V2I-Based Platooning Design with Delay Awareness

This paper studies the vehicle platooning system based on vehicle-to-infrastructure (V2I) communication, where all the vehicles in the platoon upload their driving state information to the roadside unit (RSU), and RSU makes the platoon control decisions with the assistance of edge computing. By addressing the delay concern, a platoon control approach is proposed to achieve plant stability and string stability. The effects of the time headway, communication and edge computing delays on the stability are quantified. The velocity and size of the stable platoon are calculated, which show the impacts of the radio parameters such as massive MIMO antennas and frequency band on the platoon configuration. The handover performance between RSUs in the V2I-based platooning system is quantified by considering the effects of the RSU's coverage and platoon size, which demonstrates that the velocity of a stable platoon should be appropriately chosen, in order to meet the V2I's Quality-of-Service and handover constraints

Dynamic Lane-Changing Trajectory Planning for Autonomous Vehicles Based on Discrete Global Trajectory

Automatic lane-changing is a complex and critical task for autonomous vehicle control. Existing researches on autonomous vehicle technology mainly focus on avoiding obstacles; however, few studies have accounted for dynamic lane changing based on some certain assumptions, such as the lane-changing speed is constant or the terminal state is known in advance. In this study, a typical lane-changing scenario is developed with the consideration of preceding and lagging vehicles on the road. Based on the local trajectory generated by the global positioning system, a path planning model and a speed planning model are respectively established through the cubic polynomial interpolation. To guarantee the driving safety, passenger comfort and vehicle efficiency, a comprehensive trajectory optimization function is proposed according to the path planning model and speed planning model. In addition, a dynamic decoupling model is established to solve the problems of real-time application to provide viable solutions. The simulations and real vehicle validations are conducted, and the results highlight that the proposed method can generate a satisfactory lane-changing trajectory for automatic lane-changing actions

Identification of Health, Safety and Environmental (HSE) Parameters Affecting Cloud Computing in Providing Intelligent Services in Rail Transportation System

Background and Objective: The present study was designed and conducted to identify and determine the parameters of health, safety, and environment (HSE) affecting cloud computing in providing intelligent services in the rail transportation system. Materials and Methods: This cross-sectional study was carried out based on the Delphi technique and expert opinions on the rail transportation system in 2020. This research was performed in five steps, including a comprehensive review of the related literature, identification, presentation of HSE parameters affecting cloud computing in providing intelligent services in the rail transportation system, and three Delphi rounds. Sixteen experts participated in the field of HSE and rail transportation. The coefficient of variation (CV) and desirability of each parameter were considered at < 20% and ≥ 4, respectively. Results: Based on this Delphi study, 15 parameters related to HSE and influential on cloud computing technology in the provision of intelligent services in the rail transportation system were introduced. Moreover, the CV index was estimated at 8.0%. The parameters of future research, the existence of a skilled workforce, and cloud service resource management tools had the highest degree of desirability (4.875). Conclusion: The findings indicated that identifying functions and challenges of HSE regarding cloud computing technology in the rail transportation system could help decision-makers to improve effective services in the rail transportation system and reduce the associated risks

Data Substantiation in Mobility

The world is embracing the presence of connected autonomous vehicles which are expected to play a major role in the future of intelligent transport systems. Given such connectivity, vehicles in the networks are vulnerable to making incorrect decisions due to anomalous data. No sophisticated attacks are required; just a vehicle reporting anomalous speeds would be enough to disrupt the entire traffic flow. Detection of such anomalies is vital to ensure the security of a vehicular network. This thesis proposes the use of traffic flow theory for anomalous data detection in vehicular networks, by evaluating the consistency of microscopic parameters which are derived by traffic flow theory with macroscopic views of traffic under different traffic conditions. Though little attention has been given to using traffic flow properties to determine anomalous basic safety message data, the fundamental nature of traffic flow properties makes it a robust assessment tool. The aim of this thesis is to develop a robust data substantiation framework for vehicular networks using traffic flow fundamentals. The aim is fulfilled in three objectives; (1) to provide an overview of the context in terms of existing data substantiation methods, vehicular communication, and traffic flow theory, (2) to develop data substantiation models to detect anomalies irrespective of the cause of the anomality, and (3) to assess the applicability of traffic flow theory for data substantiation in vehicular networks. Chapters 1 and 2 are introductions and literature reviews respectively. The first main chapter describes the context of vehicular networks, traffic flow theory, and the intuition of applying traffic flow theory for substantiation in vehicular networks. The next three chapters elaborate, formulate, demonstrate, and evaluate the use of macroscopic views of traffic to substantiate microscopic data in vehicular networks. The first of these discusses the use of steady state conditions in traffic flow theory to substantiate data in vehicular networks, and the second describes the use of shockwave theory in traffic to substantiate data in vehicular networks. The third chapter develops a data substantiation model utilising localised views of traffic to provide an additional resolution to the previous models

Doctor of Philosophy

dissertationThe Active Traffic and Demand Management (ATDM) initiative aims to integrate various management strategies and control measures so as to achieve the mobility, environment and sustainability goals. To support the active monitoring and management of real-world complex traffic conditions, the first objective of this dissertation is to develop a travel time reliability estimation and prediction methodology that can provide informed decisions for the management and operation agencies and travelers. A systematic modeling framework was developed to consider a corridor with multiple bottlenecks, and a series of close-form formulas was derived to quantify the travel time distribution under both stochastic demand and capacity, with possible on-ramp and off-ramp flow changes. Traffic state estimation techniques are often used to guide operational management decisions, and accurate traffic estimates are critically needed in ATDM applications designed for reducing instability, volatility and emissions in the transportation system. By capturing the essential forward and backward wave propagation characteristics under possible random measurement errors, this dissertation proposes a unified representation with a simple but theoretically sound explanation for traffic observations under free-flow, congested and dynamic transient conditions. This study also presents a linear programming model to quantify the value of traffic measurements, in a heterogeneous data environment with fixed sensors, Bluetooth readers and GPS sensors. It is important to design comprehensive traffic control measures that can systematically address deteriorating congestion and environmental issues. To better evaluate and assess the mobility and environmental benefits of the transportation improvement plans, this dissertation also discusses a cross-resolution modeling framework for integrating a microscopic emission model with the existing mesoscopic traffic simulation model. A simplified car-following model-based vehicle trajectory construction method is used to generate the high-resolution vehicle trajectory profiles and resulting emission output. In addition, this dissertation discusses a number of important issues for a cloud computing-based software system implementation. A prototype of a reliability-based traveler information provision and dissemination system is developed to offer a rich set of travel reliability information for the general public and traffic management and planning organizations

A Millimeter Wave based Sensor Data Broadcasting Scheme for Vehicular Communications

In recent years, vehicles are becoming smart with the aid of various onboard sensing, communication and computing capability, which is helpful to improve road safety and driving experiments. With data fusion technique, a vehicle can even increase the driving safety by obtaining sensor data from other vehicles. The millimeter Wave (mmWave) based Vehicle-to-Vehicle (V2V) communication technology has become a promising technology to transmit sensor data in huge size such as video streams. However, the high radio frequency of mmWave makes it vulnerable to obstacles. Furthermore, the directional propagation property is not efficient to broadcast information among vehicles. In this paper, we propose a broadcasting scheme to guarantee each vehicle to get the sensor data of all other vehicles. Head vehicles are selected to gather the information on the environment and decide those transmission vehicles and receiving vehicles in each time slot. A graph-based routing selection algorithm is proposed with relatively low complexity. Moreover, the upper bound of broadcasting delay for one dimensional platoon is analyzed based on the network calculus theory. Simulation results indicate that the proposed scheme has faster delivery rate compared to the traditional First-In-First-Out (FIFO) scheme. The maximum broadcasting delay of the proposed scheme is less than the traditional schemes about 30% in different scenarios

Advances in Automated Driving Systems

Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic

Proceedings, MSVSCC 2012

Proceedings of the 6th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2012 at VMASC in Suffolk, Virginia

Developing an advanced collision risk model for autonomous vehicles

Aiming at improving road safety, car manufacturers and researchers are verging upon autonomous vehicles. In recent years, collision prediction methods of autonomous vehicles have begun incorporating contextual information such as information about the traffic environment and the relative motion of other traffic participants but still fail to anticipate traffic scenarios of high complexity. During the past two decades, the problem of real-time collision prediction has also been investigated by traffic engineers. In the traffic engineering approach, a collision occurrence can potentially be predicted in real-time based on available data on traffic dynamics such as the average speed and flow of vehicles on a road segment. This thesis attempts to integrate vehicle-level collision prediction approaches for autonomous vehicles with network-level collision prediction, as studied by traffic engineers. [Continues.

The r-evolution of driving: from Connected Vehicles to Coordinated Automated Road Transport (C-ART)

Connected and automated vehicles could revolutionise road transport. New traffic management approaches may become necessary, especially in light of a potential increase in travel demand. Coordinated Automated Road Transport (C-ART) is presented as a novel approach that stakeholders may consider for an eventual full realisation of a safe and efficient mobility system.JRC.C.4-Sustainable Transpor