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 Virtual to Real Reinforcement Learning for Autonomous Driving

  

Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then tran…


 A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data

 

This study proposes a Deep Belief Network model to classify traffic flow states. The model is capable of processing massive, high-density, and noise-contaminated data sets generated from smartphone sensors. The statistical features of Vehicle acceleration, angular acceleration, and GPS speed data, …


 Learning Unmanned Aerial Vehicle Control for Autonomous Target Following

    

While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process. However, real-world robotic a…


 When Traffic Flow Prediction Meets Wireless Big Data Analytics

Traffic flow prediction is an important research issue for solving the traffic congestion problem in an Intelligent Transportation System (ITS). Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns. Such prediction is…


 Computation Error Analysis of Block Floating Point Arithmetic Oriented Convolution Neural Network Accelerator Design

 

The heavy burdens of computation and off-chip traffic impede deploying the large scale convolution neural network on embedded platforms. As CNN is attributed to the strong endurance to computation errors, employing block floating point (BFP) arithmetics in CNN accelerators could save the hardware c…


 Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks

  

We propose an inverse reinforcement learning (IRL) approach using Deep Q-Networks to extract the rewards in problems with large state spaces. We evaluate the performance of this approach in a simulation-based autonomous driving scenario. Our results resemble the intuitive relation between the rewar…


 A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization

 

In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this …


 A Voting-Based System for Ethical Decision Making

We present a general approach to automating ethical decisions, drawing on machine learning and computational social choice. In a nutshell, we propose to learn a model of societal preferences, and, when faced with a specific ethical dilemma at runtime, efficiently aggregate those preferences to iden…


 Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

   

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network perfo…


 DESPOT: Online POMDP Planning with Regularization

The partially observable Markov decision process (POMDP) provides a principled general framework for planning under uncertainty, but solving POMDPs optimally is computationally intractable, due to the “curse of dimensionality” and the “curse of history”. To overcome these ch…


 Multi-Modal Multi-Task Deep Learning for Autonomous Driving

 

Several deep learning approaches have been applied to the autonomous driving task, many employing end-to-end deep neural networks. Autonomous driving is complex, utilizing multiple behavioral modalities ranging from lane changing to turning and stopping. However, most existing approaches do not fac…


 SKOS Concepts and Natural Language Concepts: an Analysis of Latent Relationships in KOSs

 

The vehicle to represent Knowledge Organization Systems (KOSs) in the environment of the Semantic Web and linked data is the Simple Knowledge Organization System (SKOS). SKOS provides a way to assign a URI to each concept, and this URI functions as a surrogate for the concept. This fact makes of ma…


 A Causal And-Or Graph Model for Visibility Fluent Reasoning in Human-Object Interactions

 

Tracking humans that are interacting with the other subjects or environment remains unsolved in visual tracking, because the visibility of the human of interests in videos is unknown and might vary over times. In particular, it is still difficult for state-of-the-art human trackers to recover compl…


 Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting

   

The goal of traffic forecasting is to predict the future vital indicators (such as speed, volume and density) of the local traffic network in reasonable response time. Due to the dynamics and complexity of the traffic network flow, typical simulation experiments and classic statistical methods cann…


 Towards personalized human AI interaction – adapting the behavior of AI agents using neural signatures of subjective interest

  

Reinforcement Learning AI commonly uses reward/penalty signals that are objective and explicit in an environment — e.g. game score, completion time, etc. — in order to learn the optimal strategy for task performance. However, Human-AI interaction for such AI agents should include additi…


 Robust Physical-World Attacks on Deep Learning Models

   

Although deep neural networks (DNNs) perform well in a variety of applications, they are vulnerable to adversarial examples resulting from small-magnitude perturbations added to the input data. Inputs modified in this way can be mislabeled as a target class in targeted attacks or as a random class …


 Imitation Learning for Vision-based Lane Keeping Assistance

 

This paper aims to investigate direct imitation learning from human drivers for the task of lane keeping assistance in highway and country roads using grayscale images from a single front view camera. The employed method utilizes convolutional neural networks (CNN) to act as a policy that is drivin…


 Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks

   

Deep learning has become the state of the art approach in many machine learning problems such as classification. It has recently been shown that deep learning is highly vulnerable to adversarial perturbations. Taking the camera systems of self-driving cars as an example, small adversarial perturbat…


 Autonomous Quadrotor Landing using Deep Reinforcement Learning

  

Landing an unmanned aerial vehicle on a ground marker is an open problem despite the effort of the research community. Previous attempts mostly focused on the analysis of hand-crafted geometric features and the use of external sensors in order to allow the vehicle to approach the land-pad. In this …


 A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

     

Gated Recurrent Unit (GRU) is a recently published variant of the Long Short-Term Memory (LSTM) network, designed to solve the vanishing gradient and exploding gradient problems. However, its main objective is to solve the long-term dependency problem in Recurrent Neural Networks (RNNs), which prev…


 Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning

   

Internet traffic classification has become more important with rapid growth of current Internet network and online applications. There have been numerous studies on this topic which have led to many different approaches. Most of these approaches exploit predefined features extracted by an expert in…


 Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge

     

Multiple automakers have in development or in production automated driving systems (ADS) that offer freeway-pilot functions. This type of ADS is typically limited to restricted-access freeways only, that is, the transition from manual to automated modes takes place only after the ramp merging proce…


 Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins

 

Vehicle climate control systems aim to keep passengers thermally comfortable. However, current systems control temperature rather than thermal comfort and tend to be energy hungry, which is of particular concern when considering electric vehicles. This paper poses energy-efficient vehicle comfort c…


 Neural Networks for Safety-Critical Applications – Challenges, Experiments and Perspectives

 

We propose a methodology for designing dependable Artificial Neural Networks (ANN) by extending the concepts of understandability, correctness, and validity that are crucial ingredients in existing certification standards. We apply the concept in a concrete case study in designing a high-way ANN-ba…


 DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars

 

Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any human intervention. Most major manufacturers including Tesla, GM, Ford, BMW, and Waymo/Google are working on building and test…