Abstract: The present invention relates to the technical field of central processing units (CPUs), and in particular to a fault-tolerant system with multi-core CPUs capable of being dynamically configured. The multi-core CPUs are three reconfigurable CPUs, the fault-tolerant system with multi-core CPUs is statically configured to be in a normal fault-tolerant mode, a reliable fault-tolerant mode or a performance mode by means of configuration of the three CPUs and then is switched between the normal fault-tolerant mode, the reliable fault-tolerant mode and the performance mode according to a mode switching command of a mode switching register, and finally, the three CPUs are correspondingly configured according to the mode to be switched.

Abstract: The present disclosure provides a system and a method for motion prediction for autonomous driving. The system disclosed herein provides an efficient deep-neural-network-based system to jointly perform perception and motion prediction from 3D point clouds. This system is able to take a pair of LiDAR sweeps as input and outputs for each point in the second sweep, both a classification of the point into one of a set of semantic classes, and a motion vector indicating the motion of the point within the world coordinate system. The system includes a spatiotemporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. The training of this system is regularized with spatial and temporal consistency losses. Thus providing an improved motion planner for autonomous driving applications.

Abstract: A radar image processing device is provided for generating a radar image from a region of interest (ROI). The radar image processing device receives transmitted radar pulses and radar echoes reflected from the ROI at different positions along a path of a moving radar platform and stores computer-executable programs including a range compressor, a graph modeling generator, a signal aligner, a radar imaging generator and a focused image generator.

Abstract: A navigation system for providing driving instructions to a driver of a vehicle traveling on a route is provided. The driving instructions are generated by executing a multimodal fusion method that comprises extracting features from sensor measurements, annotating the features with directions for the vehicle to follow the route with respect to objects sensed by the sensors, and encoding the annotated features with a multimodal attention neural network to produce encodings. The encodings are transformed into a common latent space, and the transformed encodings are fused using an attention mechanism producing an encoded representation of the scene. The method further comprises decoding the encoded representation with a sentence generation neural network to generate a driving instruction and submitting the driving instruction to an output device.

Abstract: A control system for controlling a motion of a vehicle is disclosed, where the control system comprises an input interface to accept measurements of the environment and an image processor to generate an extended bird's eye view (BEV) image from the measurements. The BEV image has a set of pixels carrying information about objects in the environment, and a pixel in the set of pixels is associated with a time sequence of future positions of the pixel in subsequent time steps representing a prediction of a future motion of an object. The control system further comprises a motion planner to produce a motion trajectory of the vehicle using the extended BEV image. The control system further comprises a controller to control an actuator of the vehicle based on the produced motion trajectory.

Abstract: A radar image processing device is provided for generating a radar image from a region of interest (ROI). The radar image processing device receives transmitted radar pulses and radar echoes reflected from the ROI at different positions along a path of a moving radar platform and stores computer-executable programs including a range compressor, a graph modeling generator, a signal aligner, a radar imaging generator and a focused image generator.

Abstract: A control system for controlling a motion of a vehicle is disclosed, where the control system comprises an input interface to accept measurements of the environment and an image processor to generate an extended bird's eye view (BEV) image from the measurements. The BEV image has a set of pixels carrying information about objects in the environment, and a pixel in the set of pixels is associated with a time sequence of future positions of the pixel in subsequent time steps representing a prediction of a future motion of an object. The control system further comprises a motion planner to produce a motion trajectory of the vehicle using the extended BEV image. The control system further comprises a controller to control an actuator of the vehicle based on the produced motion trajectory.

Abstract: A navigation system configured to provide driving instructions to a driver of a moving vehicle based on real-time description of objects in a scene pertinent to driving the vehicle is provided.

Abstract: Generally, the present disclosure is directed to systems and methods for detecting objects of interest and determining location information and motion information for the detected objects based at least in part on sensor data (e.g., LIDAR data) provided from one or more sensor systems (e.g., LIDAR systems) included in the autonomous vehicle. The perception system can include a machine-learned detector model that is configured to receive multiple time frames of sensor data and implement curve-fitting of sensor data points over the multiple time frames of sensor data. The machine-learned model can be trained to determine, in response to the multiple time frames of sensor data provided as input, location information descriptive of a location of one or more objects of interest detected within the environment at a given time and motion information descriptive of the motion of each object of interest.

Abstract: This invention relates to a vehicle-based infrastructure diagnostic apparatus to assist in the maintenance of rail networks. This is a complete system—from the acquisition of vibration signals in a moving vehicle, to the presentation of the infrastructure changes to the maintenance workers. This apparatus relies on novel techniques to account for the variable speed of the train (making it useful even on non-dedicated rail vehicles) and novel techniques to normalize for various environmental conditions. This system could help maintenance departments identify and remediate anomalies more rapidly, as well as trace network deterioration over time. The apparatus consists of low-cost unobtrusive sensors, software for data analysis, and hardware that makes it easy to deploy widely even on existing rail networks.

Abstract: Systems and methods for determining a pattern in time series data representing an operation of a machine. A memory to store and provide a set of training data examples generated by a sensor of the machine, wherein each training data example represents an operation of the machine for a period of time ending with a failure of the machine. A processor configured to iteratively partition each training data example into a normal region and an abnormal region, determine a predictive pattern absent from the normal regions and present in each abnormal region only once, and determine a length of the abnormal region. Outputting the predictive pattern via an output interface in communication with the processor or storing the predictive pattern in memory, wherein the predictive pattern is a predictive estimate of an impending failure and assists in management of the machine.

Abstract: Systems and methods for determining a pattern in time series data representing an operation of a machine. A memory to store and provide a set of training data examples generated by a sensor of the machine, wherein each training data example represents an operation of the machine for a period of time ending with a failure of the machine. A processor configured to iteratively partition each training data example into a normal region and an abnormal region, determine a predictive pattern absent from the normal regions and present in each abnormal region only once, and determine a length of the abnormal region. Outputting the predictive pattern via an output interface in communication with the processor or storing the predictive pattern in memory, wherein the predictive pattern is a predictive estimate of an impending failure and assists in management of the machine.