Vision-based trajectory prediction is an important task that supports safe and intelligent behaviors in autonomous systems. Many advanced approaches have been proposed over the years with improved spatial and temporal feature extraction. However, human behavior is naturally diverse and uncertain. Given the past trajectory and surrounding environment information, an agent can have multiple plausible trajectories in the future. To tackle this problem, an essential task named multi-future trajectory prediction (MTP) has recently been studied. This task aims to generate a diverse, acceptable, and explainable distribution of future predictions for each agent. In this article, we present the first survey for MTP with our unique taxonomies and a comprehensive analysis of frameworks, datasets, and evaluation metrics. We also compare models on existing MTP datasets and conduct experiments on the ForkingPath dataset. Finally, we discuss multiple future directions that can help researchers develop novel MTP systems and other diverse learning tasks similar to MTP.

Recently, heatmap-oriented approaches have demonstrated their state-of-the-art performance in pedestrian trajectory prediction by exploiting scene information from input images before running the encoder. To align the image and trajectory information, existing methods centre the scene images to agents' last observed locations or convert trajectory sequences into images. Such alignment processes cause repetitive executions of the scene encoder for each pedestrian in an input image while there are often many pedestrians in an image, thus leading to significant memory consumption. In this letter, we address this problem by fully decoupling scene and trajectory feature extractions so that the scene information is only encoded once for an input image regardless of the number of pedestrians in the image. To do this, we directly extract temporal information from trajectories in a global pixel coordinate system. Then, we propose a transformer-based heatmap decoder to model the complex interaction between high-level trajectory and image features via trajectory self-attention, trajectory-to-image cross-attention and image-to-trajectory cross-attention layers. We also introduce scene counterfactual learning to alleviate the over-focusing on the trajectory features and knowledge transfer from Segment Anything Model to simplify the training. Our experiments show that our framework shows highly competitive performance on multiple benchmarks, demonstrating scene-compliant predictions on complex terrains and much less memory consumption when handling multi-pedestrians.

This study presents a deep learning–based wildfire behaviour model developed for iFire 2.0, an immersive visualisation project by iCinema, UNSW. The model integrates fuel conditions, ignition points, and weather conditions to simulate fire spread and crown fire activity. It demonstrates the ability to generate unforeseen dynamic scenarios under changing conditions, marking an important step toward modelling and visualising extreme wildfire behaviour, allowing users to modify variables and experience resulting changes in wildfire dynamics.