真实与虚拟场景下自动驾驶车辆的主动安全性验证与确认综述

doi:10.13229/j.cnki.jdxbgxb.20230920

Abstract

Abstract:

This article initially provides an overview of the processes and standard regulations involved in the safety verification and validation of autonomous vehicles. Building upon the human-vehicle-road system theory， the article further introduces a novel classification approach， categorizing and summarizing the current technologies and assessment standards for safety verification and validation in autonomous vehicles. It also consolidates and comparatively analyzes three major categories of methods： those based on real-world scenarios， virtual scenarios， and a combination of both. The article conducts a comparative evaluation of the limitations， advantages， and disadvantages of 16 different verification and validation methods across eight characteristic dimensions. Finally， it briefly extrapolates on the challenges and future prospects in the research of safety verification and validation schemes for autonomous vehicles.

Key words: vehicle engineering, autonomous vehicle, safety, verification and validation, formal verification

CLC Number:

U495

Zhen-hai GAO,Cheng-yuan ZHENG,Rui ZHAO. Review of active safety verification and validation for autonomous vehicles in real and virtual scenarios[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(4): 1142-1162.

Figures/Tables 11

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Table 2

Summary of verification and validation methods for autonomous driving"

方法类别	特征	方法概述
基于真实要素的验证与确认方法	基于真实驾驶员数据、真实自动驾驶车辆数据与真实场景数据的验证与确认	基于行驶里程的真实要素验证和确认方法^{［39，40］}
		基于脱离的真实要素验证和确认方法^［39］
		基于形式化的真实要素验证和确认方法	基于责任敏感安全模型的验证和确认方法^［41］
		基于形式化的真实要素验证和确认方法	基于安全力场模型的验证和确认方法^［42］
基于真实要素和虚拟要素相结合的验证与确认方法	仅场景为虚拟数据	基于虚拟试驾^［43］的真实要素和虚拟要素相结合的验证和确认方法
	仅驾驶员为虚拟数据	基于实车测试^［44］的真实要素和虚拟要素相结合的验证和确认方法
	仅场景为真实数据	基于数字孪生的真实要素和虚拟要素相结合的验证和确认方法
	仅自车为真实数据	基于整车在环^［45］的真实要素和虚拟要素相结合的验证和确认方法
	仅驾驶员为真实数据	基于驾驶模拟器测试^［46］真实要素和虚拟要素相结合的验证和确认方法
基于虚拟要素的验证与确认方法	驾驶员、自车、场景的验证要素全部基于虚拟数据	基于模型在环测试^［47］的虚拟要素验证和确认方法
		基于硬件在环测试^［48］的虚拟要素验证和确认方法
		基于形式化模型的虚拟要素验证和确认方法（目标：所有不安全场景）	责任敏感安全模型RSS^{［41，49-51］}
			安全力场模型SFF^{［42，52，53］}
			道路可达集预测RA^［54-57］
		基于机器学习的虚拟要素验证和确认方法（目标：所有不安全场景）	强化学习（RL）^［58］
		基于机器学习的虚拟要素验证和确认方法（目标：所有不安全场景）	深度学习（DL）^［59］
		基于数据挖掘的虚拟要素验证和确认方法^{［60，61］} （目标：未知不安全场景）
		基于故障注入测试的虚拟要素验证和确认方法^［62-65］（目标：已知不安全场景）

Table 2

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项目	场景盖度（覆盖广度）	数据穿透性（保真度）	时间经济性（验证效率）	定向性	具有数学模型（可解释性）	验证实时性（在线验证）	泛化性（可演化性）
1.基于行驶里程的现实场景验证和确认方法	+
2.基于脱离的现实场景验证和确认方法	+
3.基于责任敏感安全模型RSS验证和确认方法	+				+	+
4.基于安全力场模型SFF验证和确认方法	+				+	+
5.基于虚拟试驾的真实要素和虚拟要素相结合的验证和确认方法			+	+
6.基于实车测试的真实要素和虚拟要素相结合的验证和确认方法			+	+
7.基于数字孪生的真实要素和虚拟要素相结合的验证和确认方法			+	+
8.基于整车在环的真实要素和虚拟要素相结合的验证和确认方法			+	+	+
9.基于驾驶模拟器测试真实要素和虚拟要素相结合的验证和确认方法			+	+	+
10.基于在环测试的虚拟要素验证和确认方法			+	+	+
11.基于模型在环测试的虚拟要素验证和确认方法		+	+	+	+
12.基于形式化模型的虚拟要素验证和确认方法		+	+	+	+
13.基于机器学习理论的虚拟要素验证和确认方法	+	+	+		+		+
14.基于数据挖掘方法的虚拟要素验证和确认方法	+	+	+		+		+
15.基于故障注入测试的虚拟要素验证和确认方法		+			+		+
16.基于故障注入测试的虚拟要素验证和确认方法		+	+	+	+

Review of active safety verification and validation for autonomous vehicles in real and virtual scenarios

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