SWIFT: Strategic Weather-Informed Image-Based Forecasting for Trajectories

Predicting agents’ trajectories in complex environ- ments is critical for achieving safe autonomous robot navigation. Empirically, agents’ decisions and preferences are susceptible to changes in environmental factors (e.g., interactions with other agents, weather conditions, traffic rules). State-of-the-art meth- ods rely on High-Definition (HD) or semantic maps to model the environment, but do not take into account unpredictable factors such as complex weather conditions. In addition, since HD maps are nontrivial to obtain, those methods are limited in the scope of environments they can be applied in. We propose a more flexible graph based trajectory prediction model that uses only images to model the environment, without requiring expensive map information. We experimentally validate our proposed model, demonstrating robust performances in trajectory pre- diction compared to state-of-the-art methods, and outperform in complex environments that cannot be modeled with purely map based methods, such as diverse weather conditions.