面向预期功能安全的eVTOL智能避障系统运行时保证方法

doi:10.16265/j.cnki.issn1003-3033.2024.07.0198

中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (7): 105-112.doi: 10.16265/j.cnki.issn1003-3033.2024.07.0198

面向预期功能安全的eVTOL智能避障系统运行时保证方法

董磊¹^,²(), 宋文佳¹^,³, 陈曦¹^,²^,^**(), 刘嘉琛¹^,⁴, 梁博尧¹^,⁴

¹ 中国民航大学民航航空器适航审定技术重点实验室,天津 300300
² 中国民航大学科技创新研究院,天津 300300
³ 中国民航大学中欧航空工程师学院,天津 300300
⁴ 中国民航大学安全科学与工程学院,天津 300300

收稿日期:2024-01-13 修回日期:2024-04-21 出版日期:2024-07-28
通信作者:
^** 陈曦(1987—),男,河北邯郸人,博士,助理研究员,主要从事模式识别与图像处理方面的研究。E-mail:csxichen@outlook.com。
作者简介:
董磊 (1983—),男,天津人,博士,副研究员,主要从事民机安全性评估与适航审定技术、未来民机智能飞行与智能系统技术等方面的研究。E-mail: dlcauc@126.com。
基金资助:
民用航空器适航审定技术重点实验室开放基金资助(SH2023101701); 中央高校基本科研业务费(3122022044); 中央高效基本科研业务费(3122022QD07)

Runtime assurance method for eVTOL intelligent obstacle avoidance system toward safety of intended functionality

DONG Lei¹^,²(), SONG Wenjia¹^,³, CHEN Xi¹^,²^,^**(), LIU Jiachen¹^,⁴, LIANG Boyao¹^,⁴

¹ Key Laboratory of Civil Aircraft Airworthiness Technology, Civil Aviation University of China, Tianjin 300300, China
² Science and Technology Innovation Research Institute, Civil Aviation University of China, Tianjin 300300, China
³ Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China
⁴ College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China

Received:2024-01-13 Revised:2024-04-21 Published:2024-07-28

摘要/Abstract

摘要：

为保障电动垂直起降飞行器(eVTOL)在城市空中交通(UAM)场景下的预期功能安全(SOTIF),降低人工智能算法的验证难度,建立基于运行时保证(RTA)方法的避障模型。首先,使用人工势场法改进的柔性动作评价(SAC)算法作为eVTOL智能避障系统的复杂功能;然后,使用动态反应规划(DRP)作为智能航电系统的备用功能,以减缓SOTIF危险,同时,使用监视和决策模块采集环境状态,搭建RTA架构;最后,分别仿真仅使用复杂功能和具有RTA架构的避障系统,对比避障效果。结果表明:2种方法均可实现避障,但仅使用复杂功能的传统避障系统会带来SOTIF危险;经过RTA架构设计,可使eVTOL的安全飞行时长占比由78.4%提高到98.15%,总航路长度仅增加0.95%,在保障效率的同时,降低了在运行场景中的风险。

关键词: 预期功能安全(SOTIF), 电动垂直起降飞行器(eVTOL), 运行时保证(RTA), 城市空中交通(UAM), 智能避障系统

Abstract:

To ensure the SOTIF of eVTOL vehicles in UAM and reduce the verification difficulty of artificial intelligence algorithms, an obstacle avoidance model was proposed based on RTA method. Firstly, SAC (soft actor-critic) algorithm improved by the artificial potential field method was used as the complex function of the eVTOL intelligent obstacle avoidance system. Then, dynamic response planning (DRP) was used as a backup function of the intelligent avionics system to mitigate SOTIF hazards. Moreover, monitoring and decision-making modules were adopted to obtain environmental conditions and develop an RTA architecture. Finally, the simulated obstacle avoidance performance was compared between the two systems using complex function and RTA. The results showed that both methods can achieve obstacle avoidance, but the traditional obstacle avoidance system using complex functions may impose SOTIF risk. The RAT architecture design increased safe flight time from 78.4% to 98.15%, with the total route length only increasing by 0.95%, reducing risks in operational scenarios while ensuring efficiency.

Key words: safety of the intended functionality (SOTIF), electric vertical take-off and landing (eVTOL), runtime assurance (RTA), urban air mobility (UAM), intelligent obstacle avoidance system

中图分类号:

X949

董磊, 宋文佳, 陈曦, 刘嘉琛, 梁博尧. 面向预期功能安全的eVTOL智能避障系统运行时保证方法[J]. 中国安全科学学报, 2024, 34(7): 105-112.

DONG Lei, SONG Wenjia, CHEN Xi, LIU Jiachen, LIANG Boyao. Runtime assurance method for eVTOL intelligent obstacle avoidance system toward safety of intended functionality[J]. China Safety Science Journal, 2024, 34(7): 105-112.

图/表 15

图1

图2

图3

表1

APF改进SAC算法伪代码

伪代码1:APF改进SAC算法

初始化参数

θ, ψ 1, ψ 2, ψ - 1, ψ - 2

初始化经验池
for轮次=1到M
接收初始状态

s 1

for t = 1到T
依据策略

π (θ)

和状态

s t

生成动作

a t

APF优化

a t

→

a' t

执行动作

a' t

,观察奖励

r t

和新状态

s t + 1

将

(s t, a t, r t, s t + 1)

添加到经验池D
for j = 1 to更新次数do
经验池D随机采样一批数据
计算评论家目标网络的Q值
更新评论家网络参数

ψ 1

和

ψ 2

更新演员网络参数

θ

软更新评论家目标网络参数

ψ - 1

和

ψ - 2

end for
令

s t ← s t + 1

end for
end for

表1

图4

图5

图6

表2

基于DRP的SOTIF场景划分

SOTIF场景	DRP类型	运行状态
已知安全	$ϕ ∈ ϕ s$	安全
已知不安全	$ϕ ∈ ϕ u *$	非常危险
未知安全	$ϕ ∈ ϕ u 且 ϕ ∉ ϕ u *$	危险
未知不安全	$ϕ ∈ ϕ u 且 ϕ ∉ ϕ u *$	危险

表2

表3

深度强化学习试验参数

参数	数值
熵正则化系数 $α$	0.2
训练批次大小	128
经验池大小	10⁶
探索函数	随机动作

表3

表4

图7

图8

图9

图10

表5

参考文献 17

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编号	起点	终点	状态
1	(330,0,225)	(0,225,225)	已知安全
2	(330,0,105)	(0,225,180)	未知安全/ 未知不安全
3	(330,75,225)	(0,75,75)	已知安全
4	(330,75,105)	(0,75,180)	已知安全
5	(330,105,75)	(0,0,225)	已知安全
6	(330,225,225)	(0,0,75)	已知安全
7	(330,225,75)	(0,0,225)	已知安全
8	(330,225,180)	(0,0,180)	已知安全

方法	航路长度/m	安全时长占比/%
仅使用强化学习	324.98	98.15
RTA避障	328.08	78.4

面向预期功能安全的eVTOL智能避障系统运行时保证方法

Runtime assurance method for eVTOL intelligent obstacle avoidance system toward safety of intended functionality

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