Dynamic Risk Generation for Autonomous Driving: Naturalistic Reconstruction of Vehicle-E-Scooter Interactions

TL;DR

This paper develops a simulation pipeline using real traffic data and a Social Force Model to generate synthetic vehicle-e-scooter interactions for testing autonomous vehicle safety systems.

Contribution

It introduces a configurable simulation framework that recreates dynamic, risk-prone interactions between vehicles and e-scooters to evaluate AD algorithms.

Findings

Simulation can generate more critical, riskier interactions.

The framework effectively tests vehicle collision avoidance systems.

Real-world data validates the simulation's practicality.

Abstract

The increasing, high-risk interactions between vehicles and vulnerable micromobility users, such as e-scooter riders, challenge vehicular safety functions and Automated Driving (AD) techniques, often resulting in severe consequences due to the dynamic uncertainty of e-scooter motion. Despite advances in data-driven AD methods, traffic data addressing the e-scooter interaction problem, particularly for safety-critical moments, remains underdeveloped. This paper proposes a pipeline that utilizes collected on-road traffic data and creates configurable synthetic interactions for validating vehicle motion planning algorithms. A Social Force Model (SFM) is applied to offer more dynamic and potentially risky movements for the e-scooter, thereby testing the functionality and reliability of the vehicle collision avoidance systems. A case study based on a real-world interaction scenario was conducted to verify the practicality and effectiveness of the established simulator. Simulation experiments successfully demonstrate the capability of extending the target scenario to more critical interactions that may result in a potential collision.