Abstract: A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

Abstract: An Autonomous Vehicle (AV) system, including: a tracking subsystem configured to detect and track relative positioning of another vehicle that is behind or lateral to an AV configured to comply with a safety driving model, and to check a safety driving model compliance status of the other vehicle; and a risk reduction subsystem configured to plan, based on the safety driving model compliance status of the other vehicle, an AV action, wherein if the safety driving model compliance status of the other vehicle is unknown or is known to be non-compliant, the AV action is administration of a safety driving model compliance test to the other vehicle, or is a maneuver by the AV to reduce risk of collision with a leading vehicle positioned in front of the AV.

Abstract: A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

Abstract: According to various embodiments, a method for operating a vehicle may include determining a vehicular area having traffic conditions or characteristics different from traffic conditions of a current or previous location of the vehicle; obtaining traffic and driving information for the determined vehicular region; changing or updating one or more of driving model parameters of a safety driving model during operation of the vehicle based on the obtained traffic and driving information; and controlling the vehicle to operate in accordance with the safety driving model using the one or more changed or updated driving model parameters. A vehicle may seamlessly update operational rules and/or handover of traffic and driving information for transitioning from one region to another.

Abstract: In an autonomous vehicle system, data received from one or more autonomous vehicles (AVs) can be aggregated to generate aggregated data. From this aggregated data, a vector-field map can be generated that includes a plurality of cells. Each of the cells can include a corresponding vector. The vector field map can be analyzed to identify one or more vectors of the plurality of cells that exceed one or more predetermined threshold values. The analysis can include a magnitude analysis and/or a frequency analysis. Based on the analysis, traffic and/or road conditions can be determined, which can provide prior knowledge about the driving behavior of other vehicles. Advantageously, aspects of the disclosure improve predictive motion models and enhance navigation algorithms.

Abstract: Disclosed embodiments include technologies for improving safety mechanisms in computer assisted and/or automated driving (CA/AD) vehicles for protecting vulnerable road users (VRUs). Embodiments include various mechanisms to enable early Responsibility Sensitive Safety (RSS) checks for the CA/AD vehicles driving policy to protect in-danger VRUs. Embodiments also include controlled forwarding mechanisms to notify other CA/AD vehicles and roadside infrastructure when a potentially dangerous situation is detected. Other embodiments are described and/or claimed.

Abstract: A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

Abstract: A vehicle control system, including an in-vehicle bus and a plurality of electronic control units (ECUs) coupled to the in-vehicle bus, wherein at least one ECU of the plurality of ECUs is configured to: receive, at a respective at least one ECU of the plurality of ECUs, a message in a message stream on the in-vehicle bus; evaluate the message to determine at least one of a confidence value of the security classification, a significance value of the message, or a bounds check value of the message; and determine in real-time to allow or deny the message to the vehicle control system based on at least one of the significance value of the message, the bounds check value of the message, or the confidence value of the security classification of the message, to provide a sanitized message stream to the vehicle control system.

Abstract: System and techniques for vehicle occupant monitoring are described herein. Sensor data, that includes visual image data, is obtained from a sensor array of the vehicle. An object carried by the vehicle is detected from the visual image data. A safety event for the vehicle may be identified based on the object detection and an operational element of the vehicle is altered in response to detecting the safety event.

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: 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: 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: Techniques are disclosed to realize a self-adaptive multiresolution digital plate (SAMDP) system that facilitates sharing of vehicle state information via a dynamically generated binary two-dimensional multispectral pattern. The binary 2D multispectral pattern adjusts its resolution to adapt to the visual perception process while communicating vehicle state information without relying on wireless communications (i.e. V2V, V2X, etc.). The techniques as described herein leverage the use of a multichannel spectral means that helps lower the error present in sensing and representation models used by automated driving systems.

Abstract: A robotic manipulation planning system, including at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to: process perception data to detect known, familiar, and unknown objects to generate manipulation candidates; filter manipulation candidates against constraints to reduce the manipulation candidates; and determine quality metrics for the reduced manipulation candidates using a soft-body simulation technique.

Abstract: A system of a collaborative Autonomous Vehicle (AV) Safety Driving Model (SDM) system, including at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to: generate, in response to a condition being satisfied, an SDM message including encoded data representing a warning or a safety parameter; and cause a transceiver to transmit the generated SDM message.

Abstract: Technologies for management of data layers in a heterogeneous geographic information system (GIS) map are disclosed. A compute device may maintain a GIS database that includes geo-quads that represent physical locations of various scales. Data layers and layer tracks may be dynamically added to the GIS database at different scales, allowing for an extensible framework that enables a mechanism for integrating additional functionality. In the illustrative embodiment, a graph database is used to store the GIS database, allowing for a flexible structure. In some embodiments, entries in layer tracks may include binary large objects that may have properties and associated methods, allowing for application-specific functionality.

Abstract: Technologies for thermal enhanced semantic segmentation include a computing device having a visible light camera and an infrared camera. The computing device receives a visible light image of a scene from the visible light camera and an infrared image of the scene from the infrared camera. The computing device registers the infrared image to the visible light image to generate a registered image. Registering the infrared image may include increasing resolution of the infrared image. The computing device generates a thermal boundary saliency image based on the registered infrared image. The computing device may generate the thermal boundary saliency image by applying a Gabor jet convolution to the registered infrared image. The computing device performs semantic segmentation on the visible light image, the registered infrared image, and the thermal boundary saliency image to generate a pixelwise semantic classification of the scene. Other embodiments are described and claimed.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to implement a neural network. An apparatus to implement a neural network, the apparatus comprising memory formed on a substrate, neural network inference logic formed on the same substrate as the memory, the neural network inference logic to load a plurality of neural network parameters in a multiply-accumulate register, and perform a sample-multiply-add operation on the neural network parameter values and input data to generate a neural network inference result, and a memory controller to transfer the neural network inference result to at least one of a host memory external to the substrate or a host processor external to the substrate.

Abstract: A vehicle control system (VCS) for a vehicle may include an in-vehicle bus and an intrusion detection system (IDS). The IDS may: calculate a planned output of the vehicle based on a message of a message stream received from at least one electronic control unit (ECU) coupled to the in-vehicle bus, calculate a reachable set based on an uncertainty metric, and identify an intrusion of the VCS based on a comparison of the planned output and the reachable set.