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

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.

Figures (8)

• Figure 1: General pipeline for VEI scenario generation from naturalistic driving data.
• Figure 2: Vehicle and E-Scooter Interaction Scenario Reconstruction System Architecture
• Figure 3: Vehicle Longitudinal Control Flow Chart
• Figure 4: Reconstructed Scenario configured to induce collision (Top: Bird-eye View, Bottom: In-Vehicle Camera view). The trajectory of the ego vehicle is highlighted in solid red while the original waypoints of the e-scooter are marked in dashed white. The dashed green line indicates the actual trajectory of the e-scooter, whose deviation is due to the implementation of the SFM.
• Figure 5: GPS information of the example VEI scenario