scholarly journals A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 660
Author(s):  
Florin Leon ◽  
Marius Gavrilescu
Keyword(s):  
Neural Networks ◽  
Literature Review ◽  
Deep Neural Networks ◽  
Autonomous Driving ◽  
Sensor Data ◽  
Prediction Methods ◽  
Trajectory Prediction ◽  
Related Literature ◽  
Traffic Conditions ◽  
Stochastic Techniques

This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems. Specifically, we focus on two aspects extensively explored in the related literature: tracking, i.e., identifying pedestrians, cars or obstacles from images, observations or sensor data, and prediction, i.e., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating through various traffic conditions. Approaches based on deep neural networks and others, especially stochastic techniques, are reported.

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.

Pedestrian Trajectory Prediction in Crowd Scene Using Deep Neural Networks

2021 ◽  
pp. 277-288
Author(s):  
Jeer Zeibo ◽  
Manoj Kumar Mishra ◽  
Amiya Ranjan Panda ◽  
Bhabani Sankar Prasad Mishra ◽  
Pradeep Kumar Mallick
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Trajectory Prediction

scholarly journals Smoothed Deep Neural Networks for Marine Sensor Data Prediction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 22802-22811
Author(s):  
Zhigang Li ◽  
Jialin Wang ◽  
Di Cai ◽  
Yingqi Li ◽  
Changxin Cai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Sensor Data ◽  
Data Prediction

scholarly journals Analysis of Deep Neural Networks For Human Activity Recognition in Videos – A Systematic Literature Review

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hadiqa Aman Ullah ◽  
Sukumar Letchmunan ◽  
M. Sultan Zia ◽  
Umair Muneer Butt ◽  
Fadratul Hafinaz Hassan
Keyword(s):  
Neural Networks ◽  
Literature Review ◽  
Activity Recognition ◽  
Human Activity ◽  
Systematic Literature Review ◽  
Deep Neural Networks ◽  
Human Activity Recognition

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 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%.

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