Our tool offers advanced services for evaluating and enhancing localization and mapping accuracy in automated driving systems. It provides rigorous analysis, benchmarking, and reporting to help you validate algorithms, assess map quality, and make data-driven decisions to ensure optimal performance and reliability of your systems.
Our tool is designed to benefit a diverse range of professionals and organizations involved in automated driving and localization technologies.
○ Companies developing or integrating autonomous driving systems that require robust localization and mapping pipelines. They can use the tool to verify that their perception and localization stacks are accurate and reliable, enhancing vehicle performance and safety.
○ Academic researchers and institutions working on advanced driver assistance systems (ADAS), SLAM, or sensor fusion methods can leverage our tool to validate their systems, conduct experiments, and gather data for innovative research.
○ Firms that provide HD maps, localization software, or other mobility infrastructure can validate and certify the accuracy of their offerings, ensuring they meet the highest accuracy standards.
○ Companies managing fleets of autonomous or semi-autonomous vehicles can continuously monitor and validate the performance of their localization systems across different vehicle types and environments.
○ Agencies involved in setting standards and regulations for automated driving and intelligent transport systems can use the tool to define benchmarks and verify compliance with localization and mapping accuracy requirements.
○ Professionals who provide consulting and integration services in the mobility sector can deliver measurable improvements and evidence-based assessment for clients, ensuring optimal performance and reliability.
Request a demo or sign up with a demo voucher. After access is granted, upload a representative dataset or use our sample set, select the desired evaluation service, and start a run. You will receive a detailed report with KPIs, visualizations, and recommendations. Documentation and quick-start guides are included to help you configure inputs, select scenarios, and interpret results.
The demo voucher unlocks limited evaluations so you can explore the workflow end-to-end. It includes a predefined quota of jobs, access to sample datasets, and the full reporting experience with selected KPIs, charts, and heat maps. It is designed to showcase the core capabilities without needing a full contract.
Contact our team to request a voucher. Provide your organization, intended use case, and preferred services. We will verify eligibility and issue a voucher if appropriate. You can then redeem it during registration or in your profile to activate demo access.
Yes. We offer onboarding sessions, documentation, and optional workshops. Training covers dataset preparation, service selection, KPI interpretation, and how to integrate our outputs into your development or validation workflows.
The Localization Reference Service (LocRef) provides high-precision reference trajectories and ground truth. It fuses multi-sensor data and applies stringent post-processing to deliver reference poses with quantified uncertainty. You use these references to benchmark your localization algorithms or to validate maps against a trusted baseline.
You receive interactive and downloadable reports with summary KPIs, per-segment metrics, distribution plots, time-series charts, and geospatial overlays such as heat maps. Reports highlight failure modes, outliers, and conditions that degrade performance. Executive summaries and technical appendices are included for different audiences.
Our Localization Validation Service (LocVal) provides a robust and interpretable KPI framework based on precise reference trajectories and strict statistical methods. It isolates error components, quantifies uncertainty, and correlates performance with context, enabling fair comparisons across algorithms, maps, and scenarios. This reduces ambiguity and accelerates iteration by directly assessing and quantifying localization performance.
Validation follows the principle that static environmental features are consistent over time. We compare estimated poses or map features against high-quality references and compute KPIs such as absolute position error, heading error, drift, and integrity metrics. The methodology controls for noise, aligns coordinate frames, and aggregates results by context to ensure conclusions are statistically meaningful.
We collect raw sensor data and align it against the HD map to evaluate positional accuracy, landmark consistency, lane geometry fidelity, and coverage. Detected deviations are quantified and visualized in reports, including recommended updates. This process helps map providers and users maintain reliable maps that support high-performance localization.
The service detects issues such as misplaced landmarks, missing features, duplicated or outdated objects, incorrect topology, and systematic offsets. It also highlights environmental changes that require map updates. Each finding includes location, severity, and recommendations for corrective action.
The Map Evaluation Service (MapEvl) assesses how a given HD map impacts localization performance across routes and conditions. It quantifies where the map supports strong localization and where it degrades, guiding targeted improvements such as better landmark selection, denser features, or optimal landmark placement to improve localization accuracy.
Heat maps are generated from localization error statistics computed along the route and aggregated by spatial tiles. They reveal where performance is strong or weak under specific conditions. Teams use them to prioritize data collection, refine algorithms, and make decisions on where to place or update landmarks for enhanced accuracy.
