Abstract

Information-aware connected and automated vehicles (CAVs) have drawn great attention in recent years due to its potentially significant positive impacts on roadway safety and operational efficiency. In this paper, we conduct an in-depth review of three basic and key interrelated aspects of a CAV: sensing and communication technologies, human factors, and information-aware controller design. First, different vehicular sensing and communication technologies and their protocol stacks, to provide reliable information to the information-aware CAV controller, are thoroughly discussed. Diverse human factor issues, such as user comfort, preferences, and reliability, to design the CAV systems for mass adaptation are also discussed. Then, different layers of a CAV controller (route planning, driving mode execution, and driving model selection) considering human factors and information through connectivity are reviewed. In addition, critical challenges for the sensing and communication technologies, human factors, and information-aware controller are identified to support the design of a safe and efficient CAV system while considering user acceptance and comfort. Finally, promising future research directions of these three aspects are discussed to overcome existing challenges to realize a safe and operationally efficient CAV.

Highlights

  • A PREPRINT - MARCH 22, 2019Automated vehicle (AV) field testing began in 1986 in the United States when the Partners for Advanced Transit and Highways (PATH) program at the University of California Berkley developed a platooning application of six AVs in specially guided highway sections [1]

  • We conducted an in-depth review of three key factors related to the design and implementation of the connected and automated vehicles (CAVs) systems

  • It is very important to ensure the full utilization of heterogeneous sensing and communication technologies to improve the safety and operational efficiency as well as user comfort of the CAV systems

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Summary

Introduction

Automated vehicle (AV) field testing began in 1986 in the United States when the Partners for Advanced Transit and Highways (PATH) program at the University of California Berkley developed a platooning application of six AVs in specially guided highway sections [1]. To facilitate the development of AV technologies, several US states issued special permits to AV technology manufactures conducting pilot testing, most notably in California, automated vehicle laws was issued on February 26, 2018 [5] This interest in AV technology from both the automotive industry and the public sector will advance the development of fully automated (i.e., autonomous or level 5 automation) vehicle development in the decade. Identifying the impacts of three major factors (sensing and communication technologies, human factors, and information-aware controller) on fully automated vehicular systems is necessary. Further advancement of fully automated vehicular systems depends on the current research trends of different aspects of vehicular sensing and communication techniques, human acceptance with their interjections, and information-aware controller designs. We reviewed existing literature related to CAV systems in terms of the design of sensing and communication technologies human factors and information-aware controller design.

Focus of the Paper
Sensor and Communication Technologies
Challenges: Here are a few challenges for in-vehicle sensor systems
Future Research Directions
Current Status
Challenges
Human Factors
Design of a CAV System Considering User Expectation
Adaption to the Designed CAV System by User
User Preferences Modelling
CAV’s System Failure
CAV User Preferences Modelling using Artificial Intelligence
Engaging in a Case of CAV’s System Failure
Integration of Multiple Assistance Systems in the HMI
Information-aware Controller Design
Route Planning Layer
Driving Mode Selection Layer
Driving Mode Execution Layer
Centralized versus Distributed Controller
Communication Imperfection
Mixed Traffic Scenario
Hybrid Structure with Centralized and Distributed Control Scheme
Communication Imperfection Resilient Control Scheme
Findings
Conclusions
Full Text
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