Digital Driving Twins for Scaled ADAS Algorithm Development: A Systematic Review and Design Proposal for Co-Simulation Architectures, Indoor Localization Methods, and Ground Truth Strategies

Testing advanced driver assistance systems (ADAS) under rare or safety-critical conditions is impractical at full scale: track campaigns are expensive, time-intensive, and cannot easily reproduce low-probability events. Scaled cyber–physical testbeds offer a more accessible path by coupling miniature vehicle platforms with virtual simulation environments, but the field has no unified review that covers co-simulation architectures, indoor localization, and ground truth strategies in a single treatment. This paper addresses that gap with a PRISMA 2020-compliant systematic review of 92 primary sources selected from 984 records identified across IEEE Xplore and Scopus. Three topic areas are examined: real-time co-simulation architectures built on AirSim, CARLA, Gazebo, and LGSVL, compared for ROS 2 integration, synchronisation model, and edge hardware suitability; three indoor localization methods, namely AprilTag fiducial tracking, Visual Simultaneous Localization and Mapping (VSLAM), and Ultra-Wideband (UWB) radio positioning, evaluated against shared accuracy, latency, infrastructure, and robustness criteria; and existing ground truth strategies for indoor localization benchmarking. A consistent finding across the corpus is that no controlled cross-method localization comparison exists for scaled testbeds. To address this, we introduce the Programmable Ground Truth Reference System (PGTRS), which renders spatial references on a programmable LED floor panel at a pixel pitch of approximately 3.9 mm, targeting sub-centimetre ground truth accuracy without dedicated motion-capture infrastructure. The concept is demonstrated within a 1:14 scale Digital Driving Twin (DDT) testbed built at the University of Applied Sciences Wildau at a hardware cost of approximately 6576. Design guidelines and open research challenges are discussed.