Abstract: A method for assessing Safety Of The Intended Functionality of autonomous driving based on DAG block chain is provided. The method is based on DAG block chain technology and fused with system of the intended functionality, realizing safety assessment of the intended functionality. The intended functionality is objectively assessed based on data. The assessment method, in combination with an in-the-loop test, analyzes the current test data, without relying on a large amount of traditional experience to make judgments. Result of credit assessment of the nodes is taken as a result of safety assessment. Each of hardware or components is analyzed in terms of the safety and reliability, and the result of assessment is relatively complete. A device and a computer-readable storage medium are further provided.

Abstract: A requirement conformity parsing method and system, an electronic device and a storage medium. The method includes: obtaining a requirements document that needs to be parsed, and viewing a requirement in the requirements document; performing requirement conformity parsing on several upper-level requirements and corresponding lower-level requirements thereof in the requirements document; introducing a requirements knowledge map to semantically expand a proper noun in the requirements; checking a requirement traceability relationship; checking a requirement traceability label existing in the requirements document; and automatically supplementing the requirements document. The method carries out assistant checking on the requirement conformity between different levels, and facilitates requirement analysts to conveniently and quickly review the requirements between different levels.

Abstract: A method for detecting and defending against abnormal traffic of an in-vehicle network based on information entropy, including the following steps: step 1: setting a sliding window; step 2: setting a threshold; step 3: collecting and processing traffic; step 4: calculating information entropy in the sliding window when the window is full; and step 5: detecting traffic of a controller area network (CAN) bus and an in-vehicle Ethernet. Based on impact of abnormal traffic on the information entropy, the information entropy in the sliding window is calculated in real time and compared with the preset thresholds, to detect whether an abnormality occurs.

Abstract: A method for detecting and defending against abnormal traffic of an in-vehicle network based on information entropy, including the following steps: step 1: setting a sliding window; step 2: setting a threshold; step 3: collecting and processing traffic; step 4: calculating information entropy in the sliding window when the window is full; and step 5: detecting traffic of a controller area network (CAN) bus and an in-vehicle Ethernet. Based on impact of abnormal traffic on the information entropy, the information entropy in the sliding window is calculated in real time and compared with the preset thresholds, to detect whether an abnormality occurs.

Abstract: A method for assessing Safety Of The Intended Functionality of autonomous driving based on DAG block chain is provided. The method is based on DAG block chain technology and fused with system of the intended functionality, realizing safety assessment of the intended functionality. The intended functionality is objectively assessed based on data. The assessment method, in combination with an in-the-loop test, analyzes the current test data, without relying on a large amount of traditional experience to make judgments. Result of credit assessment of the nodes is taken as a result of safety assessment. Each of hardware or components is analyzed in terms of the safety and reliability, and the result of assessment is relatively complete. A device and a computer-readable storage medium are further provided.