Motional Ad LLC

Nutonomy Inc.

Philipp Robbel

Karl D Iagnemma

Eric Wolff

Hsun-Hsien Chang

Noam Weinstein

Junqing Wei

Guchan Ozbilgin

Yifan Yang

Maurilio Di Cicco

Alok Sharma

Matthias Sapuan

Jacob C Cheuvront

Akshay Bhagat

Katarzyna Anna Marczuk

Bishwamoy Sinha Roy

David Butterworth

Andrew J Eick

Mauro Aguiar

Paul Schmitt

Baoxing Qin

Maria Antoinette Meijburg

Harshavardhan Ravichandran

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

In general, an indication is received through a user interface of an intention of a potential rider to use an autonomous vehicle. In response to the receipt of the indication, a hailing request is sent by a signaling mode to at least one autonomous vehicle that can receive the hailing request directly in accordance with the signaling mode.

Hailing a vehicle

A method for operating a vehicle includes detecting a traffic light located at a first spatiotemporal location based on a first digital video stream captured by a first camera and a second digital video stream captured by a second camera. It is determined that the vehicle is located at a second spatiotemporal location by validating first location data received from sensors against second location data obtained by filtering the first location data. It is determined that the traffic light is expected at the first spatiotemporal location based on a semantic map referenced by the second spatiotemporal location. Responsive to determining that the traffic light is expected at the first spatiotemporal location, a traffic signal of the traffic light is detected based on the two digital video streams. A trajectory is determined in accordance with the traffic signal. A control circuit operates the vehicle in accordance with the trajectory.

Traffic light detection system for vehicle

Among other things, information is received that identifies or defines a function-related feature of an environment of a vehicle. Function-related information is generated that corresponds to the function-related feature.

Affecting functions of a vehicle based on function-related information about its environment

Growing community of inventors

Cambridge, MA, United States of America

Philipp Robbel