Abstract: Methods, systems, and computer programs are presented for implementing Personalized Mobility as a Service (PMaaS) to improve transportation services delivery. One storage medium includes instructions for detecting, by a mobility as a service (MaaS) system, a request for a trip from a user device of a user. The storage medium further includes instructions for mapping, using a model executing on the machine, the user to a persona from a plurality of persona models. Each persona model has one or more characteristics associated with users of the MaaS system. Further yet, the storage medium includes instructions for determining trip parameters for the trip based on the persona mapped to the user, the trip parameters defining one or more trip segments for the trip, and instructions for providing trip parameters to the user device.

Abstract: Disclosed herein are devices, methods, and systems for providing an externally augmented camera that may utilize external light sources of a separate sensor to emit light toward a scene so as to provide accurate imaging of the scene, even in dark or low-light situations or where the scene has a high dynamic range of brightness. The externally augmented camera system may include a sensor with a light source capable of emitting light toward a scene, a camera separate from the light source, where the camera includes a detector capable of detecting emitted light from the light source. The externally augmented camera system also causes the sensor to emit light toward the scene via the light source, causes the camera to capture image data of the scene that has been illuminated by emitted light from the light source, and generates an image of the scene based on the image data.

Abstract: A device includes a sensor, configured to detect electromagnetic radiation originating from a source external to the device and reflected from an object; and to generate an electrical signal representing the detected electromagnetic radiation; and a processor, configured to determine a distance between the device and the object based on the electrical signal.

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.

Abstract: Techniques are disclosed for using neural network architectures to estimate predictive uncertainty measures, which quantify how much trust should be placed in the deep neural network (DNN) results. The techniques include measuring reliable uncertainty scores for a neural network, which are widely used in perception and decision-making tasks in automated driving. The uncertainty measurements are made with respect to both model uncertainty and data uncertainty, and may implement Bayesian neural networks or other types of neural networks.

Abstract: Systems, apparatuses and methods may provide for controlling one or more end effectors by generating a semantic labelled image based on image data, wherein the semantic labelled image is to identify a shape of an object and a semantic label of the object, associating a first set of actions with the object, and generating a plan based on an intersection of the first set of actions and a second set of actions to satisfy a command from a user through actuation of one or more end effectors, wherein the second set of actions are to be associated with the command

Abstract: A DTaaS architecture is described to support communication-side optimization and to reduce communication overhead while meeting necessary reliability and latency requirements. The disclosure describe techniques for utilizing DT resources for V2X environments using both virtual and physical “twin” resources to achieve improved resiliency. Moreover, to optimize redundancy costs, the ratio of virtual DT nodes to physical DT nodes may be asymmetrical. An asymmetric approach to DT redundancies involving both virtual and physical resources enables greater flexibility in managing deployment costs.

Abstract: A controller for an autonomous vehicle may include: one or more processors configured to: determine a maneuver planned for the vehicle based on a safety driving model and based on a first message from a network component external to the vehicle, the first message including a respective assessment for each proposed maneuver of at least two maneuvers proposed for the vehicle, and provide an in-vehicle instruction to perform the maneuver planned for the vehicle.

Abstract: Provided is a device and a method for route planning. The route planning device (100) may include a data interface (128) coupled to a road and traffic data source (160); a user interface (170) configured to display a map and receive a route planning request from a user, the route planning request including a line of interest on the map; a processor (110) coupled to the data interface (128) and the user interface (170). The processor (110) may be configured to identify the line of interest in response to the route planning request; acquire, via the data interface (128), road and traffic information associated with the line of interest from the road and traffic data source (160); and calculate, based on the acquired road and traffic information, a navigation route that matches or corresponds to the line of interest and meets or satisfies predefined road and traffic constraints.

Abstract: Systems, apparatuses and methods may provide for controlling one or more end effectors by generating a semantic labelled image based on image data, wherein the semantic labelled image is to identify a shape of an object and a semantic label of the object, associating a first set of actions with the object, and generating a plan based on an intersection of the first set of actions and a second set of actions to satisfy a command from a user through actuation of one or more end effectors, wherein the second set of actions are to be associated with the command.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve spatial-temporal data management. An example apparatus includes a hypervoxel data structure generator to generate a root hexatree data structure having sixteen hypernodes, an octree manager to improve a spatiotemporal data access efficiency by generating a first degree of symmetry in the root hexatree, the octree manager to assign a first portion of the hypernodes to a positive temporal subspace and to assign a second portion of the hypernodes to a negative temporal subspace, and a quadtree manager to improve the spatiotemporal data access efficiency by generating a second degree of symmetry in the root hexatree, the quadtree manager to assign respective hypernodes of the positive temporal subspace and the negative temporal subspace to respective positive and negative spatial subspaces.

Abstract: Systems, apparatuses and methods may provide for technology that identifies a cognitive space that is to be a compressed representation of activations of a neural network, maps a plurality of activations of the neural network to a cognitive initial point and a cognitive destination point in the cognitive space and generates a first cognitive trajectory through the cognitive space, wherein the first cognitive trajectory traverses the cognitive space from the cognitive initial point to the cognitive destination point.

Abstract: In an automated vehicle (AV) system, interdependencies between components of the AV are determined, an operational domain state of one or more of the components can be determined based on the determined interdependencies, and an output signal representing the operational domain state of the one or more components can be generated. A user interface can present a notification based on the output signal to notify the driver and/or other occupants of the AV of the operational domain state of one or more of the components.

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: Systems, apparatuses and methods may provide for technology that generates, via a first neural network such as a grid network, a first vector representing a prediction of future behavior of an autonomous vehicle based on a current vehicle position and a vehicle velocity. The technology may also generate, via a second neural network such as an obstacle network, a second vector representing a prediction of future behavior of an external obstacle based on a current obstacle position and an obstacle velocity, and determine, via a third neural network such as a place network, a future trajectory for the vehicle based on the first vector and the second vector, the future trajectory representing a sequence of planned future behaviors for the vehicle. The technology may also issue actuation commands to navigate the autonomous vehicle based on the future trajectory for the vehicle.

Abstract: Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.

Abstract: Logic may determine a specific performance of a neural network based on an event and may present the specific performance to provide a user with an explanation of the inference by a machine learning model such as a neural network. Logic may determine a first activation profile associated with the event, the first activation profile based on activation of nodes in one or more layers of the neural network during inference to generate an output. Logic may correlate the first activation profile against a second activation profile associated with a first training sample of training data. Logic may determine that the first training sample is associated with the event based on the correlation. Logic may output an indicator to identify the first training sample as being associated with the event.

Abstract: The present disclosure relates to real-time localization error correction of an autonomous vehicle (AV). A processor for real-time localization error correction of the AV is provided. The processor is configured to retrieve a reference landmark around the AV from a map aggregating server (MAS), wherein the AV is configured to interact with the MAS for real-time localization; detect, in real time, a ground truth landmark corresponding to the reference landmark, according to image data captured by one or more image capture devices installed on the AV; and determine a deviation between the ground truth landmark and the reference landmark as a real-time correction value for the real-time localization of the AV.

Abstract: A component of an Autonomous Vehicle (AV) system, the component having 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 decode data encoded in a signal, wherein the data identifies a pattern of protrusions embedded in a driving surface, the signal being received from at least one vehicle sensor resulting from a vehicle driving over the pattern of protrusions in the driving surface.

Abstract: System and techniques for trusted lightweight communication in cloud robotics are described herein. A device for trusted lightweight communication in cloud robotics can include a memory having instructions and processing circuitry. When in operation, the processing circuitry can be configured by the instructions to receive a first report from a first device after the first device runs a first algorithm and a second report from a second device after the second device runs the first algorithm with an edge controller. The first algorithm can be stored on a distributed ledger. Consensus between the first device and the second device can be determined by agreement between the first report and the second report. An entry, indicative of consensus between the first device and the second device, can be added to the distributed ledger.