Rutgers, the State University of New Jersey

Stevens Institute of Technology

Gm Global Technolgoy Operations, Inc.

Marco Gruteser

Yingying Chen

Yan Wang

Suhas Mathur

Jie Yang

Jian Liu

Richard P Martin

Fan Bai

Donald K Grimm

Yunxin Liu

Douglas C Martin

Dipankar Raychaudhuri

Ramesh Govindan

Hang Qiu

Yanyong Zhang

Luyang Liu

Wuyang Zhang

Gorkem Kar

Hongyu Li

Disclosed is a method and system that receives sensor information from each of a plurality of sensors. Each sensor in the plurality is associated with a vehicle. The sensor information includes location coordinates of each vehicle in the plurality. The sensor information associated with each vehicle in the plurality then is translated to parking statistics information. In one embodiment, the translation is based on an aggregate of sensor information corresponding to the plurality of vehicles. The system then communicates parking statistics information to the vehicle.

Vehicular information systems and methods

Computer vision systems and methods for acceleration of high-resolution mobile deep vision with content-aware parallel offloading are provided. The system allows for acceleration of deep vision applications running on mobile devices via offloading of machine learning computations on edge cloud infrastructure, and speeds up computation via parallel processing on the edge cloud, employing one or more of the following processes: recurrent region proposal prediction, region proposal centric frame partitioning, and resource-aware offloading to multiple processors. Mobile deep vision applications can utilize the systems and methods disclosed herein to accelerate application processing. The system offloads computation to multiple servers to minimize the end-to-end latency, and includes a high-resolution mobile deep vision task acceleration system that offloads computation to multiple (edge located) servers.

Computer vision systems and methods for acceleration of high-resolution mobile deep vision with content-aware parallel offloading

A system includes a device that is disposed within an environment and is adapted to communicate over a radio frequency communication link. The system also includes a wireless access point disposed within the environment, including a wireless transceiver in communication with the device over a radio frequency communication link using a plurality of channels, and recording a channel state information data set for the radio frequency communication link. The system also includes a monitoring device including a memory storing a plurality of activity profiles, each of which includes an activity and a channel state information profile corresponding to the activity, and a processor receiving, from the wireless access point, the channel state information data set and determining, based on a comparison of the channel state information data set to the channel state information profile of each of the plurality of activity profiles, the activity of the person in the environment.

Device-free activity identification using fine-grained WiFi signatures

Systems and methods for edge assisted real-time object detection for mobile augmented reality are provided. The system employs a low latency offloading process, decouples the rendering pipeline from the offloading pipeline, and uses a fast object tracking method to maintain detection accuracy. The system can operate on a mobile device, such as an AR device, and dynamically offloads computationally-intensive object detection functions to an edge cloud device using an adaptive offloading process. The system also includes dynamic RoI encoding and motion vector-based object tracking processes that operate in a tracking and rendering pipeline executing on the AR device.

Systems and methods for edge assisted real-time object detection for mobile augmented reality

Device-free activity identification using fine grained WiFi signatures

A method for determining the position in a vehicle of a first device in communication with a microprocessor may include receiving, at the microprocessor, a first set of inertial data from at least one sensor of the first device; receiving, at the microprocessor, a second set of inertial data from at least one sensor of a reference device disposed within the vehicle; and determining, using the microprocessor, the position of the first device in the vehicle by comparing the first set of inertial data with the second set of inertial data.

Systems and methods for detecting driver phone operation using vehicle dynamics data

A method of tracking a service queue using a single-point signal monitor in which at least one customer carries a wireless device may include: receiving a received signal strength from the wireless device as a trace; identify first segments of the trace containing mostly positive sloped received signal strength; identify second segments of the trace containing mostly negative sloped received signal; extract attributes from the first and second segments as child variables for two parent nodes in a Bayesian Network in which the two parent nodes respectively model a beginning of service and a leaving period for a service queue; and inputting the extracted attributes into Bayesian Network; and determining a beginning of service and leaving period using the Bayesian Network. The method may also include determining critical time points in a trace using a K-L divergence technique.

Tracking service queues using single-point signal monitoring

A system and method for identifying a vehicle driver based on driver behaviors. The system and method include analyzing a sequence of vehicle start-up behaviors for rapid identification of the driver. The start-up analysis includes detecting and evaluating the sequence and timing of events including door opening, door closing, seat belt fastening, ignition switch usage and shift/drive, among others. The technique further includes analyzing a set of longitudinal (or long-term) behaviors for more robust verification of driver identification. The longitudinal behaviors include acceleration and braking patterns, speed pattern (compared to road type and speed limit), stop sign behavior, cruise control usage and many others. Statistical clustering techniques are employed for both the start-up and longitudinal behavior analyses to identify the driver.

Method and apparatus of differentiating drivers based on driving behaviors

Growing community of inventors

Princeton, NJ, United States of America

Marco Gruteser