Challenges to CV and AV Applications in Truck Freight Operations (2017)

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CRP Project HR 20-102(03) 1 C H A P T E R 1 Background Freight hauling trucking owners and operators have implemented many new truck-based technologies over the years, whether seeking direct or indirect economic benefits or perhaps in response to regulatory mandates. Many of these technologies are safety-related, such as electronic stability control systems, lane departure warning systems and forward collision warning or mitigation systems. Related developments in Connected Vehicle (CV) technologies hold the promise of an incrementally new generation of systems that create potentially new business cases and new regulations around the use of solutions based on these technologies. Many CV-based solutions build upon advanced safety technologies through the addition of communications capabilities from an individual truck to other trucks, other vehicle types, and the road infrastructure with a promise of providing even greater connectivity. Other CV solutions address benefits including improved operational efficiencies and environmental benefits. Automated Vehicle (AV) technologies further the value proposition by automating aspects of vehicular control functions such as braking, steering and throttling, so that they might occur without driver input. There has been substantial and growing interest and investment in the development of CV and AV technologies. The passenger vehicle market claims the major share of this investment, and indeed some core technologies and capabilities are applicable or extendable into the heavy trucking sector. Substantial investment specifically to address heavy trucking and freight movement is also being made, and indeed the sector’s unique characteristics present distinctive challenges as well as a different set of economic drivers, policy issues, and regulatory attention. This report is one in a series of National Cooperative Highway Research Program (NCHRP) reports looking at various aspects of readying for potential CV and AV deployments; its focus is on the unique features of these deployments in heavy trucking for freight movement. The safe and efficient movement of freight is a vital element of economic progress. Trucks carry most of the tonnage and value of freight in the U.S., and the American Trucking Associations (ATA) forecasts 27% growth in truck freight tonnage between 2016 and 2027. This projected growth will be both an opportunity and a challenge for the trucking industry to absorb, and it will stress an already stressed road infrastructure. The U.S. Department of Transportation’s (USDOT’s) Bureau of Transportation Statistics (BTS) forecasts that, assuming no change in network capacity, the number of miles of the National Highway System (NHS) with recurring congestion, and the number of large trucks is forecast to increase significantly between 2011 and 2040. On highways carrying more than 8,500 trucks per day, BTS forecasts that recurring congestion will slow traffic on close to 7,400 miles and create stop-and-go conditions on an additional 22,000 miles (BTS 2015). For the trucking industry, the cost of this congestion is substantial. The American Trucking Research Institute (ATRI) estimates that congestion caused over $49 billion in added operational costs to the trucking industry in 2014 in the U.S. (Torrey 2016). Much of this added cost is passed on to shippers and others throughout the freight supply chain. The trucking industry has historically embraced many new technologies, truck-based and otherwise, and integrated them into its operations. As connected and increasingly automated freight is integrated into current operations there will be greater flexibility in route selection and truck operations creating greater benefits than exists today with human driven vehicles. However, new technology brings both opportunities and challenges for planners and policymakers. While it promises increased capacity of existing roads and decreased environmental impacts, the extent of these benefits are uncertain at this time.

CRP Project HR 20-102(03) 2 In addition, decreases in operational cost are likely to increase truck freight demand. It is difficult to estimate how the dynamics between the increase in roadway capacity, due to truck platooning and, the increases in truck volume due to induced demand, will play in future. It is also difficult to predict how the market penetration of automated trucks will increase over time and where and how optimal benefits may be realized. These uncertainties make freight planning and the integration of autonomous and connected vehicle technologies both challenging and important. The benefits of CV and AV technologies will be significant only when their market penetration crosses some yet undetermined thresholds. Although freight may be an early adopter of the technology, the level of automation will be dictated by regional factors and the pace of technological development and its full market penetration, it will take time. Further, operation of automated trucks under mixed traffic conditions where they share roadways with non-automated trucks will pose additional challenges for planners. In addition to the basic requirements for the operation of automated vehicles in the form of better road infrastructure such as better ride quality and visible road markings, the efficient operation of automated trucks will require more dedicated freight facilities such as smoother curves for on ramps and off ramps, and gentle vertical curves that need to be planned in advance. Sometimes they may necessitate dedicated freight links. As infrastructure interventions to improve the surface quality and geometric design cannot be taken simultaneously for all links of a transportation network, some prioritization scheme needs to be devised for planning the staged construction. In summary, efficient and optimal planning initiatives need to be taken to harness the maximum benefits of these evolving technologies. This report touches upon some of these aspects related to freight planning under the CV and AV application environment. The development of CV and AV poses a challenge to regulators, policymakers and legislators. Currently the National Highway Traffic Safety Administration (NHTSA) regulates vehicle manufacturing and establishes the requirements that manufacturers must meet to ensure that their vehicles are considered safe. The Federal Motor Carrier Safety Administration (FMCSA) regulates interstate trucking, ensuring that the operation of these vehicles is consistent with the Federal Motor Carrier Safety Regulations (FMCSRs). States that accept Motor Carrier Safety Assistance Program (MCSAP) funds are required to adopt the FMCSRs for all commercial motor vehicles that require a commercial driver’s license (CDL) and apply those rules to intrastate operations. There are a few grandfathered exceptions. The licensing of drivers to operate vehicles is a third dimension to this issue. Licensing of drivers is done by state motor vehicle agencies. Drivers of heavy-duty vehicles in interstate commerce are required to obtain a CDL, the requirements for which are regulated by FMCSA. These licenses are issued by the states and again, there is some variation in how states issue licenses. The regulation of autonomous and connected vehicles may further complicate this current regulatory structure. For instance, an autonomous vehicle may require that states license the vehicle as a driver, or consider it as a driver some of the time, complicating NHTSA’s desire to ensure that vehicle regulations create a national market for vehicle manufacturers. Considering the vehicle as driver also has far reaching implications for the liability of manufacturers. Further, if autonomous and connected vehicles are only certified to operate on roadways meeting certain standards, such as a sufficient communications network, high quality lane striping or a divided limited access highway, the line between vehicle regulations, infrastructure and operations is further blurred. These types of regulations may require vehicle regulators to coordinate more closely with infrastructure providers. Automated or connected systems also change the nature of the thing being regulated. Thus while it may have previously been easy for inspectors to physically measure how much brake was left on a truck, it may be more difficult for inspectors to assess whether automatic braking is working properly. The millions of lines of software code required for autonomous and connected vehicles may be a black box to regulators, making it difficult to ensure the privacy, security and safety of these systems. Lastly, early adoption of truck platooning or other connected and autonomous vehicle features will run afoul of numerous state regulations on topics such as following distances or licensing for vehicle

CRP Project HR 20-102(03) 3 operation. Overall, navigating this patchwork of federal and state regulations, and crafting legislative or regulatory fixes to address them, will be a significant undertaking that will be required for the widespread adoption of CV and AV technologies. Project Objectives The objectives of this research project were to (1) identify and describe existing and emerging freight regulatory, planning, policy, and operational environments and challenges for connected and autonomous truck technologies; (2) identify public and private sector barriers to, and opportunities for implementation of these technologies in freight operations; and (3) propose next steps for addressing the challenges for deployment and adoption. It documents those considerations that are unique to this sector. The interested reader may wish to review complementary research projects within the NCHRP coordinated research program on CV and AV deployments, including the following:  Policy and Planning Actions to Internalize Societal Impacts of CV and AV Systems into Market Decisions (NCHRP 20-102(01))  Implications of Automation for Motor Vehicle Codes (NCHRP 20-102(07))  Cybersecurity Implications of CV/AV Technologies on State and Local Transportation Agencies (NCHRP 20-102(10))  Lessons Learned from Safety Pilot and Connected Vehicle Pilot Deployments (TBD)  Roadway Infrastructure Design Considerations for Operation of Automated Vehicles (TBD)  CV/AV Applications for State and Local Maintenance Vehicle Fleets (TBD) Project Scope Deployment of active safety systems in the trucking has been occurring for many years. Adaptive cruise control, for example, has been deployed in Class 8 heavy vehicles in the U.S. since 2008. As these systems, and now new systems, introduce on-board communications capabilities to communicate with other vehicles (V2V), with the infrastructure (V2I) and potentially to other general road users such as pedestrians or bicyclists (V2x), these systems enter the realm of CV and enter the scope of this report. While these safety-oriented applications will target use of dedicated short range communications (DSRC), they are not necessarily constrained to this technology; they may use cellular, wireless, or satellite connections, for example. The applications considered in this report are DSRC-focused, though recognizing implementations might be accomplished through other means. More research and testing have been completed around technologies and deployment for passenger vehicles than for freight carrying trucks. Some of the passenger vehicle research, including identification of future needs to promote widespread deployments of CV and AV technologies, has direct or indirect applicability to heavy trucking. This report focuses on that activity unique to heavy trucking while making note of and providing references where applicable, for relevant passenger vehicle research. Terminology SAE International defines six levels of driving automation for on-road vehicles including trucks (SAE International 2016), and NHTSA now uses this common taxonomy and definition. These definitions are:  No-Automation (SAE level 0): The full-time performance by the human driver of all aspects of the dynamic driving task, even when enhanced by warning or intervention systems.  Driver Assistance (SAE level 1): The driving mode-specific execution by a driver assistance system of either steering or acceleration/deceleration using information about the driving environment and with the expectation that the human driver perform all remaining aspects of the dynamic driving task.

CRP Project HR 20-102(03) 4  Partial Automation (SAE level 2): This level involves automation of at least two primary control functions designed to work in unison to relieve the driver of control of those functions. An example of combined functions enabling a Level 2 system is adaptive cruise control in combination with lane centering.  Conditional Automation (SAE level 3): The driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task with the expectation that the human driver will respond appropriately to a request to intervene.  High Automation (SAE level 4): The driving mode-specific performance by an automated driving system of all aspects of the dynamic driving task, even if a human driver does not respond appropriately to a request to intervene.  Full Automation (SAE level 5): The full-time performance by an automated driving system of all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver. The stakeholder community has not definitively settled on terminology that is used to describe the automation of vehicular control functions. The terms “automated” and “autonomous” are both used, and with somewhat inconsistent definitions. In a more broad and historical context, “automation” refers to machine-based control of some system, which is a lower-level function than “autonomous”, in which the system in question is capable of acting on its own with self-governance. Using such a definition, some consider SAE levels 1 and 2 to constitute “automation”, and SAE levels 3-5 as “autonomous.” But in the vehicular context, a distinction is made by some regarding whether the vehicle (truck in this case) is acting on its own, from its own sensors (“autonomously”) versus acting in a connected environment (“automated”). Thus a solution that would be considered SAE level 3, 4, or 5 might be called automated or autonomous depending on whether it is acting in a connected manner. In this report we reference a substantial body of research that use both of these terms, and we use the published term. The distinction in definitions, though not subtle, is in many contexts unimportant for understanding meaning. Where there is importance, we have tried to be explicit in definitions rather than rely on ambiguous terms, and use the SAE definitions as a definitive guidepost. We also note that both the U.S. DOT and SAE International have de-emphasized the term “autonomous” as a label applied to driving automation. Further, throughout this report we refer to specific level(s) of automation when this context is important, rather than using labels such as “self-driving” or “driverless.” Though popular in usage, they also have a lack in precision and agreement to their definition. We do nonetheless substitute the term “highly automated” as a means of convenience for referring to trucks with SAE levels 3-5 capability. This is consistent with the term “highly automated vehicles”, or HAVs that is emerging and has been advanced by NHTSA as a term referring to vehicles with SAE levels 3-5 operation.