A method, system, device, and medium for co-perception risk test timing scenario generation
By analyzing natural driving datasets and generating time-series risk scenarios using parameterized models, this approach addresses the lack of temporal dynamics and collaborative perception features in existing autonomous driving tests. It enables the efficient generation of logically sound and dynamically realistic test scenarios, supporting the performance verification of collaborative perception algorithms.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TSINGHUA UNIVERSITY
- Filing Date
- 2026-05-15
- Publication Date
- 2026-07-24
AI Technical Summary
Existing autonomous driving testing technologies lack temporal dynamism, physical rationality, and collaborative perception features when generating and evaluating test scenarios. In particular, the spatiotemporal information fusion problem under occlusion conditions leads to high testing costs and low coverage, making it impossible to effectively verify the performance of algorithms in temporally continuous scenarios.
By analyzing the natural driving dataset, a parameterized model is established to generate single-frame risk representation scenarios. Combined with iterative updates, temporal risk representation scenarios are generated. Temporal validity detection and occlusion constraint mechanisms are introduced to ensure the dynamic evolution and physical rationality of the scenarios.
It enables efficient generation of dynamic risk scenarios, supports performance verification of collaborative perception algorithms under complex dynamic conditions, improves testing efficiency and the logical rationality and dynamic realism of scenario generation, and is compatible with multiple simulation platforms.
Smart Images

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