An Autonomous Driving Approach Based on Trajectory Learning Using Deep Neural Networks

2021 ◽  
Vol 22 (6) ◽  
pp. 1517-1528
Author(s):  
Dan Wang ◽  
Canye Wang ◽  
Yulong Wang ◽  
Hang Wang ◽  
Feng Pei
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Autonomous Driving ◽  
Trajectory Learning

scholarly journals SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

2020 ◽  
Vol 34 (07) ◽  
pp. 10901-10908 ◽  
Author(s):  
Abdullah Hamdi ◽  
Matthias Mueller ◽  
Bernard Ghanem
Keyword(s):  
Neural Networks ◽  
Recent Work ◽  
Autonomous Navigation ◽  
General Framework ◽  
Deep Neural Networks ◽  
Autonomous Driving ◽  
Black Box ◽  
Semantic Meaning ◽  
Safety Critical ◽  
Adversarial Attack

One major factor impeding more widespread adoption of deep neural networks (DNNs) is their lack of robustness, which is essential for safety-critical applications such as autonomous driving. This has motivated much recent work on adversarial attacks for DNNs, which mostly focus on pixel-level perturbations void of semantic meaning. In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks. To do this, we re-frame the adversarial attack problem as learning a distribution of parameters that always fools the agent. In the semantic case, our proposed adversary (denoted as BBGAN) is trained to sample parameters that describe the environment with which the black-box agent interacts, such that the agent performs its dedicated task poorly in this environment. We apply BBGAN on three different tasks, primarily targeting aspects of autonomous navigation: object detection, self-driving, and autonomous UAV racing. On these tasks, BBGAN can generate failure cases that consistently fool a trained agent.

scholarly journals Understanding Local Robustness of Deep Neural Networks under Natural Variations

2021 ◽  
pp. 313-337
Author(s):  
Ziyuan Zhong ◽  
Yuchi Tian ◽  
Baishakhi Ray
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Deep Neural Networks ◽  
Autonomous Driving ◽  
Regression Problem ◽  
Rigorous Testing ◽  
Wide Range ◽  
Natural Variants ◽  
Norm Bound ◽  
Commercial Applications

AbstractDeep Neural Networks (DNNs) are being deployed in a wide range of settings today, from safety-critical applications like autonomous driving to commercial applications involving image classifications. However, recent research has shown that DNNs can be brittle to even slight variations of the input data. Therefore, rigorous testing of DNNs has gained widespread attention.While DNN robustness under norm-bound perturbation got significant attention over the past few years, our knowledge is still limited when natural variants of the input images come. These natural variants, e.g., a rotated or a rainy version of the original input, are especially concerning as they can occur naturally in the field without any active adversary and may lead to undesirable consequences. Thus, it is important to identify the inputs whose small variations may lead to erroneous DNN behaviors. The very few studies that looked at DNN’s robustness under natural variants, however, focus on estimating the overall robustness of DNNs across all the test data rather than localizing such error-producing points. This work aims to bridge this gap.To this end, we study the local per-input robustness properties of the DNNs and leverage those properties to build a white-box (DeepRobust-W) and a black-box (DeepRobust-B) tool to automatically identify the non-robust points. Our evaluation of these methods on three DNN models spanning three widely used image classification datasets shows that they are effective in flagging points of poor robustness. In particular, DeepRobust-W and DeepRobust-B are able to achieve an F1 score of up to 91.4% and 99.1%, respectively. We further show that DeepRobust-W can be applied to a regression problem in a domain beyond image classification. Our evaluation on three self-driving car models demonstrates that DeepRobust-W is effective in identifying points of poor robustness with F1 score up to 78.9%.

scholarly journals Online supervised attention-based recurrent depth estimation from monocular video

2020 ◽  
Vol 6 ◽  
pp. e317
Author(s):  
Dmitrii Maslov ◽  
Ilya Makarov
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Temporal Information ◽  
Depth Information ◽  
Safe Driving ◽  
Monocular Video ◽  
Depth Reconstruction

Autonomous driving highly depends on depth information for safe driving. Recently, major improvements have been taken towards improving both supervised and self-supervised methods for depth reconstruction. However, most of the current approaches focus on single frame depth estimation, where quality limit is hard to beat due to limitations of supervised learning of deep neural networks in general. One of the way to improve quality of existing methods is to utilize temporal information from frame sequences. In this paper, we study intelligent ways of integrating recurrent block in common supervised depth estimation pipeline. We propose a novel method, which takes advantage of the convolutional gated recurrent unit (convGRU) and convolutional long short-term memory (convLSTM). We compare use of convGRU and convLSTM blocks and determine the best model for real-time depth estimation task. We carefully study training strategy and provide new deep neural networks architectures for the task of depth estimation from monocular video using information from past frames based on attention mechanism. We demonstrate the efficiency of exploiting temporal information by comparing our best recurrent method with existing image-based and video-based solutions for monocular depth reconstruction.

scholarly journals Deep neural networks for computational optical form measurements

2020 ◽  
Vol 9 (2) ◽  
pp. 301-307
Author(s):  
Lara Hoffmann ◽  
Clemens Elster
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Ground Truth ◽  
Autonomous Driving ◽  
Computational Imaging ◽  
Data Driven ◽  
Learning Approach ◽  
Optical Surfaces ◽  
Machine Learning Approach ◽  
Proof Of Principle

Abstract. Deep neural networks have been successfully applied in many different fields like computational imaging, healthcare, signal processing, or autonomous driving. In a proof-of-principle study, we demonstrate that computational optical form measurement can also benefit from deep learning. A data-driven machine-learning approach is explored to solve an inverse problem in the accurate measurement of optical surfaces. The approach is developed and tested using virtual measurements with a known ground truth.

scholarly journals Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving

2021 ◽  
Author(s):  
Ibrahim Sobh ◽  
Ahmed Hamed ◽  
Varun Ravi Kumar ◽  
Senthil Yogamani
Keyword(s):  
Neural Networks ◽  
Visual Perception ◽  
Motion Detection ◽  
Deep Neural Networks ◽  
Distance Estimation ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Black Box ◽  
Safety Critical ◽  
Future Work

In recent years, deep neural networks (DNNs) have accomplished impressive success in various applications, including autonomous driving perception tasks. However, current deep neural networks are easily deceived by adversarial attacks. This vulnerability raises significant concerns, particularly in safety-critical applications. As a result, research into attacking and defending DNNs has gained much coverage. In this work, detailed adversarial attacks are applied on a diverse multi-task visual perception deep network across distance estimation, semantic segmentation, motion detection, and object detection. The experiments consider both white and black box attacks for targeted and un-targeted cases, while attacking a task and inspecting the effect on all others, in addition to inspecting the effect of applying a simple defense method. We conclude this paper by comparing and discussing the experimental results, proposing insights and future work. The visualizations of the attacks are available at https://youtu.be/6AixN90budY.

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