Embodied visual navigation remains a challenging task, as agents must explore unknown environments with limited knowledge. Existing zero-shot studies have shown that incorporating memory mechanisms to support goal-directed behavior can improve long-horizon planning performance. However, they overlook visual frontier boundaries, which fundamentally dictate future trajectories and observations, and fall short of inferring the relationship between partial visual observations and navigation goals. In this paper, we propose Semantic Cognition Over Potential-based Exploration (SCOPE), a zero-shot framework that explicitly leverages frontier information to drive potential-based exploration, enabling more informed and goal-relevant decisions. SCOPE estimates exploration potential with a Vision-Language Model and organizes it into a spatio-temporal potential graph, capturing boundary dynamics to support long-horizon planning. In addition, SCOPE incorporates a self-reconsideration mechanism that revisits and refines prior decisions, enhancing reliability and reducing overconfident errors. Experimental results on two diverse embodied navigation tasks show that SCOPE outperforms state-of-the-art baselines by 4.6% in accuracy. Further analysis demonstrates that its core components lead to improved calibration, stronger generalization, and higher decision quality.

Forecasting multiple pedestrian trajectories is a challenging task for real-world applications, as the motion patterns of pedestrian are essentially stochastic and uncertain. Previous works have demonstrated that predicting diverse goals in advance can effectively improve the performance of pedestrian trajectory prediction. However, these methods are either unable to perform probabilistic and high-efficiency trajectory prediction, or mainly rely on the predefined template trajectories which are not high-performance and insufficient to represent the possible pedestrian behaviors. In this paper, we propose a new Probabilistic Proposal Network (PPNet) to concentrate on the generation of goals and the utilization of goal guidance. PPNet firstly generates multiple weighted goals based on the diverse latent intentions automatically obtained by unsupervised learning, and then designs the goal-conditioned Transformer networks to predict probabilistic proposals as the final trajectories. Extensive experimental results on ETH/UCY datasets and Stanford Drone Dataset indicate that PPNet achieves both state-of-the-art performance and high efficiency on pedestrian trajectory prediction.

To safely and rationally participate in dense and heterogeneous traffic, autonomous vehicles require to sufficiently analyze the motion patterns of surrounding traffic-agents and accurately predict their future trajectories. This is challenging because the trajectories of traffic-agents are not only influenced by the traffic-agents themselves but also by spatial interaction with each other. Previous methods usually rely on the sequential step-by-step processing of Long Short-Term Memory networks (LSTMs) and merely extract the interactions between spatial neighbors for single type traffic-agents. We propose the Spatio-Temporal Transformer Networks (S2TNet), which models the spatio-temporal interactions by spatio-temporal Transformer and deals with the temporel sequences by temporal Transformer. We input additional category, shape and heading information into our networks to handle the heterogeneity of traffic-agents. The proposed methods outperforms state-of-the-art methods on ApolloScape Trajectory dataset by more than 7% on both the weighted sum of Average and Final Displacement Error.