The transportation sector is a major contributor to global energy consumption and air pollution. In response, the automotive industry is actively engaged in addressing this dual challenge by pursuing advancements that enhance energy sustainability and minimize vehicle emissions. In this context, modern diesel engines are continually improving, driven by growing concerns about environmental impact and human health. In particular, Diesel Particulate Filters (DPFs) play a crucial role in reducing harmful particulate matter (PM) emissions from diesel engines. Maintaining optimal DPF performance is essential for both environmental and regulatory compliance. Modern legislations for diesel vehicles require the use of a soot sensor downstream the DPF to satisfy the new challenging OBD limits. In this work, the performance of a recent diagnostic technique based on 2D projection of the resistive soot sensor is assessed using Artemis driving cycle. A validated simulation platform is employed to evaluate DPF monitoring performance under realistic conditions that incorporate engine-out soot emission variations and sensor response dispersions. The obtained results are promising and show the relevance and robustness of the proposed approach for fast and effective DPF diagnostic. A statistical analysis is performed in order to derive a relevant detection threshold that satisfies a trade-off between false alarm and non-detection rates. The resulting diagnostic strategy has been validated for on-line DPF malfunction detection and can be easily implemented for real-time applications.