# Towards Explainable Pedestrian Behavior Prediction:
## 1 Introduction and Related Work  
Nowadays, it is essential to highlight that pedestrians are among the most Vulnerable Road Users (VRUs), and they represent one of the groups most severely affected by traffic incidents on European Union roads. According to the European Road Safety Observatory, pedestrians accounted for approximately 20% of all road fatalities in 2022, which means that one in five fatal accidents involved a pedestrian [1]. Although there was a reported decline in road fatalities in 2023 and 2024 [2], progress is still insufficient to achieve a truly safe environment for all road users. This data underscores the critical need for advancements in pedestrian behavior prediction (also referred to as crossing action prediction) and road safety measures to protect this vulnerable group and reduce accidents on the road. In light of this, many research communities have developed machine learning (ML) models and methods to make more robust prediction systems and to face the related challenges. For a long time, most of these models and methods have been addressed through a diverse range of algorithms that can be considered _'black boxes'_ because they lacked the ability to explain the reasoning behind their predictions. Among these approaches, various models have been employed, including those based on Recurrent Neural Networks (RNNs), Convolutional Neural Networks (ConvNets), Long Short-Term Memorys (LSTMs), and more recently, Transformer-based architectures. We can highlight methods such as SingleRNN that focuses on leveraging contextual features and employing an encoder-decoderarchitecture powered by RNNs [3]. Then, in [4], the authors use a graph-based model and 2D human pose estimation to predict whether a pedestrian is going to cross the street. The method of CapFormer [5] uses a self-attention alternative based on Transformer architecture. This method focuses primarily on bounding boxes, pose estimation, and AV speed. Additionally, a 3D Convolutional model (C3D) is employed, along with cropped regions of pedestrian bounding boxes from RGB video sequences, to facilitate spatiotemporal feature learning [6]. Another study [7] proposes an Stacked Fusion Gated Recurrent Unit (SF-GRU) approach composed of a five-layer stacked recurrent neural network that incrementally fuses multimodal features regarding pedestrian appearance, surrounding context, 2D body joint coordinates, bounding boxes over time, and ego-vehicle speed. Finally, we highlight the study by [8], where the authors propose a Convolutional LSTM (ConvLSTM) network to capture spatiotemporal dependencies. This architecture extends the traditional LSTM by incorporating convolutional operations in both the input to state and state to state transitions. For pedestrian behavior prediction, the model takes as input a stack of images, processes them using a CNN to extract spatial features, and then feeds these features into the ConvLSTM to model temporal dynamics. Furthermore, it is worth mentioning the benchmark developed by York University, which extensively assessed the performance of various ML approaches in the pedestrian behavior task. This benchmark not only standardized the evaluation criteria for the task but also introduced a model that combines the power of RNNs and 3D convolutions [9]. Despite the growing number of models and research efforts dedicated to pedestrian behavior prediction, only a limited subset explicitly addresses explainability or is designed with interpretability as a core focus. It is important to emphasize that understanding why a machine learning model makes a specific prediction remains both a significant challenge and a critical requirement, particularly in safety critical domains where interpretability builds trust and supports informed decision-making. Recent studies have begun to address this gap. For example, ref.