The FHWA Exploratory Advanced Research project “Heavy Truck Cooperative Adaptive Cruise Control: Evaluation, Testing, and Stakeholder Engagement for Near Term Deployment,” led by Auburn University, is performing research and evaluation to assess the commercial viability of truck platooning. Joining Auburn University on the team are partners Peloton Technology, Peterbilt Trucks, Meritor WABCO, and the American Transportation Research Institute (ATRI) (a research organization within the American Trucking Associations Federation). The lead organization within Auburn is the GPS and Vehicle Dynamics Laboratory (GAVLAB). For the particular form of Cooperative Adaptive Cruise Control (CACC) addressed here, the term “Driver Assistive Truck Platooning” (DATP) has been developed to support stakeholder engagement with the trucking industry. In DATP, two or more trucks are exchanging data, with one or more trucks closely following the leader. The technology basis includes radar (for longitudinal sensing), DSRC-based V2V communications (for low latency exchange of vehicle performance parameters between vehicles), satellite positioning (sufficient to discriminate in-lane communications from out-of-lane communications), actuation (for vehicle longitudinal control), and human-machine interfaces (with distinct modes for leading or following). As a Level 1 Automation system, only longitudinal control is automated; the driver remains fully responsible for steering and has the ability to override system brake or throttle commands at any time. The intent of this research is to investigate business factors of DATP operations and the extent of potential reductions in fuel consumption, as well as safety, system robustness, and transportation impacts. This document provides a summary of Phase I results. For this project, the going-in hypothesis is that “DATP technology is near market-ready for industrial use and will provide value in specific roadway and operating conditions for heavy truck fleet operations.” This research addresses the necessary technical work, evaluation, and industry engagement to identify the key questions that must be answered prior to market introduction of heavy truck DATP, and to answer those questions. These questions must address industry needs as well as the needs of other highway travelers relating to traffic flow and safety. DATP builds on Adaptive Cruise Control, which has been available to the trucking industry for several years (approximately 100,000 ACC-equipped Class 8 trucks are on the road now). DATP has significant positive safety and fuel savings potential for heavy truck operations beyond what ACC can deliver alone. DATP also extends the functionality of forward collision mitigation systems (CMS), and may provide fleet users with extra incentives for CMS adoption due to prospective safety and fuel savings. The fuel savings offered by DATP are additive with fuel gains achieved with Predicitive Powertrain systems using 3-D mapping technology. Long haul trucking alone represents more than 10% of US oil use, with fuel representing 41% of fleet operating expenses. Regarding fuel economy, previous testing has shown that due to aerodynamic drafting effects, DATP has the potential to significantly reduce fuel use: on the order of 4% for the lead truck and 10% for the following truck. In terms of safety, the radar-based system provides an additional level of situational awareness to the driver whether DATP is activated or not. The most common highway accident for heavy trucks is frontal collisions. A DATP system enable the following truck(s) to react more quickly to changes in speed by the lead truck. This provides a faster reaction to upcoming hazards by the following truck than is available from current systems. Notably, truck fleets can proceed with implementing DATP near-term regardless of the regulatory timeline for DSRC. Phase I work included development of a DATP Concept of Operations and System Requirements document and a DATP Requirements document. The ConOps section addresses operational needs, useroriented operations, the system approach, the operational environment, the support environment, and operational scenarios. The Systems Requirements section provides high-level system requirements and is organized into the major sections of Driver Role, On-Board System, and Inter-vehicle Communications. This document is attached here as an appendix. Phase I of this research found: • “Truckload” and line-haul “less-than-truckload” fleet operations appear to be a likely fit for early adoption of DATP. • A trucking industry survey conducted by the team included these findings (which should be considered preliminary since respondents had no experience with DATP at this early stage): o 54% of carriers and drivers had an average trip length of less than 500 miles, and 46% were over 500 miles in average trip lengths. Longer trip lengths in DATP operations are more likely to generate greater return on investment. o 54% of fleet managers indicated that the DATP systems would have a “very positive,” “somewhat positive,” or no impact on driver retention. 39% of fleet manager respondents feel that drivers are very likely, likely, or moderately likely to use a DATP system. Owner-operator response for driver retention or usage was more towards the negative end of this scale, however. o Owner-operators expected a mean DATP payback period of 10 months, while fleet respondents had a mean expectation of 18 months. • Typical automotive radar shows good tracking ability at headways expected in DATP operations. • In aerodynamics simulations, the follower vehicle appears to see large amounts of drag reduction, even distances greater than 100 feet. At closer distances these savings are beneficially compounded by lead vehicle drag reduction. The inter-vehicle distances required for leader fuel savings do not appear to be below the margin of safety for the DATP system to be operated. • Using data of actual truck movements on a section of highway, platoon formation modeling results were promising. Results showed platoon formation of 30-45% in one dataset, with those trucks remaining platooned between 55-75% of the 300-mile road segment. • Traffic modeling results showed that DATP caused no delays compared to existing conditions. Gaining significant benefits in traffic flow start to appear at truck market penetrations of over 60% with headways lower than 1.25 seconds (approximately 100 feet at 60 mph); DATP headways are expected to be significantly less than this. During 2015, Phase II work will focus on: a. System testing: equipping the Peterbilt tractors with the Peloton DATP system plus data acquisition, followed by performance testing at the test track in the areas of wireless communications, vehicle control, positioning, driver comfort, and safety. Additionally, the team will acquire suitable trailers for testing. On-road testing for fuel economy evaluations will be planned, arranged, and conducted. b. Human factors: providing driver training, assessing driver reactions to operating the system at several points during testing (both on-track and on-road). Interviews will be conducted posttraining and after 1-year to understand the impact from both training and hands-on experience towards acceptance, concerns, and potential pros. c. Wireless communications: on-track testing will seek to stress the DSRC system, which will be evaluated for packet loss, message delay, channel congestion, and other performance indices. Algorithms and protocols will be designed that improve scalability of DSRC. The team will evaluate an adaptive strategy that allows the DSRC subsystem to prioritize important safetyrelated messages differently during distinct driving modes. d. Aerodynamics modeling: developing models with greater detail to support more in-depth evaluations. This will include platoons of more than two vehicles. These models will be integrated with vehicle models to provide a comprehensive evaluation tool for DATP expected to be useful to system developers. e. Platoon formation: Future modeling with regard to assessing platoon formation will include extending the analysis of the ATRI-provided truck data for additional highway corridors, such as urban corridors, as well as incorporating differences in fuel economy benefits depending on platoon position. f. Traffic impacts evaluation: modeling different times of day, platoons of more than two vehicles, other highway types, and truck lane restrictions; to include varying truck percentages and modeling three- truck platoons. While the model implementation so far has been focused on freeways, an investigation on a rural arterial that is a major trucking route that is less controlled and includes traffic signals will be done. Finally, it is important to evaluate how traffic overall will operate in entry/exit situations. Driver Assistive Truck Platooning, if shown to be commercially viable, would lead to new levels of freight/fleet efficiency and improved mobility for all highway travelers, while substantially reducing trucking-based emissions and enhancing the V2X communications environment. Overall project results will constitute an important step towards realizing this potential. (Author/publisher)
Samenvatting