The Charles Stark Draper Laboratory, Inc.

Veoneer US, Inc.

Other

Troy B Jones

Scott Lennox

Jon Dee Demerly

Fei Sun

Hsin-Hsiang Yang

Robert D Tingley

Christopher Bessette

Nicholas Alexander Zervoglos

Afrah Faiz Naik

John Sgueglia

Matthew Graham

Kyra Horne

Stephen Sheldon Strong

In an embodiment, a method includes separating a heterogeneous radio-frequency (RF) signal, received at multiple antennas, into multiple homogenous signals. The multiple antennas have a known positional arrangement. The method further includes estimating a location relative to the known positional arrangement of the antennas of an object producing the RF signal based on phase and amplitude of each homogeneous signal as received at each of the plurality of antennas. The location can further be estimated over multiple time steps, such that velocity and acceleration can be accelerated from the estimated locations.

Autonomous collision avoidance through physical layer tracking

In an embodiment, a method includes receiving, at an autonomous vehicle, reported data regarding an object in proximity to the autonomous vehicle. The data is collected by a collecting device external to the autonomous vehicle, and is relayed to the autonomous vehicle via a server. The reported data includes a current location, type, or predicted location of the object. The method further includes determining whether the reported data of the object matches an object in an object list determined by on-board sensors of the autonomous vehicle. If the determination finds a found object in the object list, the method correlates the reported data of the object to the found object in the object list. Otherwise, the method adds the reported data of the object to an object list of objects detected by sensor from on-board sensors of the autonomous vehicle. In embodiments, the collecting device is a mobile device.

Enhancing autonomous vehicle perception with off-vehicle collected data

Previous self-driving car systems can detect objects separately with either vision systems, RADAR systems or LIDAR systems. In an embodiment of the present invention, an object fusion module normalizes sensor output from vision, RADAR, and LIDAR systems into a common format. Then, the system fuses the object-level sensor data across all systems by associating all objects detected and predicting tracks for all objects. The present system improves over previous systems by using the data from all sensors combined to develop a single set of knowledge about the objects around the self-driving car, instead of each sensor operating separately.

Autonomous vehicle: object-level fusion

An architecture for an autonomous vehicle uses a top-down approach to enable fully automated driving. The architecture is modular and compatible with hardware from different manufacturers. Each modular component can be tailored for individual cars, which have different vehicle control subsystems and different sensor subsystems.

Autonomous vehicle: modular architecture

The invention relates to a hand, wrist and forearm protector for use during sporting activities, in particular, snowboarding. The protector is a unitary piece or shell which encloses a hand, wrist and forearm of a user; thus, providing protection against injury to the user's hand, fingers, wrist and forearm, such as breakage, sprain, strain, bruising or hyperextension. The protector also prevents excessive wear and tear to a snowboarder's clothing, in particular, gloves and jacket sleeves. The invention may also be used for something to sit on during a rest period of the user.

Hand, wrist and forearm protector

Growing community of inventors

Richmond, VA, United States of America

Troy B Jones