Review of Evaluation Metrics for the Credibility of Autonomous Driving Test Scenarios

doi:10.16182/j.issn1004731x.joss.25-1067

Abstract

Abstract:

Currently, scenario-based simulation testing has become a core technical approach for verifying autonomous driving system functionality. However, due to the absence of unified evaluation standards for simulation scenarios, significant differences exist among the metrics constructed and adopted by different studies, each with its own limitations. This hinders mutual recognition of research outcomes and prevents scientific measurement of scenario quality. This study systematically reviews relevant research from the past five years, categorizing autonomous driving test scenarios into four types: parameter-descriptive, trajectory-driven, environment-simulation, and perception-data-driven. It clarifies the core characteristics, application objectives, and generation mechanisms of each scenario type. Subsequently, focusing on credibility as the core criterion, it systematically organizes existing evaluation metrics developed in various studies across five dimensions: challenge, realism, coverage, human-likeness, and acceleration. Third, through systematic integration of existing research, a unified credibility evaluation framework for multi-type scenarios is established. Finally, current challenges in this field are summarized, and future research directions are explored. This study provides a theoretical foundation for credibility verification of autonomous driving simulation scenarios and offers guidance for advancing simulation testing standardization and credibility evaluation system development.

Key words: autonomous driving, simulation testing, credibility, scenario construction, scenario evaluation

CLC Number:

TP391.9

Li Jiwei, Wang Runmin, Zhu Yu, Cheng Jingjun, Zhao Xiangmo. Review of Evaluation Metrics for the Credibility of Autonomous Driving Test Scenarios[J]. Journal of System Simulation, 2026, 38(9): 2464-2485.

Figures/Tables 13

Table 1

Value assignment of Fpc

TTC/s	$F p c$
5~10	1
3~5	2
1~3	3
<1	4

Table 1

Table 2

Value assignment of Fbe

可能性	$F b e$
极低	1
低	2
中	3
高	4

Table 2

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Table 3

Table 4

Table 5

Table 6

Table 7

Typical metrics for credibility evaluation of test scenarios

评价维度	指标类型	范例指标	典型计算公式
挑战性	性能一致性	AP	$∫ 01 P (R) d R$
	模块敏感性	成功率变化	$S (θ + Δ θ) - S (θ)$
	统计特征	碰撞率	$N c o l l i s i o n N t e s t$
	风险特征	风险指数	$Δ v, c o l l i s i o n - T T C, n o c o l l i s i o n$
真实性	分类一致性	一致性分数	$E x ∼ D g e n [D (x)] - 0.5$
	质量保真性	SSIM	$(2 μ x μ y + C 1) (2 σ x y + C 2) (μ x 2 + μ y 2 + C 1) (σ x 2 + σ y 2 + C 2)$
	统计特征	EMD	$m i n T ∑ i = 1 N ∑ j = 1 M T i j ⋅ p i - q j 2$
	数值误差	RMSE	$1 N ∑ i = 1 N (x i - x ̂ i) 2$
覆盖度	实例覆盖度	场景覆盖率	$C = N k e y N c o v e r e d$
	聚类覆盖度	要素覆盖率	${C k ∃ x i ∈ C k} K$
	数值误差	ADE	$1 N ∑ i = 1 N 1 T ∑ t = 1 T p i t - p i e x p e c t e d$
	统计特征	参数空间覆盖率	$V g e n V t o t a l$
拟人性	数值误差	RMSE	$1 N ∑ i = 1 N (x i - x ̂ i) 2$
拟人性	行为一致性	驾驶风格相似度	$s i m (B g e n, B h u m a n)$
加速性	采样加速率	关键产出率	$N k e y N s a m p l e$
	生成加速率	生成速率	$N g e n T g e n$
	测试加速率	缺陷触发效率	$N f a i l N s c e n e$

Table 7

Fig. 6

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评价维度	指标类型	指标描述	典型文献
挑战性	统计特征	如仿真中危险行为的发生比例	[6,17-20,36-44]
挑战性	风险特征	由TTC等子指标组合而成的综合风险度量	[19,22,43-44]
真实性	数值误差	生成场景与真实场景的接近程度	[45-46]
覆盖度	统计特征	生成场景在总体参数空间中的分布比例	[18,21,43,45-53]
覆盖度	误差特征	ADE、AOE等，通过误差体现生成场景的多样性	[20,45]
加速性	采样加速率	单位时间内可提取的有效场景数量	[43,46]
	生成加速率	往往综合考虑场景复杂度与生成速度	[19,44]
	测试加速率	在有限资源下的有效测试次数或运行场景数量	[6,22]