Motional Ad LLC

Nutonomy Inc.

Hsun-Hsien Chang

Harshavardhan Ravichandran

Karl D Iagnemma

Shih-Yuan Liu

Emilio Frazzoli

Eric Wolff

Kostyantyn Slutskyy

Scott D Pendleton

Andrea Censi

Philipp Robbel

Noam Weinstein

Junqing Wei

Ayman Alalao

Guchan Ozbilgin

Yifan Yang

Maurilio Di Cicco

Alok Sharma

Matthias Sapuan

Jacob C Cheuvront

Katarzyna Anna Marczuk

Akshay Bhagat

Mauro Aguiar

Bishwamoy Sinha Roy

David Butterworth

Andrew J Eick

Tichakorn Wongpiromsarn

Dmytro S Yershov

Radboud Duintjer Tebbens

Calin Belta

Anne Collin

Amitai Bin-Nun

Jesse Adam Miller

Among other things, planning a motion of a machine having moving capabilities is based on strategic guidelines derived from various basic principles, such as laws, ethics, preferences, driving experiences, and road environments.

Planning autonomous motion

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

Among other things, we describe systems and method for validating sensor calibration. For validating calibration of a system of sensors having several types of sensors, an object may be configured to have a substantially reflective portion such that the sensors can isolate the substantially reflective portion, and their sensor data can be compared to determine, if the detected locations of the substantially reflective portion by each sensor are aligned. For calibrating a system of sensors, an object having known calibration features can be used and detected by each sensor, and the detected data can be compared to known calibration data associated with the object to determine if each sensor is correctly calibrated.

Systems and methods for validating sensor calibration

Among other things, sensor signals are received using a vehicle comprising an autonomous driving capability. A risk associated with operating the vehicle is identified based on the sensor signals. The autonomous driving capability is modified in response to the risk. The operation of the vehicle is updated based on the modifying of the autonomous capability.

Risk processing for vehicles having autonomous driving capabilities

Enclosed are embodiments for scenario-based behavior specification and validation. In an embodiment, a method comprises: obtaining, using at least one processor, at least one trajectory associated with a driving scenario for an autonomous vehicle system; evaluating, using the at least one processor and at least one rulebook, the at least one trajectories to determine whether the at least one trajectory violates at least one rule in the at least one rulebook, wherein each rule in the rulebook is associated with at least one violation metric that is used to determine a degree to which the rule was satisfied or violated; determining, using the at least one processor and the at least one violation metric, a score for the at least one trajectory; and sending, using the at least one processor, the score to at least one of a software module in a software stack of the autonomous vehicle system, a simulation of the autonomous vehicle system or as a report or in a visual presented through a user interface of a cloud-based platform.

Scenario-based behavior specification and validation

Among other things, an apparatus includes a processor, and storage for instructions executable by the processor to, in connection with a trip of a person in an autonomous vehicle, select a specific location at which the person will be picked up for the trip or a specific location at a destination of the trip, and present through a user interface of a device visible information that depicts the specific location.

Augmented reality vehicle interfacing

Among other things, a vehicle having autonomous driving capabilities is operated in a mixed driving mode.

Mixed-mode driving of a vehicle having autonomous driving capabilities

Technology described in this document can be embodied in a method that includes generating, using one or more processing devices of a vehicle operating in an environment, a discretized representation of the environment, including a plurality of cells. The cells are occupied by particles representing at least one of: an object, or a free space in the environment. Sensor data indicative of a state of at least one particle is received, and an update to a time-varying particle density function associated with a location of the particle in the dynamic occupancy grid is calculated from the sensor data and one or more models. The method further includes generating a prediction of occupancy of at least one cell in the discretized representation based on the updated particle density function, and operating the vehicle, using a controller circuit of the vehicle, based at least in part on the prediction.

Field theory based perception for autonomous vehicles

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.

Teleoperation of autonomous vehicles

Growing community of inventors

Brookline, MA, United States of America

Hsun-Hsien Chang