Unified Pedestrian Path Prediction Framework: A Comparison Study

Pedestrian path prediction is an emerging and crucial task in numerous applications, such as autonomous vehicles. Due to the complexity of the task, various formulations are proposed throughout the literature. However, the interconnection between these formulations remains to be seen, which makes a fair comparison challenging. This work proposes a unified pedestrian path prediction framework via Markov decision process (MDP). We demonstrate that by carefully designing the components of the MDP, various standard formulations can be perceived as specific combinations of settings in our framework. Additionally, the unified framework allows us to discover new combinations of settings that integrate the benefits of current formulations improving the prediction performance. We conduct a comparison study and evaluate several formulations in well-controlled experiments. Furthermore, we carefully assess the influence of various settings, such as policy stochasticity and sequential decision-making, on prediction performance. The goal of this work is not to propose a new state-of-the- art method but to study various formulations of the pedestrian path prediction task under a unifying framework and uncover new directions that can eventually advance the current state-of-the-art.