Abstract: An autonomous vehicle is provided that includes one or more processors configured to provide a local compute manager to manage execution of compute workloads associated with the autonomous vehicle. The local compute manager can perform various compute operations, including receiving offload of compute operations from to other compute nodes and offloading compute operations to other compute notes, where the other compute nodes can be other autonomous vehicles. The local compute manager can also facilitate autonomous navigation functionality.

Abstract: A device may include a processor. The processor may receive sensor data representative of an environment of a vehicle. The processor may also generate task data using the sensor data in accordance with a perception task. In addition, the task data may include a plurality of features of the environment. The processor may identify a latent representation of a negative effect of the environment within the sensor data. Further, the processor may estimate an error distribution for the task data based on the identified latent representation, the task data, and the perception task. The processor may generate output data. The output data may include a normalized distribution of the plurality of features based on the estimated error distribution and the task data.

Abstract: Methods, apparatus, systems, and articles of manufacture to generate a surrogate model based on traces from a computing unit are disclosed. An example apparatus includes an interface; instructions; and processor circuitry to execute the instructions to generate sequences of events for a platform; train an artificial intelligence (AI)-based model using the sequences of events; generate a surrogate model based on the trained AI-based model; and perform unit testing using the surrogate model.

Abstract: An autonomous vehicle is provided that includes one or more processors configured to provide a local compute manager to manage execution of compute workloads associated with the autonomous vehicle. The local compute manager can perform various compute operations, including receiving offload of compute operations from to other compute nodes and offloading compute operations to other compute notes, where the other compute nodes can be other autonomous vehicles. The local compute manager can also facilitate autonomous navigation functionality.

Abstract: An example includes obtaining first sensor data from a first sensor and second sensor data from a second sensor, the first sensor of a first sensor type different than a second sensor type of the second sensor; generating first encoded sensor data based on the first sensor data and second encoded sensor data based on the second sensor data; generating a contextual fused sensor data representation of the first and second sensor data based on the first and second encoded sensor data; generating first and second reconstructed sensor data based on the contextual fused sensor data representation; determining a deviation estimation based on the first and second reconstructed sensor data, the deviation estimation representative of a deviation between: (a) the first reconstructed sensor data, and (b) the first sensor data; and detecting an anomaly in the deviation estimation, the anomaly indicative of an error associated with the first sensor.

Abstract: One embodiment provides for a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with an autonomous vehicle network, a set of multiple processors including a general-purpose processor and a general-purpose graphics processor, the set of multiple processors to execute a compute manager to manage execution of compute workloads associated with the autonomous vehicle, the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.

Abstract: Devices and methods for a vehicle are provided in this disclosure. A device for controlling an active seat of a vehicle may include a processor and a memory. The memory may be configured to store a transfer function. The processor may be configured to predict an acceleration of the active seat of the vehicle based on a first sensor data and the transfer function. The first sensor data may include information indicating an acceleration of a vibration source for the vehicle. The processor may be further configured to generate a control signal to control a movement of the active seat at a first instance of time based on the predicted acceleration.

Abstract: An autonomous vehicle (AV) system may include a memory having computer-readable instructions stored thereon. The AV system may include a processor operatively coupled to the memory and configured to read and execute the computer-readable instructions to perform or control performance of operations. The operations may include receive an instruction text vector representative of a command for an AV provided by a user. The operations may include receive an environment text vector representative of a spatio-temporal feature of an environment of the AV. The operations may include generate a sense set that includes words based on the instruction text vector and the environment text vector. The operations may include compare the words of the sense set to navigational instructions (NIs) within a corpus and identify a NI that corresponds to the words based on the comparison. The operations may also include update a trajectory of the AV based on the NI.

Abstract: One embodiment provides for a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with an autonomous vehicle network, a set of multiple processors including a general-purpose processor and a general-purpose graphics processor, the set of multiple processors to execute a compute manager to manage execution of compute workloads associated with the autonomous vehicle, the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.

Abstract: Technologies for performing in-memory training data augmentation for artificial intelligence include a memory comprising media access circuitry connected to a memory media. The media access circuitry is to obtain an input training data set that includes an initial amount of data samples that are usable to train a neural network. The media access circuitry is further to produce, from the input training data set, an augmented training data set with more data samples than the input training data set.

Abstract: One embodiment provides for a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with an autonomous vehicle network, a set of multiple processors including a general-purpose processor and a general-purpose graphics processor, the set of multiple processors to execute a compute manager to manage execution of compute workloads associated with the autonomous vehicle, the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.

Abstract: An example includes obtaining first sensor data from a first sensor and second sensor data from a second sensor, the first sensor of a first sensor type different than a second sensor type of the second sensor; generating first encoded sensor data based on the first sensor data and second encoded sensor data based on the second sensor data; generating a contextual fused sensor data representation of the first and second sensor data based on the first and second encoded sensor data; generating first and second reconstructed sensor data based on the contextual fused sensor data representation; determining a deviation estimation based on the first and second reconstructed sensor data, the deviation estimation representative of a deviation between: (a) the first reconstructed sensor data, and (b) the first sensor data; and detecting an anomaly in the deviation estimation, the anomaly indicative of an error associated with the first sensor.

Abstract: One embodiment provides for a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with an autonomous vehicle network, a set of multiple processors including a general-purpose processor and a general-purpose graphics processor, the set of multiple processors to execute a compute manager to manage execution of compute workloads associated with the autonomous vehicle, the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.