INNAbstract: An INN-Based Abstraction Method for Large-Scale Neural Network Verification.

Neural networks (NNs) have witnessed widespread deployment across various domains, including some safety-critical applications. In this regard, the demand for verifying means of such artificial intelligence techniques is more and more pressing. Nowadays, the development of evaluation approaches for NNs is a hot topic that is attracting considerable interest, and a number of verification methods have been proposed.

A risk assessment study on accidents at French level crossings using Bayesian belief networks.

Level crossing (LX) safety continues to be one of the most critical issues for railways despite an ever increasing focus on improving design and practices. In the present paper, a framework of probabilistic risk assessment and improvement decision based on Bayesian belief networks (PRAID-BBN) is proposed. The developed framework aims to analyse various impacting factors which may cause LX accidents, and quantify the contribution of these factors so as to identify the crucial factors which contribute most to the LX accidents.

A new insight on the risky behavior of motorists at railway level crossings: An observational field study.

Accidents at railway level crossings (LXs) give rise to serious material and human damage. Particularly, collisions between trains and motorized vehicles are the most critical accidents occurring at LXs. It is worth noticing that violations committed by vehicle drivers are the primary cause of such accidents.