Evaluating Interactions between Automated Vehicles and Cyclists using a coupled In-the-Loop Test Environment

TL;DR

This paper introduces a coupled in-the-loop test environment that enables realistic, bidirectional interaction between a real cyclist and an automated vehicle for safety evaluation, validated through experiments with an automated shuttle bus.

Contribution

It presents a novel integrated test setup combining Cyclist-in-the-Loop and Vehicle-in-the-Loop environments with a virtual environment for realistic interaction testing.

Findings

Feasibility of the coupled test environment demonstrated

Vehicle trajectories in real and virtual environments are comparable

Component latencies in the setup are quantified

Abstract

Testing and evaluating automated driving systems (ADS) in interactions with vulnerable road users (VRUs), such as cyclists, are essential for improving the safety of VRUs, but often lack realism. This paper presents and validates a coupled in-the-loop test environment that integrates a Cyclist-in-the Loop test bench with a Vehicle-in-the-Loop test bench via a virtual environment (VE) developed in Unreal Engine 5. The setup enables closed-loop, bidirectional interaction between a real human cyclist and a real automated vehicle under safe and controllable conditions. The automated vehicle reacts to cyclist gestures via stimulated camera input, while the cyclist, riding a stationary bicycle, perceives and reacts to the vehicle in the VE in real time. Validation experiments are conducted using a real automated shuttle bus with a track-and-follow function, performing three test maneuvers - straight-line driving with stop, circular track driving, and double lane change - on a proving ground and in the coupled in-the-loop test environment. The performance is evaluated by comparing the resulting vehicle trajectories in both environments. Additionally, the introduced latencies of individual components in the test setup are measured. The results demonstrate the feasibility of the approach and highlight its strengths and limitations for realistic ADS evaluation.