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Suggested Citation:"Chapter 2 - Definitions of CAVs and Current Status." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Suggested Citation:"Chapter 2 - Definitions of CAVs and Current Status." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Suggested Citation:"Chapter 2 - Definitions of CAVs and Current Status." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Page 9
Suggested Citation:"Chapter 2 - Definitions of CAVs and Current Status." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
×
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Suggested Citation:"Chapter 2 - Definitions of CAVs and Current Status." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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6 Definitions of AVs and CVs AV technologies represent a switch in responsibility for the task of driving from human to machine. They encompass a diverse range of automated technologies, from relatively simple driver assistance sys- tems 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, accel- erator, or brake pedal (see Figure 1). All driving functionality is handled through onboard computers, software, maps, and radar and light detection and ranging (lidar) sensors (see Figure 2). 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 enhancement for underserved populations, such as low- income people, older adults, persons with disabilities, and rural residents. With advance- ments in artificial intelligence—particularly in areas of big data analytics, machine learning, and knowledge management—rapid progress is being made in terms of AV development and deployment. 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 CV, in contrast, has internal devices that enable it to communicate wirelessly with other vehicles, as in V2V communication, or with an intelligent roadside unit, as in V2I communication. V2V applications enable crash prevention, and V2I applications enable telecommunica- tion, safety, mobility, and environmental benefits. The acronym V2X is sometimes used to designate vehicle-to-everything (including pedes- trian and bicyclist) communication. Data communications that enable real-time driver advisories and warnings of imminent threats and haz- ards on the roadway are the foundation of CVs (Hong et al. 2014). At present, V2I and V2V applications solely provide driver alerts; they do not control vehicle operations. Dedicated short-range communications (DSRC) and 4G-LTE are two candidate schemes for CV applications, and 5G is on the horizon. C H A P T E R 2 Definitions of CAVs and Current Status Chapter Highlights • Defines AVs and CVs. • Describes six levels of automated vehicles. • Summarizes the current state of AV and CV development and deployment. AVs encompass a range of automated technologies, from relatively simple driver assistance systems to fully autonomous or self-driving vehicles. CVs have internal devices that connect to other vehicles, other road users, or back-end infrastructure.

Definitions of CAVs and Current Status 7 Figure 2. AV technologies and levels of automation. Figure 1. Interior of a fully self-driving vehicle.

8 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles Levels of Automation The National Highway Traffic Safety Administration (NHTSA) has adopted a framework for automated driving developed by SAE International (2016) that categorizes automation into six levels. Vehicles with Levels 0, 1, and 2 technologies are already available for private owner- ship 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 at all. The driver executes all tasks involved in operating the vehicle. • Level 1 is referred to as “driver assistance.” At this level, the driver is in control but has the option of assistance with some tasks, such as steering or braking and accelerating. However, the automated driving system cannot operate both steering and speed at the same time. Basic cruise control falls into Level 1. • Level 2 is referred to as “partial automation.” At this level, 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. NHTSA categorizes vehicles with Levels 3, 4, and 5 technologies 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 referred to as “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, and the vehicle can drive for an extended period of time on its own. However, the driver must be ready to take control when alerted by the system. • Level 4 is referred to as “high automation.” When a vehicle is operating at this level, it can do everything, and the driver is not expected to take control. However, the automated driving system can only be in control 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 referred to as “full automation.” ADSs are fully autonomous in any condition or environment without a human driver or occupant. The driver is completely optional. Because drivers no longer need to control the vehicle, some Level 5 ADSs do not have a steering wheel, brake, or gas pedal. Current AV Context Many people believe that highly automated vehicles will first be available to consumers as SAVs (Reinventing Wheels 2018). Cost is a main factor. Lidar sensors are still too expensive to be used in mass-produced vehicles. The cost of this technology is considered less of a barrier for fleet vehicles because they generate revenue throughout the day to cover the expense, whereas the typical privately owned vehicle is used for a small fraction of a day. Related to this is the fact that the global shared mobility market was $54 billion in 2016 (Grosse-Ophoff et al. 2017). The United States is one of the largest shared mobility markets, at $23 billion. As of February 2018, testing of SAVs on public roads in the United States was occurring through 17 active pilots in eight states—Arizona, California, Florida, Massachusetts, Michigan, Pennsylvania, Texas, and Washington—by companies such as Waymo, Uber, EasyMile, Ford, Navya, GM Cruise, and Drive.ai (Stocker and Shaheen 2017). After the fatality caused by an Uber vehicle in Arizona in March 2018, Uber suspended testing in North America. The

Definitions of CAVs and Current Status 9 majority of these pilots are targeting Level 4 technology in which a human operator does not need to control the vehicle as long as it is operating in a suitable design domain given its capa- bilities. The pilots have been implemented as two types: (a) on private roads and in planned communities and (b) on public roads and city streets. Many original equipment manufacturers (OEMs) have made bold claims about when highly automated vehicles will be available to car-buying consumers (Fagella 2017). Renault–Nissan, under a new partnership with Microsoft, plans to release 10 different Level 4 vehicles by 2020. Volvo hopes to have a car that can drive fully autonomously on the highway by 2021. It envi- sions that full autopilot will be a highly enticing option on a premium vehicle and will initially be priced at $10,000. In 2015, Volvo became the first car company to promise to accept full liability whenever one of its cars is in autonomous mode. Hyundai is working on self-driving vehicles, but with more focus on affordability. It is developing a low-cost platform that can be installed in models the average consumer can afford and is targeting highway driving in 2020 and urban driving in 2030. BMW has a high-profile collaboration with Intel and Mobileye to develop autonomous cars, with the goal of getting highly and fully automated driving into series production by 2021. Still, due to regulatory, legal, or infrastructure readiness issues, the actual timeframe for deployment and adoption of these highly automated privately owned vehicles is hard to project. For example, associated research under the NCHRP 20-102(07) rubric, “Impli- cations of Automation for Motor Vehicle Codes,” provides guidance concerning legal changes that will result from the rollout of AVs. Current CV Context Several manufacturers, including Kapsch, Savari, Cohda Wireless, DENSO, and Arada Sys- tems, are actively developing and testing CV devices and applications. Other companies (e.g., Qualcomm, Savari) are developing V2X equipment that uses other forms of wireless communi- cations, including cellular, Wi-Fi, and Bluetooth. However, the U.S. Department of Transporta- tion (U.S. DOT) and others have been committed to DSRC being the primary mechanism for vehicle safety applications under the expectation of new Federal Motor Vehicle Safety Standards (FMVSS) to mandate V2V communications for new light vehicles and to standardize the mes- sage and format of the V2V transmissions. Nonetheless, as of 2018, such rulemaking has not advanced. In November 2017, NHTSA issued a statement that it had not made any final decision on the proposed rulemaking concerning a V2V mandate. The key enabler for CAVs is communication of location and status data and an ability to analyze and interpret data intelligently. While emerging forms of connectivity (e.g., DSRC, 5G mobile communications) offer promise for new communication services, many practical ben- efits of CAVs can be achieved over existing mobile networks. Coupling the development of CVs with the deployment of emerging communication standards may delay the societal benefits that CAVs can offer. The federal government has played a significant role in supporting the research, development, and piloting of CV technology. The U.S. DOT Connected Vehicle Safety Pilot Program sought to demonstrate that DSRC-based CV technology was ready for large-scale deployments. Executed in Ann Arbor, Michigan, this program equipped vehicles with vehicle awareness devices, after- market safety devices, and retrofit safety devices, and it deployed DSRC infrastructure to assess the functional performance of V2V and V2I safety applications (Bezzina and Sayer 2015). The U.S. DOT is also currently sponsoring three additional CV pilot deployments in New York, Wyoming, and Florida that are being rigorously evaluated to assess benefits: • The pilot program in New York is evaluating the use of CV technology in a dense urban envi- ronment with significant pedestrian and cyclist traffic in addition to vehicular traffic. In-vehicle

10 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles equipment has been installed on up to 10,000 city and fleet vehicles to test V2V applications such as intersection movement assist and forward collision warning, and on roadside infra- structure in Manhattan and Brooklyn to test V2I applications such as detection of pedestrians in signalized intersections and a red-light violation warning system (Galgano et al. 2016). • The pilot program in Wyoming focuses on applying CV technology along freight-intensive corridors that experience significant weather-related incidents and delays. DSRC onboard equipment is installed in a combination of maintenance vehicles, emergency vehicles, and private trucks, and roadside equipment is installed along Interstate 80 to communicate road conditions, variable speed limit zones, and detour information (Gopalakrishna et al. 2015). • The pilot program in Tampa, Florida, is evaluating CV technology deployed in a suburban-to- urban corridor with managed lanes that experiences significant congestion and delays while bringing thousands of vehicles to and from a dense urban center with high pedestrian traffic. V2V safety applications such as forward collision warning and intersection movement assist are being evaluated, as are V2I applications such as curve speed warning and transit signal priority (Waggoner et al. 2016). Significant research and standardization have gone into the development of CV technology specifically related to DSRC. SAE and IEEE have been actively working on standards for DSRC (SAE J2735; IEEE 1609.2, IEEE 1609.3, and IEEE 1609.4) and V2V performance (SAE J2945/1). The various DSRC manufacturers will be required to certify that their equipment conforms to these standards to ensure interoperability of vehicles from different OEMs using different hardware. The U.S. DOT has organized several CV PlugFests throughout the country at which CV vendors have been able to test their devices’ performance, interoperability with other equip- ment, and conformance to the aforementioned standards (Abuelhiga 2013). The U.S. DOT has indicated that CV technologies could be critical to the success of AVs’ safety. For example, connected technologies could help AVs maintain or improve situational aware- ness by communicating traffic control messages that camera- and radar-based crash avoidance technologies may not be able to detect because of obstructions such as buildings or fog. Addi- tionally, CVs have other potential benefits, including congestion mitigation and reduction of air pollution and GHGs. The U.S. DOT’s research and other efforts related to CVs historically have been largely independent of vehicle automation, but recent departmental efforts have sought to study the potential interactions and synergies between the two concepts.

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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 2: Guidance provides detailed information and guidelines for state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) to help update their modeling and forecasting tools. These tools address expected impacts of connected and automated vehicles (CAVs) on transportation supply, road capacity, and travel demand components. CAVs are likely to influence all personal and goods movement level of demand, travel modes, planning and investment decisions, physical transportation infrastructure, and geographic areas.

DOTs and regional MPOs are required to have a multimodal transportation plan with a minimum time horizon of 20 years under the requirements of the Moving Ahead for Progress in the 21st Century Act (MAP-21) requirements. This report explores ways to develop new planning and modeling processes that include CAVs in the transportation environment. The volume provides the details to NCHRP Research Report 896: Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 1.

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

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