Robert Boschgmbh

Honda Motor Co., Ltd.

Robert Bosch Gbmh

Nanxiang Li

Liu Ren

Lincan Zou

Teruhisa Misu

Axel Wendt

Bilal Alsallakh

Eman Hassan

Liang Gou

Ashish Tawari

Shabnam Ghaffarzadegan

Huan Song

Lin Ren

Fei Tao

Shen Yan

Visual analytics tool for updating object detection models in autonomous driving applications. In one embodiment, an object detection model analysis system including a computer and an interface device. The interface device includes a display device. The computer includes an electronic processor that is configured to extract object information from image data with a first object detection model, extract characteristics of objects from metadata associated with image data, generate a summary of the object information and the characteristics, generate coordinated visualizations based on the summary and the characteristics, generate a recommendation graphical user interface element based on the coordinated visualizations and a first one or more user inputs, and update the first object detection model based at least in part on a classification of one or more individual objects as an actual weakness in the first object detection model to generate a second object detection model for autonomous driving.

Visual analytics platform for updating object detection models in autonomous driving applications

A system and method for domain adaptation involves a first domain and a second domain. A machine learning system is trained with first sensor data and first label data of the first domain. Second sensor data of a second domain is obtained. Second label data is generated via the machine learning system based on the second sensor data. Inter-domain sensor data is generated by interpolating the first sensor data of the first domain with respect to the second sensor data of the second domain. Inter-domain label data is generated by interpolating first label data of the first domain with respect to second label data of the second domain. The machine learning system is operable to generate inter-domain output data in response to the inter-domain sensor data. Inter-domain loss data is generated based on the inter-domain output data with respect to the inter-domain label data. Parameters of the machine learning system are updated upon optimizing final loss data that includes at least the inter-domain loss data. After domain adaptation, the machine learning system, which is operable in the first domain, is adapted to generate current label data that identifies current sensor data of the second domain.

System and method for unsupervised domain adaptation with mixup training

Methods, systems, and non-transitory computer-readable media for generating augmented data to train a deep neural network to detect traffic lights in image data. The method includes receiving a plurality of real roadway scene images and selecting a subset of the plurality of real roadway scene images. The method also includes selecting an image from the subset and determining a distribution indicting how likely each location in the selected image can contain a traffic light. The method further includes selecting a location in the selected image by sampling the distribution and superimposing a traffic light image onto the selected image at the selected location to generate an augmented roadway scene image. The method also includes processing each image in the subset to generate a plurality of augmented roadway scene images. The method further includes training a deep neural network model using the pluralities of real and augmented roadway scene images.

Semantically-consistent augmented training data for traffic light detection

Systems, methods, and non-transitory computer-readable media for detecting small objects in a roadway scene. A camera is coupled to a vehicle and configured to capture a roadway scene image. An electronic controller is coupled to the camera and configured to receive the roadway scene image from the camera. The electronic controller is also configured to generate a Generative Adversarial Network (GAN) model using the roadway scene image. The electronic controller is further configured to determine a distribution indicting how likely each location in the roadway scene image can contain a roadway object using the GAN model. The electronic controller is also configured to determine a plurality of locations in the roadway scene image by sampling the distribution. The electronic controller is further configured to detect the roadway object at one of the plurality of locations in the roadway scene images.

Generative adversarial network models for small roadway object detection

A computer-program product storing instructions which, when executed by a computer, cause the computer to receive an input data, encode the input via an encoder, during a first sequence, obtain a first latent variable defining an attribute of the input data, generate a sequential reconstruction of the input data utilizing the decoder and at least the first latent variable, obtain a residual between the input data and the reconstruction utilizing a comparison of at least the first latent variable, and output a final reconstruction of the input data utilizing a plurality of residuals from a plurality of sequences.

Apparatus and system for unsupervised disentangled representation learning with a residual variational autoencoder

Weaknesses may be exposed in image object detectors. An image object is overlaid onto a background image at each of a plurality of locations, the background image including a scene in which the image objects can be present. A detector model is used to attempt detection of the image object as overlaid onto the background image, the detector model being trained to identify the image object in background images, the detection resulting in background scene detection scores indicative of likelihood of the image object being detected at each of the plurality of locations. A detectability map is displayed overlaid on the background image, the detectability map including, for each of the plurality of locations, a bounding box of the image object illustrated according to the respective detection score.

Visual analytics exposure of image object detector weakness

A system includes a display device, a memory configured to store a visual analysis application and image data including a plurality of images including detectable objects; and a processor, operatively connected to the memory and the display device. The processor is configured to execute the visual analysis application to learn generative factors from objects detected in the plurality of images, visualize the generative factors in a user interface provided to the display device, receive grouped combinations of the generative factors and values to apply to the generative factors to control object features, create generated objects by applying the values of the generative factors to the objects detected in the plurality of images, combine the generated objects into the original images to create generated images, and apply a discriminator to the generated images to reject unrealistic images.

Involved generative machine learning models for functional testing

The systems and methods provided herein are directed to the uploading and transmission of vehicle data to a remote system when a physiological event for a driver has been detected using one or more sensors. Information such as the driver's heart rate, temperature, voice inflection or facial expression may be monitored to detect the physiological event. Vehicle data, such as gathering or control system data, may be sent once the event has been detected. Selected vehicle data associated with the event or all data during the time of the event may be sent. After receiving the vehicle data, the remote system may process or store it where it may be used to modify automated driving functionalities.

Vehicle data selection system for modifying automated driving functionalities and method thereof

Systems and methods for analyzing a driver's brake behavior are provided. In some aspects, the system includes a memory that stores instructions for executing processes for analyzing the driver's brake behavior, and a processor configured to execute the instructions. In aspects, the processes include: receiving data from one or more multi-modal sensors, determining whether each braking event of a plurality of braking events is a hard braking event; analyzing the one or more physiological signals of the driver to determine whether each of the hard braking events is a reactive hard braking event or an intended hard braking event; applying a Lasso regression analysis and a classification framework for each of the hard braking events to identify features that indicate whether a respective hard braking event is a reactive hard braking event or an intended hard braking event; and generating a signal notifying a vehicle of the features.

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San Mateo, CA, United States of America

Nanxiang Li