Assessing the safety of vision-language models (VLMs) in autonomous driving is particularly important; however, existing work mainly focuses on traditional benchmark evaluations. As interactive components within autonomous driving systems, VLMs must maintain strong safety cognition during interactions. From this perspective, we propose a novel evaluation method: Safety Cognitive Driving Benchmark (SCD-Bench) . To address the large-scale annotation challenge for SCD-Bench, we develop the Autonomous Driving Image-Text Annotation System (ADA) . Additionally, to ensure data quality in SCD-Bench, our dataset undergoes manual refinement by experts with professional knowledge in autonomous driving. We further develop an automated evaluation method based on large language models (LLMs). To verify its effectiveness, we compare its evaluation results with those of expert human evaluations, achieving a consistency rate of 99.74%. Preliminary experimental results indicate that existing open-source models still lack sufficient safety cognition, showing a significant gap compared to GPT-4o. Notably, lightweight models (1B-4B) demonstrate minimal safety cognition. However, since lightweight models are crucial for autonomous driving systems, this presents a significant challenge for integrating VLMs into the field.

Continual learning (CL) has been an essential topic in the contemporary application of deep neural networks, where catastrophic forgetting (CF) can impede a model's ability to acquire knowledge progressively. Existing CL strategies primarily address CF by regularizing model updates or separating task-specific and shared components. However, these methods focus on task model elements while overlooking the potential of leveraging inter-task relationships for learning enhancement. To address this, we propose a transferability-aware task embedding named H-embedding and train a hypernet under its guidance to learn task-conditioned model weights for CL tasks. Particularly, H-embedding is introduced based on an information theoretical transferability measure and is designed to be online and easy to compute. The framework is also characterized by notable practicality, which only requires storing a low-dimensional task embedding for each task, and can be efficiently trained in an end-to-end way. Extensive evaluations and experimental analyses on datasets including Permuted MNIST, Cifar10/100, and ImageNet-R demonstrate that our framework performs prominently compared to various baseline methods, displaying great potential in exploiting intrinsic task relationships.

With the development of Multimodal Large Language Models (MLLMs) technology, its general capabilities are increasingly powerful. To evaluate the various abilities of MLLMs, numerous evaluation systems have emerged. But now there is still a lack of a comprehensive method to evaluate MLLMs in the tasks related to flowcharts, which are very important in daily life and work. We propose the first comprehensive method, FlowCE, to assess MLLMs across various dimensions for tasks related to flowcharts. It encompasses evaluating MLLMs' abilities in Reasoning, Localization Recognition, Information Extraction, Logical Verification, and Summarization on flowcharts. However, we find that even the GPT4o model achieves only a score of 56.63. Among open-source models, Phi-3-Vision obtained the highest score of 49.97. We hope that FlowCE can contribute to future research on MLLMs for tasks based on flowcharts. \url{https://github.com/360AILAB-NLP/FlowCE} \end{abstract}