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

TL;DR

This study analyzes field data from autonomous shuttles to develop a car-following model that improves passenger comfort and operational planning, revealing unique driving characteristics and calibrating existing models for better accuracy.

Contribution

It provides the first field-data-based car-following model for autonomous shuttles, with insights into their driving performance and recommended trajectory adjustments.

Findings

Autonomous shuttles exhibit higher jerk values affecting comfort.

Calibrated ACC model better predicts AS spacing and speed.

AS shows lower peak acceleration and higher deceleration than existing models.

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 to assess and plan for AS operations. To fill this gap, this study collected field data from AS, analyzed their driving performance, and suggested changes in the AS trajectory model to improve passenger comfort. A sample was collected with more than 4000 seconds of AS following a conventional car. The sample contained GPS positions from both AS and conventional vehicles. Latitude and longitude positions were used to calculate the speed, acceleration, and jerk of the leader and follower. The data analyses indicated that AS have 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 has lower peak acceleration and higher deceleration than the parameters that were calibrated for autonomous vehicle models in other research