With the developing of high integrations in large scale systems, such as aircraft and other industrial systems, there are new challenges in safety analysis due to the complexity of the mission process and the more complicated coupling characteristic of multi-factors. Aiming at the evaluation of coupled factors as well as the risk of the mission, this paper proposes a combined technology based on the Decision Making Trial and Evaluation Laboratory (DEMATEL) model and the Bayesian network (BN). After identifying and classifying the risk factors from the perspectives of humans, machines, the environment, and management, the DEMATEL technique is adopted to assess their direct and/or indirect coupling relationships to determine the importance and causality of each factor; moreover, the relationship matrix in the DEMATEL model is used to generate the BN model, including its parameterization. The inverse reasoning theory is then implemented to derive the probability, and the risk of the coupled factors is evaluated by an assessment model integrating the probability and severity. Furthermore, the key risk factors are identified based on the risk radar diagram and the Pareto rule to support the preventive measurements. Finally, an application of the take-off process of aircraft is provided to demonstrate the proposed method.

ISO26262: 2018 is an international functional safety standard for electrical and/or electronic (E/E) systems within road vehicles. It provides appropriate safety requirements for road vehicles to avoid unreasonable residual risk according to automotive safety integrity levels (ASILs) derived from hazard analysis and risk assessment (HARA) required in the ISO26262 concept phase. Systems theoretic process analysis (STPA) seems to be designed specifically to deal with hazard analysis of modern complex systems, but it does not include risk evaluation required by most safety related international standards. So we integrated STPA into Failure Mode and Effect Analysis (FMEA) template to form a new method called system theoretic process analysis based on an FMEA template, STPAFT for shot, which could not only meet all the requirements of the concept phase in ISO26262, but also make full use of the advantages of the two methods. Through the focus of FMEA on low-level components, STPAFT can obtain more detailed causal factors (CFs), which is very helpful for derivation of safety goals (SGs) and the functional safety requirements (FSRs) in the concept phase of ISO26262. The application of STPAFT is described by the case study of fuel level estimation and display system (FLEDS) to show how the concept phase of ISO26262 could be supported by STPAFT.

The inclusion of autonomous vehicles into our society requires not only mature automated technologies but also adequate confidence in safety guarantees. Safety of the intended functionality (SOTIF) is an incipient important field that focuses on risk led by performance limitations rather than faults. There are still plenty of gaps in addressing it, especially when concerning perception ability. This article introduces a method to evaluate SOTIF-oriented perception effectiveness for forward obstacle detection of autonomous vehicles. Both inevitable existence uncertainty and state uncertainty in perception results are taken into account, and a formal SOTIF requirement specification is defined. Based on the safety distance model, we further deduce the minimum required perception area and the perception error requirements under a given driving situation to refine the SOTIF requirement specification. Then, a SOTIF-oriented unified sensing model is established by combining sensing space and ability characteristics. The system-level perception ability of an autonomous vehicle is subsequently deduced with the assumption that every available sensor is described in the form of our proposed sensing model. Evaluation can be executed according to the above methods that support determining the SOTIF requirements and the system-level perception ability, and the entire evaluation process is presented as an algorithm in this article. Finally, a practical case generated according to a well-known autonomous vehicle prototype is used to prove the effectiveness of the method. Evaluation results can serve as suggestions to improve the overall SOTIF level for perception.