Motional Ad LLC

Bence Cserna

Kevin C Gall

Eric Wolff

Shih-Yuan Liu

Juraj Kabzan

Alexander Brown

Scott D Pendleton

Henggang Cui

Gianmarco Alessandro Bernasconi

Sammie Katt

Sammy Omari

Aravindkumar Vijayalingam

Elena Corina Grigore

Hans Andersen

Laith Sahawneh

Mochan Shrestha

Puneet Singhal

Ruben Strenzke

Tianyi Gu

Thomas Kølbæk Jespersen

Provided are methods for thermal sensor data vehicle perception, which can include obtaining thermal sensor data, obtaining non-thermal sensor data, and determining, based on the thermal sensor data and the non-thermal sensor data, a perception parameter indicative of an object. Some methods described also include generating a trajectory for an autonomous vehicle. Systems and computer program products are also provided.

Thermal sensor data vehicle perception

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 light-based object localization, which can include comparing unexpected light sources to expected light sources for determination of an agent, such as a partially and/or fully occluded agent. Some methods described also include generating a trajectory for an autonomous vehicle based on the comparison. Systems and computer program products are also provided.

Light-based object localization

Among other things, techniques are described for receiving, from at least one sensor of a vehicle, sensor data associated with a surface along a path to be traveled by a vehicle; using a surface classifier to determine a classification of the surface based on the sensor data; determining, based on the classification of the surface, drivability properties of the surface; planning, based on the drivability properties of the surface, a behavior of the vehicle when driving near the surface or on the surface; and controlling the vehicle based on the planned behavior.

Road surface condition guided decision making and prediction

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.

Passenger preference route and alternative destination estimator

Among other things, techniques are described for driver data guided spatial planning. A spatial structure is generated comprising a plurality of nodes connected by edges. At least some of the nodes and edges represent a path to navigate a vehicle from a first point to a second point. Edges of the spatial structure are labeled as useful based on a distance metric. The spatial structure is pruned by removing one or more edges from the spatial structure according to a respective label of the edges, wherein an extent of the removal is based on a predetermined graph size, a predetermined performance, or any combinations thereof to obtain a pruned graph. A path from the first point to the second point on the pruned graph is identified and the vehicle is navigated in accordance with the path from the first point to the second point on the pruned graph.

Driving data guided spatial planning

Among other things, techniques are described for notifying and explaining the action performed by an autonomous vehicle, including but not limited to: receiving a planned path of a vehicle, a state of the vehicle and environment data of an environment in which the vehicle is operating, receiving a deviation signal, determining whether the deviation signal was reported by a first system or a second system of the vehicle, in response selecting a first set of simulators or a second set of simulators for simulating the vehicle in the environment, simulating the vehicle in the environment using the selected first or second set of simulators, based on results of the simulating, generating a message and presenting the message to at least one occupant of the vehicle.

Autonomous vehicle post-action explanation system

Among other things, techniques are described for receiving, from at least one sensor of a vehicle, a sensor measurement indicative of at least one of a sound or a vibration associated with a road element; identifying the road element based on a pattern in the sensor measurement; determining, based on the road element, a vehicle behavior for the vehicle; and controlling the vehicle to operate according to vehicle behavior.

Surface guided vehicle behavior

Among other things, techniques are described for conditional motion predictions. The techniques include generating, by a planning circuit, a set of candidate trajectories for a vehicle based on possible macro actions by the vehicle, predicting, by the planning circuit and for at least some trajectories in the set of candidate trajectories, a response by a target vehicle to the respective trajectory and a probability of the response by the target vehicle, selecting, by the planning circuit, a trajectory from the set of candidate trajectories based at least in part on the response by the target vehicle to the trajectory, the probability of the response by the target vehicle, and characteristics of the trajectory, and operating, by a control circuit, the vehicle based on the selected trajectory.

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Boston, MA, United States of America

Bence Cserna