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Suggested Citation:"Definitions of Automated Vehicles and Connected Vehicles." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 1: Executive Summary. Washington, DC: The National Academies Press. doi: 10.17226/25319.
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Suggested Citation:"Definitions of Automated Vehicles and Connected Vehicles." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 1: Executive Summary. Washington, DC: The National Academies Press. doi: 10.17226/25319.
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Suggested Citation:"Definitions of Automated Vehicles and Connected Vehicles." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 1: Executive Summary. Washington, DC: The National Academies Press. doi: 10.17226/25319.
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4 Definitions of Automated Vehicles and Connected Vehicles Automated vehicle (AV) technologies represent a switch in responsibility for the task of driv- ing from human to machine. They encompass a diverse range of automated technologies, from relatively simple driver assistance systems to fully automated vehicles. An autonomous vehicle is one in which there is no human driver and the levels of vehicle automation are higher. A fully automated vehicle does not require a steering wheel, accelerator, or brake pedal. All driving functionality is handled through onboard computers, software, maps, and radar and light detec- tion and ranging (lidar) sensors. Because most traffic crashes are caused by human error, the safety benefits AVs could provide are compelling—although incontrovertible empirical proof that AVs deliver safety benefits has yet to be produced. Other potential benefits are related to congestion mitigation, air pollution, greenhouse gas (GHG) reduction, and mobility enhance- ment for underserved populations. With advancements in artificial intelligence—particularly in areas of big data analytics, machine learning, and knowledge management—rapid progress is being made in terms of development and deployment of automated vehicles (AVs). AVs can be further distinguished as being connected or not. Connectivity is seen by many to be a major enabler for driverless vehicles in the medium term. A connected vehicle (CV), in contrast, has internal devices that enable it to communicate wirelessly with other vehicles, as in vehicle-to-vehicle (V2V) communication, or with an intel- ligent roadside unit, as in vehicle-to-infrastructure (V2I) communication. The acronym V2X is sometimes used to designate vehicle-to-everything (including pedestrian and bicyclist) commu- nication. Data communications that enable real-time driver advisories and warnings of immi- nent threats and hazards on the roadway are the foundation of CVs. DSRC and 4G-LTE are two candidate schemes for CV applications, and 5G is on the horizon. Levels of Automation The National Highway Traffic Safety Administration (NHTSA) has adopted a framework for automated driving developed by SAE International that categorizes automation into six levels. Vehicles with Levels 0, 1, and 2 technologies are already available for private ownership and currently operate on public roadways. Some observers believe that current Level 1 and Level 2 technology could have a major impact on safety. Levels 0, 1, and 2 are defined as follows: • Level 0 involves no automation. • Level 1 is referred to as “driver assistance.” The driver is in control but has the option of assis- tance with some steering or braking and accelerating. However, the automated driving system cannot operate both steering and speed at the same time. • Level 2 is referred to as “partial automation.” The automated driving system can execute both steering and braking or accelerating at the same time. The driver is responsible for monitoring the driving environment at all times and taking control of the vehicle when needed.

Definitions of Automated Vehicles and Connected Vehicles 5 NHTSA categorizes vehicles with Levels 3, 4, and 5 tech- nologies as automated driving systems (ADSs). Vehicles with ADSs are still in development, and automakers and technology firms are actively testing them on public roads. Levels 3, 4, and 5 are defined as follows: • Level 3 is called “conditional automation.” The automated driving system can operate the vehicle in certain conditions, but the driver is a necessity. The driver can take his or her hands off the wheel and perform other activities, such as reading text messages. However, the driver must be ready to take control when alerted by the system. • Level 4 is “high automation.” The vehicle can do every- thing, but only in certain geographic areas or on specific road types, such as in a particular area within the city limits or in a designated self-driving vehicle lane on a highway. The driver might still need to control the vehicle when the vehicle is outside of these areas. • Level 5 is “full automation.” ADSs are fully autonomous in any condition or environment without a human driver or occupant. Uncertain CAV Adoption Timelines Adoption timelines remain uncertain. For the purposes of this report on planning and model- ing tools, three general phases or categories of adoption were assumed. While the information below suggests evolutionary growth of CAVs, the authors acknowledge that this is not a con- sensus view. • Testing and early deployments: Currently, most vehicles on the road are at Level 1. The transition to Level 2 or Level 3 vehicles will be influenced by fleet turnover rates. With people keeping their vehicles on average for about 7 years, and with an average age of vehicles on the road of 11 years, it will take decades to obtain saturation of Level 4 or 5 vehicles. • Consumer initial adoption: Growth in Level 4+ to 50% or more of the overall vehicle fleet will take time. Level 4–5 vehicles entail self-driving operations. Road operators need to imple- ment coordinated rules of the road for their safe operation. An owner of a private vehicle may not want to pay a high purchase price for a vehicle that is initially geographically constrained in its sphere of operations. • System-level organization as CAVs become predominant on the road: Traffic will be pre- dominantly Level 4+ CAVs. Usage will be widespread enough to achieve systematic route and flow optimization, practically eliminating delay due to congestion. Modeling and planning tools can be developed to address the short-, mid-, and long-term impacts on travel behavior that each of these conditions promulgates. • In the short term, many existing planning and modeling tools will suffice, as travel behavior changes will not be significant other than increasing use of new modes, such as TNCs, and perhaps new types of access and egress options for public transportation systems. • In the mid-range, the operational characteristics of CAVs will become more widespread, and non-AVs will be either minimized in the fleet (by natural attrition) or regulated in such a way that their usefulness and attractiveness to buyers/riders are limited. The existence of non-AVs in the fleet becomes a problem for system operations because AVs can be controlled by route and operational functions while competing for roadway maneuvering space with manual vehicles that are unpredictable in their behavior. Modeling and planning tools need to address Modeling and planning tools can be developed to address short-, mid-, and long-term impacts on travel behavior.

6 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles this important phase of market penetration and must be able to present problems related to having mixed fleets of CAVs and non-CAVs. • In the longer term, the technology will be pervasive and will require a complete set of new assumptions about urban form, land use, parking requirements, and other indirect impacts in addition to the direct impacts on travel behavior and choice. Planning tools and the mod- els that support them will need to be based on scenario assumptions for this longer-range timeframe.

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Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 1: Executive Summary Get This Book
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TRB's National Cooperative Highway Research Program (NCHRP) Research Report 896: Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 1: Executive Summary summarizes guidelines to help agencies update their modeling and forecasting tools to address expected impacts of connected and automated vehicles (CAVs) on transportation supply, road capacity, and travel demand components.

Under requirements for long-range transportation planning, state departments of transportation and regional metropolitan planning organizations are required to have a multimodal transportation plan with a minimum time horizon of 20 years.

Because manufacturers and shared fleet operators suggest that CAVs will be present on the highway system in significant numbers well before 2038, the planning community will require procedures and methods to address the potential positive and negative direct and indirect outcomes from their deployment.

Volume 1 conveys the key findings of NCHRP Research Report 896: Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Volume 2 explores ways to develop new planning and modeling processes that include CAVs in the transportation environment.

The research report is accompanied by a PowerPoint presentation that can be adapted for presentations to agency decision makers.

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