The field of assistive technologies is experiencing a significant shift towards the development of innovative solutions that promote inclusive interaction for individuals with disabilities. Recent advancements in artificial intelligence, machine learning, and virtual reality are being leveraged to create personalized and adaptive systems that support users with diverse needs. Researchers are focusing on designing accessible and engaging interfaces that facilitate interaction, exploration, and learning for people with visual, hearing, and cognitive impairments. Noteworthy developments include the creation of mixed-initiative AI assistance for non-visual cooking, such as AROMA, and AI-empowered virtual reality prototypes like RemVerse, which supports reminiscence activities for older adults. These developments highlight the potential for assistive technologies to transform the lives of individuals with disabilities, promoting independence, autonomy, and inclusivity. In the field of autonomous driving, researchers are making significant progress in developing more accurate and efficient prediction models. The integration of self-supervised learning, spatial-temporal risk-attentive frameworks, and multimodal benchmarking has improved the performance of autonomous vehicles in complex scenarios. Particularly noteworthy papers include STRAP, which proposes a novel spatial-temporal risk-attentive trajectory prediction framework, and Foresight in Motion, which introduces an interpretable, reward-driven intention reasoner to enhance trajectory prediction confidence. The development of large-scale benchmarks for human behavior analysis has also provided a comprehensive evaluation suite for assessing the understanding of human behavior in autonomous driving. Furthermore, the field of autonomous driving and traffic accident prediction is rapidly advancing, with a focus on developing more accurate and efficient models for predicting and preventing accidents. The use of large language models, generative video models, and multimodal fusion techniques has led to significant improvements in accuracy and robustness. Notable papers in this area include ALCo-FM, which introduces an adaptive long-context foundation model for accident prediction, and Domain-Enhanced Dual-Branch Model, which presents a framework for efficient and interpretable accident anticipation. The field of human-computer interaction and simulation-based research is also moving towards a more inclusive and diverse direction. Researchers are developing innovative technologies and methods to support equitable participation in various domains, such as creative industries, transportation, and healthcare. The integration of accessibility and inclusivity considerations into the design of motion capture systems, virtual reality training platforms, and simulation tools is a key trend. Noteworthy papers include EqualMotion, which introduces a body-agnostic motion capture system, and OpenCAMS, an open-source co-simulation platform that combines the strengths of three best-in-class simulation tools. Overall, these developments demonstrate the potential for technology to promote inclusive interaction, autonomous systems, and equitable participation, ultimately enhancing the lives of individuals with disabilities and improving safety-critical applications.