Motional Ad LLC

Nutonomy Inc.

Shih-Yuan Liu

Hsun-Hsien Chang

Karl D Iagnemma

Harshavardhan Ravichandran

Scott D Pendleton

Oscar Olof Beijbom

Tichakorn Wongpiromsarn

Bence Cserna

Sourabh Vora

Daniele De Francesco

Eric Wolff

Ayman Alalao

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

Using a planning circuit of a vehicle, a map is accessed that includes information identifying at least one lane on which vehicles can travel. Using the planning circuit and from the map, a graph representing a driving environment of the vehicle is generated. The graph includes multiple trajectories. At least one trajectory includes a lane change. Each trajectory is a path for the vehicle to move from a first spatiotemporal location to a second spatiotemporal location. The trajectory includes at least one lane alone which the vehicle can move. Using the planning circuit, a trajectory of the multiple trajectories for the vehicle to travel is selected based on an initial vehicle trajectory of the vehicle. The selected trajectory includes a stem. The stem is a portion of the selected trajectory to which the vehicle is configured to adhere. Using the control circuit, the vehicle is moved along the selected trajectory.

Motion graph construction and lane level route planning

Among other things, a determination is made that intervention in an operation of one or more autonomous driving capabilities of a vehicle is appropriate. Based on the determination, a person is enabled to provide information for an intervention. The intervention is caused in the operation of the one or more autonomous driving capabilities of the vehicle.

Intervention in operation of a vehicle having autonomous driving capabilities

Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Operation of a vehicle using motion planning with machine learning

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

An autonomous vehicle receives sensor data from one or more sensors of the autonomous vehicle, and generates a request for remote control of the autonomous vehicle by a computer system remote from the autonomous vehicle. Generating the request includes determining a quality metric associated with a network connection between the autonomous vehicle and the computer system, and upon determining that the quality metric is greater than a threshold quality level, transmitting, from the autonomous vehicle to the computer system the request for remote control and a first data item representing the sensor data. The first data item meets one or more conditions associated with the threshold quality level.

Growing community of inventors

Boston, MA, United States of America

Shih-Yuan Liu