The field of autonomous systems is rapidly evolving, with a focus on improving perception, navigation, and decision-making. Recent developments have seen the integration of diffusion models, probabilistic methods, and semantic priors to enhance the accuracy and robustness of autonomous systems. Notably, the use of diffusion-based architectures has shown promise in predicting human gaze behavior, generating diverse and realistic scanpaths, and improving road perception in autonomous driving. Furthermore, advancements in sensor calibration, mapping, and coverage control have also been made, enabling more efficient and reliable autonomous navigation.

Particularly noteworthy papers include: Diffusion-FS, which introduces a novel self-supervised approach for free-space sample generation and a specialized diffusion-based architecture for predicting safe multimodal navigable corridors. Probabilistic Collision Risk Estimation through Gauss-Legendre Cubature and Non-Homogeneous Poisson Processes, which presents a principled two-stage integration method for estimating collision risk in high-speed autonomous racing. MapDiffusion, which leverages the diffusion paradigm to learn the full distribution of possible vectorized maps, enabling uncertainty-aware decision-making for autonomous vehicles.