Simcenter Autonomy Data Analysis

Optimizing Autonomous Driving Data with Simcenter Autonomy Data Analysis The development of Autonomous Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS) relies on vast amounts of sensor and vehicle data, often exceeding a petabyte per week. However, extracting relevant insights from such massive datasets is a major challenge, which overloads and distracts engineers from focusing on development and validation. Simcenter Autonomy Data Analysis streamlines this process by automating scenario-based data processing, providing engineers with quick access to actionable insights. It enables rapid scenario extraction, system validation, and enhanced traceability, leading to a more efficient, data-driven development process. Efficient Data Management & Processing Modern vehicle testing generates extensive real-world data across diverse environments. Managing this manually is inefficient and error-prone. Simcenter Autonomy Data Analysis automates data ingestion, organization, and categorization using AI-driven perception and processing. It filters out irrelevant data, identifies key driving scenarios and system failures, reducing manual effort and storage costs. All data is securely stored and anonymized to comply with privacy regulations. Engineers can efficiently navigate structured datasets via interactive dashboards, live maps, and 3D visualizations, gaining deeper insights into recorded drives. Scenario Based Analysis & Test Case Generation Validating autonomous systems requires testing performance across diverse conditions. Simcenter Autonomy Data Analysis automates scenario extraction, transforming large datasets into structured, categorized driving events. Engineers can define custom scenarios beyond pre-built ones (e.g., ENCAP). By analyzing scenario characteristics, teams can design targeted test plans to replicate real-world challenges. Extracted scenarios and specific time periods can be exported in OpenSCENARIO and OpenDRIVE formats, ensuring seamless integration with SiL, HiL, and MiL testing environments. Additionally, raw sensor signals can be extracted for high-fidelity replay, supporting hardware validation. Optimizing System Behavior & Compliance Ensuring compliance with safety regulations and industry standards is crucial for ADS and ADAS development. Simcenter Autonomy Data Analysis automates pass/fail evaluations, detecting performance deviations in predefined scenarios, enabling rapid debugging and transparent verification. Continuous Improvement via Data Driven Insights Autonomous vehicle development requires continuous performance monitoring. Simcenter Autonomy Data Analysis provides advanced query capabilities to identify rare, high-risk events like abrupt lane changes, hard braking, and adverse conditions. With 70+ prebuilt tags and custom tagging options, validation teams can track failure rates and prioritize improvements, reducing late stage failures. Customization & Seamless Integration Every development team has unique workflows. Simcenter Autonomy Data Analysis offers full customization, allowing engineers to define scenario categories, create specialized tags, and integrate proprietary algorithms (e.g., object detection, sensor annotation). The OpenAPI framework and CLI enable automation, while Siemens Teamcenter Share enhances collaboration with secure data storage and sharing. To summarize, by automating scenario extraction and test case generation, Simcenter Autonomy Data Analysis reduces validation time and effort. This streamlined, cost effective approach allows engineering teams to focus on innovation rather than data wrangling, paving the way safe and robust autonomous mobility.