Evaluation Methodology of Leader-Follower Autonomous Vehicle System for Work Zone Maintenance

Mobile and slow-moving operations, such as striping, sweeping, bridge flushing, and pothole patching, are critical for efficient and safe operation of a highway transportation system. However, reducing hazards for roadway workers and achieving a safer environment for both roadway maintenance operators and the public is a challenging problem. In 2017 alone, a total of 158,000 vehicle crashes occurred in work zones in the U.S.A., accounting for 61,000 injuries. The autonomous truck-mounted attenuator (ATMA) vehicle, sometimes referred to as an autonomous impact protection vehicle (AIPV), offers a promising solution to eliminate injuries to roadway maintenance workers and the public. This paper presents the evaluation methodology for the ATMA system, as well as the outcomes of field testing in Sedalia, Missouri. To the best of the authors’ knowledge, this is the first academic research to focus on ATMA. The ATMA system is first reviewed, followed by an introduction to the field testing procedures that includes descriptions of test cases and data collected, and their format. An analysis methodology is then proposed to quantitatively evaluate the system’s performance, and statistical models and hypothesis testing procedures are developed and presented. The numerical analysis results from real-world field testing under a controlled environment are presented, and the ATMA system’s performance is summarized. This paper can serve as a reference for transportation agencies that are interested in deploying similar technologies or for academic researchers to assess characteristics of autonomous vehicles and to apply knowledge gained in transportation modeling and simulation practices.