Work

Exploratory Driving Performance and Car-Following Modeling for Autonomous Shuttles Based on Field Data

Year: 2025

Type: article

Abstract: Autonomous shuttles (AS) operate in several cities and have shown potential to improve the public transport network. However, there is no car-following model that is based on field data and allows decision-makers (planners, and traffic engineers) to assess and plan for AS operations. To fill this gap, this study collected field data from AS operations, analyzed their driving performance, and suggested changes in the AS trajectory model to improve passenger comfort. A sample was collected with over 4,000 seconds of data of AS following a conventional car (human driver). The sample contained GPS positions from both AS and conventional vehicles. Latitude and longitude coordinates were used to calculate the speed, acceleration, and jerk of the leader and follower. The data analyses indicated that AS has higher jerk values that may impact the passengers’ comfort. Several existing models were evaluated, and the researchers concluded that the calibrated ACC model resulted in lower errors for AS spacing and speed. The results of the calibration indicate that the AS exhibits lower peak acceleration and higher deceleration than those found in calibrated parameters of autonomous vehicle models from other studies.

Source: IEEE Transactions on Intelligent Transportation Systems

DOI: 10.1109/tits.2025.3552506

Authors: Renan Favero, Lily Elefteriadou

Institution: University of Florida

Language: English

Topic: Traffic control and management

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: green

Funder: U.S. Department of Transportation