A Progressive Fusion Architecture for Validating Distance Estimation in Aftermarket ADAS Installations

The proliferation of retrofitted Advanced Driver Assistance Systems (ADAS) in the automotive aftermarket has introduced significant challenges regarding performance validation, particularly for distance estimation capabilities that underpin critical safety functions. Small-scale installation workshops typically lack standardized testing infrastructure, creating uncertainty about system reliability and hindering consumer confidence. This work presents a progressive fusion architecture that addresses these validation challenges through intelligent integration of existing sensor outputs. Our methodology employs a hierarchical multi-stage network design to synthesize observations from multiple ADAS sources, generating dependable distance estimates that serve as validation benchmarks for newly installed systems. The approach circumvents the need for expensive ground truth equipment by learning from curated datasets to produce reliable performance indicators. Validation experiments conducted on 24 carefully collected driving scenario recordings demonstrate the framework's capability to achieve distance prediction with minimal deviation from actual measurements. Comparative analysis reveals that our fusion-based validation approach delivers superior assessment accuracy compared to conventional single-device reference methods, establishing its practical value for aftermarket ADAS quality assurance.