Abstract: A vehicle computing system onboard an autonomous vehicle can include one or more processors and one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the computing system to perform operations. The operations can include obtaining sensor data from one or more sensors of the autonomous vehicle; applying lossy compression to the sensor data to generate compressed sensor data; storing data describing the compressed sensor data; decompressing the compressed sensor data to generate decompressed sensor data; and inputting data describing the decompressed sensor data into an autonomy system comprising one or more machine-learned models. The autonomy system can be configured to control operations of the autonomous vehicle based on the decompressed sensor data.

Abstract: Systems and methods for handling autonomous vehicle faults are provided. A system includes a plurality of function nodes arranged in a directed graph architecture. The function nodes include a plurality of detector nodes and a plurality of fault handler nodes. Each detector node is communicatively connected to at least one function node and an associated fault handler node. The detector node is configured to obtain output from the function node, detect a fault, and provide a fault event to the associated fault handler node. Each fault handler node can be associated with a fault severity and a corresponding vehicle action. The associated fault handler node can receive the fault event from the detector node and initiate a fault response. The fault response can include initiating a respective vehicle action in response to the fault.

Abstract: Systems and methods for implementing deterministic simulation for autonomous vehicle testing can include an autonomy bookkeeper system configured to generate data logs that include inputs and outputs for each of a first plurality of tasks associated with an autonomy stack. The data logs can be generated upon detection of events such as failed implementation of an autonomy stack. A simulation conductor system can be configured to access the data logs as part of implementing offline testing of an autonomy testing scenario including a second plurality of tasks. A task controller within the simulation conductor system can schedule the second plurality of tasks into a task order determined at least in part from the first plurality of tasks (e.g., based on bookmarks stored in the data logs obtained during implementation of the first plurality of tasks). The flow of inputs to and outputs from the second plurality of tasks can be based at least in part on the task order.

Abstract: 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.

Abstract: Systems and methods for implementing a low-latency braking action for an autonomous vehicle are provided. A computing system can include a vehicle autonomy system comprising one or more processors configured to determine a motion plan for an autonomous vehicle based at least in part on sensor data from one or more sensors of the autonomous vehicle. The computing system can further include a low-latency braking system comprising one or more processors configured to determine that the autonomous vehicle has a likelihood of colliding with an object in a surrounding environment based at least in part on a previously-determined motion plan obtained from the vehicle autonomy system. In response to determining that the autonomous vehicle has a likelihood of colliding with the object in the surrounding environment, the low-latency braking system can further be configured to implement a braking action for the autonomous vehicle.

Abstract: The present disclosure provides a vehicle interface for an autonomous vehicle. In particular, the systems and methods of the present disclosure can, responsive to receiving, from an autonomy computing system of an autonomous vehicle, a time-based trajectory for the autonomous vehicle, verify that execution of the time-based trajectory is within parameters of the autonomous vehicle. Responsive to verifying that execution of the time-based trajectory is within the parameters of the autonomous vehicle, the time-based trajectory can be converted into a spatial path for the autonomous vehicle, and one or more controls of the autonomous vehicle can be interfaced with such that the autonomous vehicle tracks the spatial path.

Abstract: Systems and methods for implementing deterministic simulation for autonomous vehicle testing can include an autonomy bookkeeper system configured to generate data logs that include inputs and outputs for each of a first plurality of tasks associated with an autonomy stack. The data logs can be generated upon detection of events such as failed implementation of an autonomy stack. A simulation conductor system can be configured to access the data logs as part of implementing offline testing of an autonomy testing scenario including a second plurality of tasks. A task controller within the simulation conductor system can schedule the second plurality of tasks into a task order determined at least in part from the first plurality of tasks (e.g., based on bookmarks stored in the data logs obtained during implementation of the first plurality of tasks). The flow of inputs to and outputs from the second plurality of tasks can be based at least in part on the task order.

Abstract: Systems and methods for implementing a low-latency braking action for an autonomous vehicle are provided. A computing system can include a vehicle autonomy system comprising one or more processors configured to determine a motion plan for an autonomous vehicle based at least in part on sensor data from one or more sensors of the autonomous vehicle. The computing system can further include a low-latency braking system comprising one or more processors configured to determine that the autonomous vehicle has a likelihood of colliding with an object in a surrounding environment based at least in part on a previously-determined motion plan obtained from the vehicle autonomy system. In response to determining that the autonomous vehicle has a likelihood of colliding with the object in the surrounding environment, the low-latency braking system can further be configured to implement a braking action for the autonomous vehicle.

Abstract: The present disclosure provides a vehicle interface for an autonomous vehicle. In particular, the systems and methods of the present disclosure can, responsive to receiving, from an autonomy computing system of an autonomous vehicle, a time-based trajectory for the autonomous vehicle, verify that execution of the time-based trajectory is within parameters of the autonomous vehicle. Responsive to verifying that execution of the time-based trajectory is within the parameters of the autonomous vehicle, the time-based trajectory can be converted into a spatial path for the autonomous vehicle, and one or more controls of the autonomous vehicle can be interfaced with such that the autonomous vehicle tracks the spatial path.

Abstract: 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.