Design and Evaluation of Data Dissemination Algorithms to Improve Object Detection in Autonomous Driving Networks

In the last few years, the amount of information that is produced by an autonomous vehicle is increasing proportionally with the number and resolution of sensors that cars are equipped with. Cars can be provided with cameras and Light Detection and Ranging (LiDAR) sensors, respectively needed to obtain a two-dimensional (2D) and three-dimensional (3D) representation of the environment. Due to the huge amount of data that multiple self-driving vehicles can push over a communication network, how these data are selected, stored, and sent is crucial. Various techniques have been developed to manage vehicular data; for example, compression can be used to alleviate the burden of data transmission over bandwidth-constrained channels and facilitate real-time communications. However, aggressive levels of compression may corrupt automotive data, and prevent proper detection of critical road objects in the scene. Along these lines, in this thesis, we studied the trade-off between compression efficiency and accuracy. To do so, we considered synthetic automotive data generated from the SELMA dataset. Then, we compared the performance of several state-of-the-art algorithms, based on machine learning, for compressing and detecting objects on LiDAR point clouds. We were able to reduce the point cloud by tens to hundreds times without any significant loss in the final detection accuracy. In a second phase, we focused our attention on the optimization of the number and type of sensors that are more meaningful to object detection operations. Notably, we tested our dataset on a sensor fusion algorithm that can combine both 2D and 3D data to have a better understanding of the environment. The results show that, although sensor fusion always achieves more accurate detections, using 3D inputs only can obtain similar results for large objects while mitigating the burden on the channel.In the last few years, the amount of information that is produced by an autonomous vehicle is increasing proportionally with the number and resolution of sensors that cars are equipped with. Cars can be provided with cameras and Light Detection and Ranging (LiDAR) sensors, respectively needed to obtain a two-dimensional (2D) and three-dimensional (3D) representation of the environment. Due to the huge amount of data that multiple self-driving vehicles can push over a communication network, how these data are selected, stored, and sent is crucial. Various techniques have been developed to manage vehicular data; for example, compression can be used to alleviate the burden of data transmission over bandwidth-constrained channels and facilitate real-time communications. However, aggressive levels of compression may corrupt automotive data, and prevent proper detection of critical road objects in the scene. Along these lines, in this thesis, we studied the trade-off between compression efficiency and accuracy. To do so, we considered synthetic automotive data generated from the SELMA dataset. Then, we compared the performance of several state-of-the-art algorithms, based on machine learning, for compressing and detecting objects on LiDAR point clouds. We were able to reduce the point cloud by tens to hundreds times without any significant loss in the final detection accuracy. In a second phase, we focused our attention on the optimization of the number and type of sensors that are more meaningful to object detection operations. Notably, we tested our dataset on a sensor fusion algorithm that can combine both 2D and 3D data to have a better understanding of the environment. The results show that, although sensor fusion always achieves more accurate detections, using 3D inputs only can obtain similar results for large objects while mitigating the burden on the channel

DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft SystemsUnmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniquesconsume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection. In this paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, we conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of deep learning applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.info:eu-repo/semantics/publishedVersio

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Understanding Traffic Density from Large-Scale Web Camera Data

Understanding traffic density from large-scale web camera (webcam) videos is a challenging problem because such videos have low spatial and temporal resolution, high occlusion and large perspective. To deeply understand traffic density, we explore both deep learning based and optimization based methods. To avoid individual vehicle detection and tracking, both methods map the image into vehicle density map, one based on rank constrained regression and the other one based on fully convolution networks (FCN). The regression based method learns different weights for different blocks in the image to increase freedom degrees of weights and embed perspective information. The FCN based method jointly estimates vehicle density map and vehicle count with a residual learning framework to perform end-to-end dense prediction, allowing arbitrary image resolution, and adapting to different vehicle scales and perspectives. We analyze and compare both methods, and get insights from optimization based method to improve deep model. Since existing datasets do not cover all the challenges in our work, we collected and labelled a large-scale traffic video dataset, containing 60 million frames from 212 webcams. Both methods are extensively evaluated and compared on different counting tasks and datasets. FCN based method significantly reduces the mean absolute error from 10.99 to 5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.Comment: Accepted by CVPR 2017. Preprint version was uploaded on http://welcome.isr.tecnico.ulisboa.pt/publications/understanding-traffic-density-from-large-scale-web-camera-data

Embarking on the Autonomous Journey: A Strikingly Engineered Car Control System Design

openThis thesis develops an autonomous car control system with Raspberry Pi. Two predictive models are implemented: a convolutional neural network (CNN) using machine learning and an input-based decision tree model using sensor data. The Raspberry Module controls the car hardware and acquires real-time camera data with OpenCV. A dedicated web server and event stream processor process data in real-time using the trained neural network model, facilitating real-time decision-making. Unity and Meta Quest 2 VR set create the VR interface, while a generic DIY kit from Amazon and Raspberry PI provide the car hardware inputs. This research demonstrates the potential of VR in automotive communication, enhancing autonomous car testing and user experience.This thesis develops an autonomous car control system with Raspberry Pi. Two predictive models are implemented: a convolutional neural network (CNN) using machine learning and an input-based decision tree model using sensor data. The Raspberry Module controls the car hardware and acquires real-time camera data with OpenCV. A dedicated web server and event stream processor process data in real-time using the trained neural network model, facilitating real-time decision-making. Unity and Meta Quest 2 VR set create the VR interface, while a generic DIY kit from Amazon and Raspberry PI provide the car hardware inputs. This research demonstrates the potential of VR in automotive communication, enhancing autonomous car testing and user experience