Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving with Focus on Bicyclist Safety

2025-08-30

AI Summary

Autonomous driving systems exhibit insufficient safety guarantees for vulnerable road users (VRUs)—particularly cyclists—due to inadequate delay-tolerant control and the absence of standardized testing methodologies. Method: This paper proposes a unified planning and collision-avoidance framework integrating perception, trajectory prediction, automated steering, and braking control, augmented with a delay-tolerant control strategy. Leveraging vehicle-in-the-loop virtual environment (VVE) technology, we develop a high-fidelity simulation platform for cyclist–vehicle interaction and safety-critical decision validation. Contribution/Results: The proposed approach significantly improves collision-avoidance success rates in complex, dynamic traffic scenarios. Experimental validation demonstrates VVE’s effectiveness in VRU safety function development, evaluation, and scalable testing—addressing a critical gap in automated, cyclist-specific safety verification techniques.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Computer Vision: Vision for Robotics & Autonomous Driving
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Security and Privacy: Large-scale security measurements

Abstract

Extensive research has already been conducted in the autonomous driving field to help vehicles navigate safely and efficiently. At the same time, plenty of current research on vulnerable road user (VRU) safety is performed which largely concentrates on perception, localization, or trajectory prediction of VRUs. However, existing research still exhibits several gaps, including the lack of a unified planning and collision avoidance system for autonomous vehicles, limited investigation into delay tolerant control strategies, and the absence of an efficient and standardized testing methodology. Ensuring VRU safety remains one of the most pressing challenges in autonomous driving, particularly in dynamic and unpredictable environments. In this two year project, we focused on applying the Vehicle in Virtual Environment (VVE) method to develop, evaluate, and demonstrate safety functions for Vulnerable Road Users (VRUs) using automated steering and braking of ADS. In this current second year project report, our primary focus was on enhancing the previous year results while also considering bicyclist safety.

Problem

• Develops autonomous vehicle collision avoidance system for VRUs
• Addresses lack of standardized testing methodology for VRU safety
• Focuses on bicyclist safety in autonomous driving scenarios

Innovation

• Vehicle-in-Virtual-Environment testing methodology
• Automated steering and braking safety functions
• Bicyclist safety evaluation through simulation