Simcenter Autonomy Data Analysis solution

Modern vehicle testing campaigns involve extensive real-world driving data collected from diverse operational environments. Managing such large volumes manually is inefficient and error-prone. Simcenter Autonomy Data Analysis streamlines this process by automatically ingesting, organizing, and categorizing raw data with AI based perception and processing algorithms. It identifies relevant driving scenarios, system failure events and filters out irrelevant parts of the drive data, significantly reducing manual effort and storage costs. All uploaded data is securely stored and anonymized to comply with privacy regulations. Engineers can efficiently navigate through structured datasets using interactive dashboards, live map overlays, and 3D environment visualizations to gain deeper insights into recorded drives.

A key aspect of autonomous system validation is ensuring that vehicles perform reliably under diverse real-world conditions. Simcenter Autonomy Data Analysis automates scenario extraction, transforming large datasets into structured, categorized driving events. In addition to the pre-built scenarios from standards and protocols such as ENCAP, the user can define their own scenario of interest. By analyzing scenario characteristics in terms of parameter distribution and ranges, engineers can design targeted test plans that replicate real-world challenges. Furthermore, the extracted scenarios or any specific time period of drive are exportable in industry-standard formats such as OpenSCENARIO and OpenDRIVE, ensuring seamless interoperability with simulation tools for Software-in-the-Loop (SiL), Hardware-in-the-Loop (HiL), and Model-in-the-Loop (MiL) testing. Additionally, raw sensor signals can be extracted for high-fidelity replay, supporting hardware-based validation.

Ensuring compliance with safety regulations and industry standards is critical in ADS and ADAS development. Simcenter Autonomy Data Analysis simplifies the on-road evaluation of system versions by enabling automated pass/fail evaluations for system performance in predefined scenarios. Using configurable safety criteria, engineers can assess system behavior in critical driving conditions. If performance deviations are detected, the system automatically flags these cases, allowing rapid issue identification and resolution. Engineers can share scenario links directly with relevant teams, ensuring transparency and traceability in debugging and verification processes.

The iterative nature of autonomous vehicle development requires continuous performance monitoring and refinement. Simcenter Autonomy Data Analysis offers advanced tag-based querying capabilities, allowing validation teams to search across structured datasets for rare and high-risk events, such as abrupt lane changes, hard braking, adverse weather and lighting conditions. While the solution offers more than 70 prebuilt tags for scenarios, ODD attributes and events, you can add your own tag of interest. By monitoring scenario failure rates, system developers can identify weak areas requiring improvement. Performance analytics helps prioritize design changes, ensuring that new software versions address identified gaps. This proactive approach reduces late-stage failures, optimizes test coverage, and improves overall system robustness.

Every development team has unique workflows, tools, and requirements. Simcenter Autonomy Data Analysis is built with flexibility in mind, offering extensive customization capabilities. Teams can define custom scenario categories, create specialized tags, and integrate proprietary algorithms for additional data processing, such as object detection and sensor annotation. With a powerful OpenAPI framework and command-line interface (CLI), the platform supports programmatic access to all functionalities, enabling automation and seamless integration with existing development pipelines. Native compatibility with Siemens Teamcenter Share further enhances collaborative workflows, providing secure storage and easy access to scenario data across teams.