Work

Augmented Reality-Enhanced Digital Twin for Automated Vehicle Testing

Year: 2025

Type: article

Abstract: The developments in automated driving system (ADS) technologies over the last decade have significantly increased the need for robust and high-quality ADS-equipped vehicle (AV) testing. While testing in naturalistic driving environments (NDEs) offers realism, it is often costly, and inefficient in capturing rare corner or edge cases critical for safety evaluation. A more practical solution is to design an Augmented Reality (AR)-enhanced, Digital Twin (DT) system. This paper proposes an AR-enhanced DT framework integrating two open-source automated driving software platforms, Autoware and CARLA. By generating digital traffic, CARLA provides a virtual driving environment (VDE), within which Autoware, serving as the ADS software, navigates. In this architecture, the pivotal component is a ROS2-based merger which injects simulated traffic into Autoware. This integrated framework was deployed on the BELIV research AV platform, the BELIV vehicle, at the Polytechnic campus of Arizona State University. The experimental results demonstrate the feasibility of this approach, highlighting its potential to enhance AV testing.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.07.030

Authors: Rohan Khaire, Hengcong Guo, Harsh Gangwar, Nithish Kumar Saravanan, Jeffrey Wishart, Junfeng Zhao

Institutions: Arizona State University, Science Foundation Arizona

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: diamond

Funder: Science Foundation Arizona