Corner Scenario Generation Method Based on Feature-Optimized Combinations for Automated Driving Systems China FAW Group Corporation R and D Center: First Automobile

Format:

Book

Conference/Event

Author/Creator:

Zhou, Shiying, author.

Contributor:

Zhang, Dongbo

Zhang, Peixing

Zhao, Deyin

Zhu, Bing

Conference Name:

SAE 2025 Intelligent and Connected Vehicles Symposium (2025-09-19 : Shanghai, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

With the advancement of automated driving system levels, corner scenarios characterized by low probability and high risk have become critical for the safety validation of automated vehicles. However, due to the typical long-tail distribution of such scenarios, data-driven mining approaches face significant challenges in achieving efficient generation. To address this issue, this study proposes a feature-optimized combination-based method for generating corner scenarios in automated driving systems. Key scenario features related to functional failures are first identified using a combined approach of system theoretic process analysis (STPA) and hazard and operability analysis (HAZOP). Based on these features, an adaptive genetic algorithm is employed to optimize feature combinations and generate large numbers of corner scenario types that meet specified constraints. The proposed method is validated using cut-in and pedestrian-crossing scenarios as baseline cases. The results show that this method enables large-scale generation of corner scenario types grounded in regulatory scenarios and provides significant support for the development of comprehensive corner scenario libraries for automated vehicle testing

Notes:

Vendor supplied data

Publisher Number:

2025-01-7317

Access Restriction:

Restricted for use by site license