Argo Ai Gmbh

Uatc, LLC

Motional Ad LLC

Volkswagen Group of America Investments, LLC

Guillaume Binet

Randall Nortman

Evgeny Televitckiy

Nicholas J Meyer

Diana Yanakiev

David McAllister Bradley

Frederic Tschanz

Dillon Collins

Shailendra Deva

Patrick Zhou

Yijun Tang

Provided are methods for forming redundant node configurations in a multi-systems-on-a-chip environment. Each system-on-a-chip can include one or processors and memories independent of other systems-on-a-chip and in communication via a cache coherent fabric. To facilitate rapid and extensible reconfiguration, various systems-on-a-chip from the multi-systems-on-a-chip environment can be configured into a redundant node configuration, with each logical node implemented by one or more of the systems-on-a-chip. Each system-on-a-chip within a logical node can be configured to utilize a shared memory space, such as by transparent mirroring of logical memory addresses. Across nodes, systems-on-a-chip can communicate using a device-to-device protocol such as a non-transparent bridge. My reconfiguration of communication between systems-on-a-chip, the multi-systems-on-a-chip environment can be reconfigured to represent a variety of redundant configurations. In one example, a multi-systems-on-a-chip environment can be configured to support safe, resilient operation of an autonomous vehicle.

Software-defined compute nodes on multi-SoC architectures

To identify sources of data resulting from an execution flow in a robotic device such as an autonomous vehicle, an operating system receives sensor data from various sensors of the robotic device. For each sensor, the system generates a data log comprising an identifier of a first checkpoint associated with that sensor, as well as a first timestamp. The system performs an execution flow on the sensor data from that sensor. The system updates the data log to include an identifier and timestamp for one or more additional checkpoints during the execution flow. The system then fuses results, uses the fused data as an input for a decision process, and causes a component of the robotic device to take an action in response to an output of the decision process. The system may record the action, an action timestamp and the data logs for each sensor in a memory.

Checkpoint-based tracing for monitoring a robotic system

A system includes a computer-readable memory for storage and retrieval of LiDAR data. The memory includes one or more point cloud data structures, each point cloud data structure including a first header block, and a plurality of point blocks. Each of the plurality of point blocks is configured to store at least a portion of LiDAR point cloud data. The memory includes one or more image data structures having a second header block, and a plurality of image column blocks. Each image column block includes a column of pixels of a corresponding image from the LiDAR point cloud data, and the image column blocks collectively contain all pixels of the corresponding image.

Methods and system for analyzing dynamic lidar point cloud data

A method of scheduling a plurality of tasks in an autonomous vehicle system (AVS) includes, by a processor, prior to runtime of an autonomous vehicle, identifying a plurality of tasks to be implemented by the AVS of the autonomous vehicle, for each of the tasks, identifying at least one fixed parameter and at least one variable, and developing a schedule for each of the tasks. The schedule includes an event loop that minimizes an overall time for execution of the tasks. The method includes compiling the schedule into an execution plan, and saving the execution plan to a memory of the autonomous vehicle. During runtime of the autonomous vehicle, the processor receives data corresponding to the variables of the tasks, and uses the variables to implement the execution plan on the autonomous vehicle.

Ahead of time scheduling process for autonomous vehicles

Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes obtaining data representing a first trajectory including one or more states corresponding to a motion path. The method includes determining a second trajectory based at least in part on the first trajectory, the second trajectory including a first state corresponding to the motion path, and one or more secondary states corresponding to the motion path, the one or more secondary states indicating a state of the autonomous vehicle relative to the first state. The method includes determining one or more control signals based at least in part on the second trajectory. The method includes controlling a motion of the autonomous vehicle according to the motion path, based at least in part on the one or more control signals.

Systems and methods of controlling an autonomous vehicle using an enhanced trajectory following configuration

Growing community of inventors

Pittsburgh, PA, United States of America

Guillaume Binet