Assessment Scheme for Scenario Allocation in Automated Driving Based on a Hybrid Genetic–Fuzzy Framework
Source abstract
Abstract
To address the structural differences between closed-track and open-road testing in terms of scenario coverage, risk controllability, and validation consistency, this study proposes a scenario-driven combined testing method for automated driving systems. The proposed approach constructs a multi-dimensional scenario space based on functional decomposition and jointly quantifies scenario complexity and hazard level from the perspectives of information heterogeneity and interaction-induced risks. Based on these two-dimensional scenario attributes, a fuzzy inference mechanism is developed to dynamically allocate validation resources across different testing environments. To further improve rule-base generalization and mapping stability, an enhanced genetic algorithm integrating simulated annealing and K-means clustering is introduced to optimize the rule structures in an evolutionary manner. Experimental results demonstrate that, compared with traditional testing methods and single-mechanism optimization strategies, the proposed approach achieves a more consistent and interpretable mapping between scenarios and testing proportions in high-complexity urban traffic scenarios. While ensuring test adequacy, the testing economy is significantly improved, with an overall average improvement exceeding 20%. In addition, stable resource allocation performance is observed across multiple scenarios with different levels of complexity and risk, confirming the scalability and applicability of the proposed method for multi-scenario automated driving system testing.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Autonomous Vehicle Technology and Safety · Traffic control and management · Traffic and Road Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
China Automotive Technology and Research Center Co., Ltd., Tianjin 300300, China
School of Automotive Studies, Tongji University, Shanghai 201804, China
Funding
National Key R&D Program of China
References (31)
Show source-provided references
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