汽车功能安全:面向敏感指令攻击场景的自主驾驶车辆路径规划风险缓解控制

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中文摘要：为解决敏感动作指令攻击诱导的路径规划失效与跟踪控制失稳问题,提出一种基于状态解耦与实时模型预测控制相结合的自主驾驶车辆路径规划风险缓释控制方法.首先,从汽车的横向、纵向及航向等三个自由度进行风险解耦判断,并引入了网络风险因子和阻断风险因子对汽车可能遭受的敏感指令攻击风险进行量化评估;然后,将风险因子引入模型预测控制中的局部路径规划模块,对其惩罚函数进行实时修正,规划出考虑动态网络攻击风险的局部最优参考路径:此外,还在轨迹跟踪层加入基于阈值驱动的主备冗余总线切换机制,缓解敏感动作指令攻击对汽车操控能力的恶性破坏;最后,引入三种常见的敏感动作集攻击场景进行有效性验证.测试结果表明,在加速动作指令攻击场景和制动动作指令攻击场景下,与没有缓解控制的方案相比,所提方案能够缓解约31％的速度急剧变化,有效地规避汽车的纵向驾驶风险和横向失稳问题;此外,面向转向动作指令攻击时,所提方案可以制止错误的转向过程,阻止汽车发生碰撞事故.

外文标题：Automotive Functional Safety:Risk Mitigation Control of Path Planning for Autonomous Vehicles towards Sensitive Command Attack Scenarios

外文摘要：A risk mitigation control strategy for autonomous driving vehicles is proposed based on the state decoupling and real-time model predictive control(MPC)to address the problem of path planning failure and tracking instability resulting from sensitive command attacks.Firstly,the potential risks in the lateral,longitudinal,and heading states are decoupled to design network risk index and network block risk index.These indices quantitatively assess the risk of sensitive command attacks of an autonomous vehicle.Secondly,the network risk index is introduced into the local path planner based on MPC,which real-time modifies the penalty functions and plan the locally optimal reference path with consideration of the dynamic network attack risks.Additionally,a threshold-driven redundant bus switching mechanism is added to the trajectory tracking layer to mitigate the harmful effects of sensitive command attacks on vehicle control ability.Finally,three common scenarios of sensitive command attack sets are used to validate the effectiveness of the proposed strategy.The results show that the proposed strategy can mitigate approximately 31％of sudden speed changes and prevent longitudinal driving risk and lateral instability in the scenarios of acceleration and braking command attacks compared with the non-mitigated scheme.Furthermore,for steering command attacks,the proposed strategy can halt the incorrect steering process and effectively prevent collision accidents.

外文关键词：

intelligent connected vehiclesautonomous drivingpath planningnetwork attackactive safety

作者：

林晨、魏洪乾、荆威、张幽彤

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作者单位：

北京理工大学机械与车辆学院北京 100081

清洁车辆北京市重点实验室北京 100081

北京理工大学长三角研究院嘉兴 314000

汽车测控与安全四川省重点实验室成都 610039

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关键词：

智能网联汽车自主驾驶路径规划网络攻击主动安全

基金：

国家重点研发计划国家自然科学基金中国博士后自然科学基金中国博士后自然科学基金汽车新技术安徽省工程技术研究中心开放基金汽车测控与安全四川省重点实验室开放基金

项目编号：

2021YFB3101500522024612022TQ00322022M710380QCKJ202202AQCCK2023-001

出版年：

2024

DOI：

10.3901/JME.2024.10.302

机械工程学报

中国机械工程学会

机械工程学报

CSTPCD北大核心

影响因子：1.362

ISSN：0577-6686

年,卷(期)：2024.60(10)