Uber Technologies, Inc.

University of California

Checkpoint Systems, Inc.

Plato Systems, Inc.

Motorola Corporation

Bell Communications Research, Inc.

Merunetworks, Inc.

Bytemobile, Inc.

Lucent Technologies Inc.

University of Illinois

Upamanyu Madhow

Andrew T Irish

Kannan Ramchandran

Dinesh Ramasamy

Ben J Wild

Daniel Iland

Dineshkumar Karuppanna Gounder Ramasamy

Mohammad Amin Arbabian

Alvin AuYoung

Sheng Yang

Shivendra Pratap Singh

Chandan Prakash Sheth

Vikram Saxena

Babak Mamandipoor

Livia Zarnescu Yanez

Jason T Isaacs

Colin D Frank

Brent Evans Hamby

Juthika Kiran Dabholkar

Brandon Iles

Michael L Honig

Artem Tkachenko

Sungwon Ha

William J Salisbury

Tae-eun Kim

Francois Quitin

Joao P Hespanha

Anson Chu

Mohammed Waleed Kadous

Vaduvur Bharghavan

Phillip D Rasky

Sung-Wook Han

T V Lakshman

Robert G Barton

Himaanshu Gupta

Hao Nan

Liping Julia Zhu

Jingchen Liu

Constantine Polychronopoulos

Sung-wook Han

Bernhard Suter

Yuh-Jie Eunice Chen

Ben Wild

Artem Tkachenco

Luis Galup

Yevgeny Visotsky

Rahul Singh

Brent Hamby

Michael Latham Honig

Andrew Irish

Systems and methods of using satellite signal strength to determine indoor/outdoor transition points for places are disclosed herein. In some example embodiments, a computer system accesses service data and sensor data for a plurality of requests for a transportation service associated with a place, with the service data comprising pick-up data indicating a pick-up location and drop-off data indicating a drop-off location, and the sensor data comprising satellite signals indicating a pick-up path or a drop-off path, with the satellite signals each having a corresponding signal strength. The computer system determines a transition geographic location for the place based on the signal strengths of the satellite signals.

Indoor/outdoor transition points based on satellite signal strength

Systems and methods of using sensor data for coordinate prediction are disclosed herein. In some example embodiments, for a place, a computer system accesses corresponding service data comprising pick-up data and drop-off data for requests, and accesses corresponding sensor data indicating at least one path of mobile devices of the requesters of the requests, with the at least one path comprising at least one of a pick-up path ending at the pick-up location indicated by the pick-up data and a drop-off path beginning at the drop-off location indicated by the drop-off data. In some example embodiments, the computer system generates at least one predicted geographic location using the paths indicated by the sensor data, and stores the at least one predicted geographic location in a database in association with an identification of the place.

Using sensor data for coordinate prediction

A radar data acquisition circuit comprising: a radar receiver to receive radar data representing a radar scene in response to a sequence of extended sampling waveform frames that includes multiple sampling waveform subframes; and a first processor; configured with stored program instructions to generate one or more replacement sampling waveform subframes to include in an extended sampling waveform frame of the sequence based at least in part upon one or more radar parameters of radar data received using the sequence of extended sampling waveform frames.

Robust radar-centric perception system

A radar sensor system is provided that includes: multiple sensor units that each includes a radar receiver and a location sensor; and a processor that is configured to use location sensor-based information to determine differences between respective sensor unit nominal locations and respective sensor unit actual locations and to use the determined differences to fuse the radar data received at the radar receivers to produce a wide radar aperture.

Multimodal sensing, fusion for machine perception

A network system uses Wi-Fi signals or other types of short-range transmissions to determine pickup locations for users receiving services provided via the network system. The network system builds a database of search records mapping pickup locations to signatures of short-range transmission detected by users' client devices when they searched for the pickup locations. By comparing a signature detected by a given user's client device to the signatures in the database, the network system can check for similarities between the short-range transmissions. Responsive to finding a match, the network system predicts that the given user is likely to select a similar pickup location as other users whose client devices detected the signatures corresponding to the match. Accordingly, by leveraging the database, the network system can predict pickup locations without requiring the given user to input a search for a pickup location.

Geographic positioning using short-range transmissions

A network system, such as a transport management system, efficiently allocates resources by monitoring the geospatial and topological locations of a rider responsive to receiving a trip request. A trip management module matches a rider with an available driver based in part on an comparison of the estimated times of arrival of the rider and the driver at the pickup location. A client positioning module monitors the rider's progress through nodes and edges in a topological graph associated with the origin location based on radio signatures received at the rider client device. A client ETA module calculates a rider ETA based on the rider's rate of travel through the origin location represented by nodes in the topological graph. Responsive to determining that the rider ETA and the driver ETA vary by over a threshold amount of time, the trip management module matches the rider with a second available driver.

Determining a topological location of a client device using received radio signatures

A method of determining location of a user device includes receiving global navigation satellite system (GNSS) fix data that represents GNSS calculated position of the user device, receiving signal strength data associated with each satellite communicating with the user device, and receiving satellite data regarding locations of satellites. The method further includes retrieving satellite blocking values from a cache that describe a likelihood of a satellite signal being blocked at a plurality of possible locations. A non-linear filter, implemented by one or more processors, is applied to the GNSS fix data, signal strength data, and satellite blocking values to generate an updated position estimate of the user device.

System and method for localization and tracking

Various embodiments each include at least one of systems, methods, devices, and software for GNSS simultaneous localization and mapping (SLAM). The disclosed techniques demonstrate that simultaneous localization and mapping (SLAM) can be performed using only GNSS SNR and geo-location data, collectively termed GNSS data henceforth. A principled Bayesian approach for doing so is disclosed. A 3-D environment map is decomposed into a grid of binary-state cells (occupancy grid) and the receiver locations are approximated by sets of particles. Using a large number of sparsely sampled GNSS SNR measurements and receiver/satellite coordinates (all available from off-the-shelf GNSS receivers), likelihoods of blockage are associated with every receiver-to-satellite beam. Loopy Belief Propagation is used to estimate the probabilities of each cell being occupied or empty, along with the probability of the particles for each receiver location.

Systems and methods for GNSS SNR probabilistic localization and 3-D mapping

A collision warning system determines probabilities of potential collisions between a vehicle and other objects such as other vehicles. In an embodiment, sensors of a client device capture sensor data including motion data and image frames from a forward-facing view of the vehicle. An orientation of the client device relative to the vehicle may be determined using the motion data. The collision warning system determines cropped portions of the image frames and detects an object captured the image frames by processing the cropped portions. The collision warning system determines a probability of a potential collision between the vehicle and the object by tracking motion of the object. Responsive to determining that the probability is greater than a threshold value, the collision warning system may provide a notification of the potential collision to a driver of the vehicle.

Image processing for vehicle collision avoidance system

Growing community of inventors

Santa Barbara, CA, United States of America

Upamanyu Madhow