Learning from AI-generated annotations is wellrecognized as a key advance of deep learning techniques in medical image segmentation. Towards this direction, in this paper, we investigate two questions: (1) how to accurately measure loss value on AI-generated annotations that often contain errors and (2) how to effectively update model’s parameters when the loss value is no longer a correct supervision for medical image segmentation. The main results are that (1) ‘error-tolerant’ loss functions exist and (2) ‘cross-training’, updating the model using data with a small loss of its ‘twin’ model, can tolerate the loss function to some extent. Per the main results, we yet derived a robust training algorithm, called confidence regularized coteaching, that helps deep models to combat annotation errors in medical image segmentation. This algorithm simultaneously trains two ‘twin’ segmentation models and updates model’s parameters by cross-training with disagreement confident data that are predicted differently by the two models, thereby being able to learning from data with annotation errors. The empirical evidence from a publicly available dataset shows that this new algorithm works better on combating annotation errors than existing methods for medical image segmentation, opening the opportunity to use AI-generated annotations to train segmentation model for medical image segmentation.

Nowadays, accurate and efficient short-term traffic flow forecasting plays a critical role in intelligent transportation systems (ITS). However, due to the fact that traffic flow is susceptible to factors such as weather and road conditions, traffic flow data tend to exhibit dynamic uncertainty and nonlinearity, making the construction of a robust and reliable forecasting model still a challenging task. Aiming at this nonlinear and complex traffic flow forecasting problem, this paper constructs a short-term traffic flow forecasting hybrid optimization model, SSA-ELM, based on extreme learning machine by embedding the sparrow search algorithm in order to solve the above problem. Extreme learning machine has been widely used in short-term traffic flow forecasting due to its characteristics such as low computational complexity and fast learning speed. By using the sparrow search algorithm to optimize the input weight values and hidden layer deviations in the extreme learning machine, the sparrow search algorithm is utilized to search for the global optimal solution while taking into account the original characteristics of the extreme learning machine, so that the model improves stability while increasing prediction accuracy. Experimental results on the Amsterdam A10 road traffic flow dataset show that the traffic flow forecasting model proposed in this paper has higher forecasting accuracy and stability, revealing the potential of hybrid optimization models in the field of short-term traffic flow forecasting.

Making an effective traffic strategy is a challenging problem that involves efficiency and safety, where traffic lights play an important role. Especially in cases without traffic lights, creating traffic strategies benefits significantly more complex and challenging. This complexity underscores the importance of research on autonomous vehicles, which has been trained widespread attention in recent years. In order to avoid these potential crises, the designed traffic strategy at traffic light-free intersections must be effective and reliable, ensuring the basic processes work properly. In this paper, we propose a novel method to formally model the traffic strategy for vehicles to make sure they safely and orderly pass through traffic light-free intersections. We use the state space analysis method to analyze the vehicle traffic model to ensure the decision made at each step is correct. By using Petri net, our proposed model achieves excellent analytical capabilities for asynchronous or highly concurrent systems than simpler Unified Modeling Language (UML) systems, as well as the ability to dynamically analyze system security and reliability.