor, preference data indicative of a user route preference; determining, using the at least one processor, based on the user destination and the preference data, a set of routes towards a set of destinations, wherein at least one route of the set of routes is associated with one or more operational metrics; ranking, using the at least one processor, based on the preference data, the routes of the set of routes; and controlling, using the at least one processor, based on at least one of the ranked routes, navigation of an autonomous vehicle Systems and computer program products are also provided.

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Boston, MA, United States of America

Kevin C Gall