Waymo LLC

Verily Life Sciences LLC

Ganesh Balachandran

Choon Ping Chng

Peng Cong

Cheng-Han Wu

You Zou

Peter Strohm

Maria Moon

Salil Pandit

Clement Wright

Keith Hutchings

Jonathan Lee Pedersen

Lauren Schwendimann

Kevin Joseph Malta

Daniella Gutlansky

Vasily Starostenko

Ilmo Van Der Lowe

Andrew Warren

One example system for detecting an emergency siren comprises a microphone device, a band pass filter operably coupled to the microphone device and configured to filter sound data from the microphone device to produce filtered sound data, a phase lock loop circuit configured to phase filter the filtered sound data to produce phase filtered data, and a processor configured to determine a presence of a siren signal from the phase filtered data.

Phase lock loop siren detection

Aspects of the disclosure relate to generating a model to assess maximum numbers of concurrent trips for an autonomous vehicle transportation service. For instance, historical trip data, including when requests for assistance were made, response times for those requests for assistance, and a number of available resources when each of the requests for assistance were made may be received. In addition, a number of concurrent trips, or trips that overlap in time, occurring when each of the requests for assistance were made may be received. The model may be trained using the historical trip data and the numbers of concurrent trips. The model may be configured to provide a maximum number of concurrent trips given a period of time, a number of available resources, and a response time requirement.

Resource allocation for an autonomous vehicle transportation service

The technology employs a holistic approach to passenger pickups and other wayfinding situations. This includes identifying where passengers are relative to the vehicle and/or the pickup location. Information synthesis from different sensors, agent behavior prediction models, and real-time situational awareness are employed to identify the likelihood that the passenger to be picked up is at a given location at a particular point in time, with sufficient confidence. The system can provide adaptive navigation by helping passengers understand their distance and direction to the vehicle, for instance using various cues via an app on the person's device. Rider support tools may be provided, which enable a remote agent to interact with a customer via that person's device, such as using the camera on the device to provide wayfinding support to enable the person to find their vehicle. Ride support may also use sensor information from the vehicle when providing wayfinding support.

Identifying a customer of an autonomous vehicle

A system includes multiple microphone arrays positioned at different locations on a roof of an autonomous vehicle. Each microphone array includes two or more microphones. Internal clocks of each microphone array are synchronized by a processor and used to generate timestamps indicating when microphones capture a sound. Based on the timestamps, the processor is configured to localize a source of the sound.

External microphone arrays for sound source localization

Aspects of the disclosure relate to models for estimating the likelihood of fatigue in test drivers. In some instances, training data including videos of the test drivers while such test drivers are tasked with monitoring driving of a vehicle operating in an autonomous driving mode may be identified. The training data also includes driver drowsiness values generated from one or more human operators observing the videos. The training inputs and outputs may be used to train the model such that when a new video of a first test driver is input into the model, the model will output an estimate of a likelihood of fatigue for that test driver.

Fatigue monitoring system for drivers tasked with monitoring a vehicle operating in an autonomous driving mode

Systems and methods for active charge-balancing for high frequency neural stimulation are disclosed. One illustrative method described herein includes: applying, through a pair of electrodes electrically coupled to a bundle of nerve fibers during a stimulation phase of a neural stimulation procedure, a first current to the bundle of nerve fibers; applying, through the pair of electrodes during a recovery phase of the neural stimulation procedure, a second current to the bundle of nerve fibers, the first current and the second current having opposite polarities; determining sampled voltages between the pair of electrodes during the stimulation phase, during the recovery phase, or between the stimulation phase and the recovery phase; determining a charge buildup in the bundle of nerve fibers based on the sampled voltages; applying, through the pair of electrodes during the stimulation phase or during the recovery phase, a delta current to the bundle of nerves based on the sampled voltages to minimize the charge build up.

Systems and methods for active charge-balancing for high-frequency neural stimulation

Examples of determining and using physiological response estimates with glucose monitoring and insulin dosing systems and methods are disclosed. For example, one method includes receiving a selection of food items from a predefined catalog of food items as being part of a meal associated with a user profile, the catalog including nutritional information corresponding to food items in the catalog; determining nutritional information of the meal based on the nutritional information of each selected food item; receiving a notification from a user interface that a user associated with the user profile has started eating the meal; receiving successive indications of the user's glucose levels; computing a physiological response estimate associated with the user profile and the meal based on the successive indications of the user's glucose levels and the nutritional information of the meal.

Growing community of inventors

Mountain View, CA, United States of America

Ganesh Balachandran