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At least as safe as manned shipping? Autonomous shipping, safety and “human error”
Published in Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, Safety and Reliability – Safe Societies in a Changing World, 2018
T. Porathe, Å. Hoem, Ø. Rødseth, K. Fjørtoft, S.O. Johnsen
To simplify the definition of autonomous and unmanned, we will start with a concept borrowed from the US car industry and its definition of terminology for autonomous cars (SAE 2016). This is called the “Operational Design Domain” (ODD) which is the operational conditions that limits when and where a specific autonomous car can be used. The corresponding capabilities of the car and its control systems is the “Dynamic Driving Task” (DDT). The concept also includes the “DDT Fallback” which is procedures and safety guards that are built into the vehicle and control systems for handling situations when the ODD is exceeded. The DDT Fallback will bring the system to a “minimal risk condition” (SAE 2016). For a ship, we suggest renaming DDT to the “Dynamic Navigation Task” (DNT).
Constructing spatiotemporal driving volatility profiles for connected and automated vehicles in existing highway networks
Published in Journal of Intelligent Transportation Systems, 2022
Xing Fu, Qifan Nie, Jun Liu, Asad Khattak, Alexander Hainen, Shashi Nambisan
This study is particularly valuable for the early CAV adoption period when the transportation infrastructures are not fully ready for CAVs, and early adopters have to share the roads with conventional vehicles in the network. The information presented in this paper is important for both private and public stakeholders. The current operation and test of the autonomous vehicles are required to conduct under certain conditions, defined as the operational design domain (ODD) (US DOT, 2018). The vehicles must know the start and the end of the ODDs. As the ODD can be described by various features: road infrastructures, traffic dynamics, weather, lighting conditions, etc (US DOT, 2018). Different vehicle manufacturers could declare their own ODD features according to their automated driving systems (ADS) (General Motors 2018; NVIDIA, 2018; Waymo, 2017). They may compare the defined ODDs and driving volatility profiles to get a sense of whether the ADS can safely function in the existing road network with a large portion of conventional vehicles. The driving volatility profiles can work as a quantitative benchmark for different ODD declarations. For public agencies who plan to deploy CAVs or AVs, they may collect driving data and use the method proposed in this study to construct driving volatility profiles for the roads within their region. The agencies can distinctly identify the defects of the current network and accordingly develop plans or strategies to prepare the infrastructures for the arrivals of CAVs in the near future.