Mitsubishi Electric Research Laboratories, Inc.

Mitsubishi Electric Corporation

Siheng Chen

Anthony Vetro

Alan Sullivan

Petros T Boufounos

Dong Tian

Jonathan Le Roux

Dehong Liu

Tim Marks

Takaaki Hori

Anoop Cherian

Bret Harsham

Chiori Hori

Chen Feng

Jun Wang

Pengxiang Wu

Xiaolong Li

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.

System and method for motion prediction in autonomous driving

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. The radar image processing device performs range compression on the radar echoes by deconvolving the transmitted radar pulses and a radar measurement to obtain frequency-domain signals, generate a graph model represented by sequential positions of the moving radar platform and a graph shift matrix computed using the frequency-domain signals, iteratively denoise and align the frequency-domain signals to obtained denoised data and time shifts by solving a graph-based optimization problem represented by the graph model, wherein the approximated time shifts compensate phase misalignments caused by perturbed positions of the moving radar platform, and perform radar imaging based on the denoised data and the estimated time shifts to generate focused radar images.

Graph-based array signal denoising for perturbed synthetic aperture radar

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.

Method and system for scene-aware interaction

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.

Bird's eye view map based recognition and motion prediction for autonomous systems

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.

Methods and systems for fast resampling method and apparatus for point cloud data

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Cambridge, MA, United States of America

Siheng Chen