Trajectory prediction is a critical component of autonomous driving, essential for ensuring both safety and efficiency on the road. However, traditional approaches often struggle with the scarcity of labeled data and exhibit suboptimal performance in multi-agent prediction scenarios. To address these challenges, we introduce a disentangled context-aware pre-training framework for multi-agent motion prediction, named DECAMP. Unlike existing methods that entangle representation learning with pretext tasks, our framework decouples behavior pattern learning from latent feature reconstruction, prioritizing interpretable dynamics and thereby enhancing scene representation for downstream prediction. Additionally, our framework incorporates context-aware representation learning alongside collaborative spatial-motion pretext tasks, which enables joint optimization of structural and intentional reasoning while capturing the underlying dynamic intentions. Our experiments on the Argoverse 2 benchmark showcase the superior performance of our method, and the results attained underscore its effectiveness in multi-agent motion forecasting. To the best of our knowledge, this is the first context autoencoder framework for multi-agent motion forecasting in autonomous driving. The code and models will be made publicly available.

In contrast to the science fiction portrayal of evil computers plotting to overthrow humankind, artificial intelligence (AI) in fact seems poised to help improve human health in a multitude of ways, including flagging suspicious moles for dermatologist follow-up, monitoring blood volume in military field personnel and tracking flu outbreaks via Twitter. The Colorado Clinical and Translational Science Institute (CCTSI) recently held the 7th annual CU-CSU Summit on the topic of "AI and Machine Learning in Biomedical Research", with over 150 researchers, clinicians and student attendees from all three CU campuses and CSU. Ronald Sokol, MD, CCTSI director, said, "The purpose of the CCTSI is to accelerate and catalyze translating discoveries into better patient care and population health by bringing together expertise from all our partners." Rather than individual campuses operating in silos, the annual Summit brings together clinicians, basic and clinical researchers, post-doctoral fellows, mathematicians and others to highlight ongoing research excellence, establish collaborations and increase interconnectivity of the four campuses. This year's conference on AI hit capacity for registration, including attendance by more mathematicians and with more poster submissions than the preceding six events.