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 A Two-Phase Safe Vehicle Routing and Scheduling Problem: Formulations and Solution Algorithms

We propose a two phase time dependent vehicle routing and scheduling optimization model that identifies the safest routes, as a substitute for the classical objectives given in the literature such as shortest distance or travel time, through (1) avoiding recurring congestions, and (2) selecting rou…


 Laying Down the Yellow Brick Road: Development of a Wizard-of-Oz Interface for Collecting Human-Robot Dialogue

 

We describe the adaptation and refinement of a graphical user interface designed to facilitate a Wizard-of-Oz (WoZ) approach to collecting human-robot dialogue data. The data collected will be used to develop a dialogue system for robot navigation. Building on an interface previously used in the de…


 Distributed algorithm for empty vehicles management in personal rapid transit (PRT) network

In this paper, an original heuristic algorithm of empty vehicles management in personal rapid transit network is presented. The algorithm is used for the delivery of empty vehicles for waiting passengers, for balancing the distribution of empty vehicles within the network, and for providing an empt…


 Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control

  

Flow is a new computational framework, built to support a key need triggered by the rapid growth of autonomy in ground traffic: controllers for autonomous vehicles in the presence of complex nonlinear dynamics in traffic. Leveraging recent advances in deep Reinforcement Learning (RL), Flow enables …


 On a Formal Model of Safe and Scalable Self-driving Cars

In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of this paper is to add to the equation two additional crucial parameters. The first is standardization of sa…


 Towards lightweight convolutional neural networks for object detection

 

We propose model with larger spatial size of feature maps and evaluate it on object detection task. With the goal to choose the best feature extraction network for our model we compare several popular lightweight networks. After that we conduct a set of experiments with channels reduction algorithm…


 GaDei: On Scale-up Training As A Service For Deep Learning

   

Deep learning (DL) training-as-a-service (TaaS) is an important emerging industrial workload. The unique challenge of TaaS is that it must satisfy a wide range of customers who have no experience and resources to tune DL hyper-parameters, and meticulous tuning for each user’s dataset is prohi…


 Creating a Social Brain for Cooperative Connected Autonomous Vehicles: Issues and Challenges

The connected autonomous vehicle has been often touted as a technology that will become pervasive in society in the near future. Rather than being stand alone, we examine the need for autonomous vehicles to cooperate and interact within their socio-cyber-physical environments, including the problem…


 DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction

   

Traffic flow prediction is an important research issue to avoid traffic congestion in transportation systems. Traffic congestion avoiding can be achieved by knowing traffic flow and then conducting transportation planning. Achieving traffic flow prediction is challenging as the prediction is affect…


 Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

 

Enabling robots to autonomously navigate complex environments is essential for real-world deployment. Prior methods approach this problem by having the robot maintain an internal map of the world, and then use a localization and planning method to navigate through the internal map. However, these a…


 DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting

   

Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining roa…


 Traffic Optimization For a Mixture of Self-interested and Compliant Agents

This paper focuses on two commonly used path assignment policies for agents traversing a congested network: self-interested routing, and system-optimum routing. In the self-interested routing policy each agent selects a path that optimizes its own utility, while the system-optimum routing agents ar…


 A Simple Reinforcement Learning Mechanism for Resource Allocation in LTE-A Networks with Markov Decision Process and Q-Learning

  

Resource allocation is still a difficult issue to deal with in wireless networks. The unstable channel condition and traffic demand for Quality of Service (QoS) raise some barriers that interfere with the process. It is significant that an optimal policy takes into account some resources available …


 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…