Theses and Dissertations Collection

or decades, researchers on Vehicular Ad-hoc Networks vehicular ad-hoc networks (VANETs)

and autonomous vehicles presented various solutions for vehicular safety and autonomy,

respectively. Yet, the developed work in these two areas has been mostly conducted in

their own separate worlds, and barely affect one- another despite the obvious relation-

ships. In the coming years, the Internet of Vehicles Internet of Vehicles (IOV), encom-

passing sensing, communications, connectivity, processing, networking, and computation

is expected to bridge many technologies to offer value-added information for the navi-

gation of self-driving vehicles, to reduce vehicle on board computation, and to deliver

desired functionalities. Potentials for bridging the gap between these two worlds and

creating synergies of these two technologies have recently started to attract significant

attention of many companies and government agencies. We present a comprehensive

survey and an overview of the emerging key challenges related to the two worlds of Ve-

hicular Clouds (VCs) including communications, networking, traffic modelling, medium

access, VC Computing Vehicular Cloud Computing (VCC), VC collation strategies, secu-

rity issues, and autonomous driving Autonomous Driving (AD) including 3D environment

learning approaches and AD enabling deep-learning, computer vision and Artificial Intel-

ligence Artificial Intelligence (AI) techniques. We then discuss the recent related work

and potential trends on merging these two worlds in order to enrich vehicle cognition of

its surroundings, and enable safer and more informed and coordinated AD systems.

Modern vehicles are equipped with advanced communication, computation and storage

capabilities in On-Board Units (OBUs), that are used to form a Vehicular Cloud Vehicular

Cloud (VC) as coalitions of affordable resources to host infotainment applications. With

the limitation of static vehicular communication schemes and the computational capa-

bilities constraints in vehicular micro-datacenter, VCs have overcome these technological

limitations. VCs are supposed to maximize the usage of Vehicle to Infrastructure (V2I)

communications over Service Channels (SCHs) for non-safety applications while

maintaining reliable short-lived safety applications Vehicle to Vehicle Vehicle-to-Vehicle (V2V)

communications in the Dedicated Short Range Communication (DSRC) technology. We

present a novel Advanced Activity-Aware (AAA) scheme to enhance Multi-Channel Oper-

ations based on IEEE 1609.4 standard in MAC Protocol implemented in Wireless Access

in Vehicular Environments Wireless Access for Vehicular Environment (WAVE). The

developed AAA scheme relies on the awareness of the vehicular safety load. It aims at dy-

namically finding an optimal setup for switching between Service Channel Interval (SCHI)

and Control Channel Interval (CCHI) by decreasing every inactivity in the network. Our

scheme is implemented using NS3 and maintains the default Synchronization Interval

Synchronization Interval (SI), as defined by the standard in Vehicular Ad hoc Networks

(VANETs).

In addition, we evaluate a sequentially and a parallel CUDA-accelerated Markov De-

cision Process Markov Decision Process (MDP) based scheme and a fast greedy heuristics

algorithm to optimize the problem of vehicular task placement with both IEEE 1609.4 and

opportunistically available V2I of AAA scheme. We derive the system reward of Vehicular

Cloud Computing VCC by considering the overall utilization of the virtualized resources

of the distributed Vehicular Clouds (VCs) as well as the optimality of the solution of

placement of the vehicular Bag-of-Tasks (BOTs)

We present our vision to create such a beneficial link by designing a multimodal

scheme for object detection, recognition, and mapping based on the fusion of stereo camera

frames, point cloud Velodyne LIDAR scans, and Vehicle-to-Vehicle (V2V) Basic Safety

Messages Basic Safety Messages (BSMs) exchanges using VANET protocols. Exploiting

the high similarities in the underlying manifold properties of the three data sets, and their

high neighborhood correlation, the proposed scheme employs semi-supervised manifold

alignment to merge the key features of rich texture descriptions of objects from 2D images,

depth and distance between objects provided by 3D point cloud, and awareness of self-

declared vehicles from BSMs’ 3D information including the ones not seen by camera and

LIDAR. The proposed scheme is applied to create joint pixel-to-point-cloud and pixel-

to-V2V correspondences of objects in frames from the KITTI Vision Benchmark Suite,

using a semi-supervised manifold alignment, to achieve camera-LIDAR and camera-V2V

mapping of their recognized objects. We present the alignment accuracy results over 2

different driving sequences and illustrate the additional acquired knowledge of objects

from the various input modalities. We also study the effect of the number of neighbors

employed in the alignment process on the alignment accuracy.