@inproceedings{8c27c46faf4e4575bb93a215b2faa370,
title = "Attributes Based Bayesian Unknown Hazards Assessment for Digital Twin Empowered Autonomous Driving",
abstract = "Safety of the intended functionality is an indispensable safe condition that must be considered for autonomous driving on the road. The key to study SOTIF is to construct as many hazardous scenarios as possible and accurately assess SOTIF. It is difficult to test many automated driving vehicles in the current research and discover all the unknown triggering events in the intended functionality. As a new simulation method, vehicle digital twins can simulate as many driving scenarios as possible efficiently and at a low cost in a short time. Therefore, this paper proposes an algorithm to assess SOTIF with unknown hazards based on the vehicle digital twins based on a Bayesian hazard graph with attributes and Noisy-OR structures. This paper uses CARLA to build the vehicle digital twins and testing this algorithm based on these digital twins. After testing, the SOTIF quantitative assessment algorithm proposed in this paper is effective and has good performance.",
keywords = "Bayesian network, SOTIF, autonomous driving, hazard assessment",
author = "Zhonglin Hou and Hong Liu and Yan Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021 ; Conference date: 20-12-2021 Through 22-12-2021",
year = "2022",
doi = "10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00137",
language = "英语",
series = "2021 IEEE 23rd International Conference on High Performance Computing and Communications, 7th International Conference on Data Science and Systems, 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "853--860",
booktitle = "2021 IEEE 23rd International Conference on High Performance Computing and Communications, 7th International Conference on Data Science and Systems, 19th International Conference on Smart City and 7th International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021",
address = "美国",
}