Closing the Real-to-Sim Gap – Turning Highway Drives into Simulation-Ready Datasets

Modern ADAS/AD programmes invest heavily in collecting and labelling real-world driving data, yet many projects stall when the dataset must be transferred into simulation for scenario variation, closed-loop validation and regression testing. The root cause is often not a lack of data, but a lack of dataset completeness: missing calibration and time-synchronisation evidence, inconsistent metadata, unclear labelling specifications and non-traceable quality KPIs. As a result, simulation teams cannot reliably convert real logs into a synthetic environment, even when the original data-collection and labelling budgets were substantial. In this presentation, b-plus and IPG showcase an end-to-end real-to-sim-ready workflow using a Highway Drive Pilot as a reference case. The process begins with an ODD and labelling specification tailored to highway driving and perception tasks. Data is collected with a synchronised sensor stack that ensures time-exact recording, calibration integrity and structured metadata. The dataset is then processed through high-volume automated labelling—including 2D and 3D bounding boxes, traffic signs and lane lines—reaching approximately 90% initial quality at very high throughput, with up to approximately 4,000 km processed overnight. b-plus then applies an independent quality-assurance and enrichment process established with TÜV SÜD since 2020, adds premium labelling tasks where required, and raises quality to approximately 99% with transparent KPIs and reports. Finally, the curated dataset package—raw streams, labels, KPIs, reports and metadata—is transformed into a simulation scenario for CarMaker. The presentation demonstrates how a governed data pipeline removes friction at the real-to-sim interface and enables faster, more economical and higher-confidence validation loops for ADAS/AD development. What the audience will learn • Why many data-rich ADAS projects still fail at real-to-sim conversion, including metadata, calibration and KPI-traceability issues. • How an ODD-driven pipeline produces CarMaker-ready datasets in days rather than weeks. • How automated bulk labelling combined with independent QA enables scalable, cost-efficient and high-confidence validation. Key advantages • Real-to-sim readiness by design, with calibration, time synchronisation and metadata captured as first-class deliverables. • ODD and labelling specifications agreed upfront, avoiding relabelling loops and definition drift. • Independent QA using a four-eyes principle and a TÜV SÜD-established process in place since 2020. • A flexible full-service approach that allows customers to rent or lease a complete sensor stack instead of purchasing hardware. • A standards-led approach using structured metadata and interoperability to enable downstream simulation workflows.