Critical Factor Graph Situation Clusters for Accelerated Automotive Safety Validation
Critical Factor Graph Situation Clusters for Accelerated Automotive Safety Validation
复制标题
用于加速汽车安全验证的关键因素图情况集群
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Mykel J. Kochenderfer
中科院分区：
文献类型：
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作者：
T. Wheeler;Mykel J. Kochenderfer
先进的汽车安全系统的现代验证方法涉及在安全关键交通事件中模拟人类驾驶行为。关键情况往往是煞费苦心地列举和建模，很难建立信心，关键交通事件的空间是充分覆盖。这项工作提出了一种自动化方法，用于识别和聚类关键情况，捕获严重程度和发生频率，从而允许基于风险的安全性验证。我们证明了新方法的能力，以加快汽车安全系统的安全验证，使用重要性抽样和有效地优化其参数。
Modern validation approaches of advanced automotive safety systems involve simulations of human driving behavior in safety-critical traffic events. Critical situations are often painstakingly enumerated and modeled, and it is difficult to establish confidence that the space of critical traffic events is adequately covered. This work presents an automated method for identifying and clustering critical situations that capture severity and frequency of occurrence, thereby allowing for risk-based safety validation. We demonstrate the ability of the new approach to accelerate the safety validation of an automotive safety system using importance sampling and efficiently optimize its parameters.