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Technical Paper

Buckendale Lecture Series: Transformational Technologies Reshaping Transportation—A Government Perspective

2018-09-01
2018-01-2011
Transportation departments are under-going a dramatic transformation, shifting from organizations focused primarily on building roads to a focus on mobility for all users. The transformation is the result of rapidly advancing autonomous vehicle technology and personal telecommunication technology. These technologies provide the opportunity to dramatically improve safety, mobility, and economic opportunity for society and industry. Future generations of engineers and other transportation professionals have the opportunity to be part of that societal change. This paper will focus on the technologies state DOT’s and the private sector are researching, developing, and deploying to promote the future of mobility and improved efficiency for commercial trucking through advancements in truck platooning, self-driving long-haul trucking, and automated last mile distribution networks.
Technical Paper

Evaluating Trajectory Privacy in Autonomous Vehicular Communications

2019-04-02
2019-01-0487
Autonomous vehicles might one day be able to implement privacy preserving driving patterns which humans may find too difficult to implement. In order to measure the difference between location privacy achieved by humans versus location privacy achieved by autonomous vehicles, this paper measures privacy as trajectory anonymity, as opposed to single location privacy or continuous privacy. This paper evaluates how trajectory privacy for randomized driving patterns could be twice as effective for autonomous vehicles using diverted paths compared to Google Map API generated shortest paths. The result shows vehicles mobility patterns could impact trajectory and location privacy. Moreover, the results show that the proposed metric outperforms both K-anonymity and KDT-anonymity.
Technical Paper

Experimental Setup Enabling Self-Confrontation Interviews for Modelling Naturalistic Driving Behavior

2019-04-02
2019-01-1082
Behavioral models of traffic actors have a potential of unlocking sophisticated safety features and mitigating several challenges of urban automated driving. Intuitively, volunteers driving on routes of daily commuting in their private vehicles are the preferred source of information to be captured by data collection system. Such dataset can then serve as a basis for identifying efficient methods of context representation and parameterization of behavioral models. This paper describes the experimental setup supporting the development of driver behavioral models within the SIMUSAFE project. In particular, the paper presents an IoT data acquisition and analysis infrastructure supporting self-confrontation interviews with drivers. The proposed retrofit system was installed in private vehicles of volunteers in two European cities. Wherever possible, the setup used open source software and electronic components available on consumer market.
Research Report

Unsettled Topics Concerning Human and Autonomous Vehicle Interaction

2020-12-18
EPR2020025
This report examines the current interaction points between humans and autonomous systems, with a particular focus on advanced driver assistance systems (ADAS), the requirements for human-machine interfaces as imposed by human perception, and finally, the progress being made to close the gap. Autonomous technology has the potential to benefit personal transportation, last-mile delivery, logistics, and many other mobility applications enormously. In many of these applications, the mobility infrastructure is a shared resource in which all the players must cooperate. In fact, the driving task has been described as a “tango” where we—as humans—cooperate naturally to enable a robust transportation system. Can autonomous systems participate in this tango? Does that even make sense? And if so, how do we make it happen? Click here to access the full SAE EDGETM Research Report portfolio.
Journal Article

Using Delphi and System Dynamics for IoT Cybersecurity: Preliminary Airport Implications

2021-03-02
2021-01-0019
Day by day, airports adopt more IoT devices. However, airports are not exempt from possible failures due to malware’s proliferation that can abuse vulnerabilities. Computer criminals can access, corrupt, and extract information from individuals or companies. This paper explains the development of a propagation model, which started with a Delphi process. We discuss the preliminary implications for airports of the simulation model built from the Delphi recommendations.
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