## 2 Related Work
### Pedestrian Intention Prediction  
The problem of intention prediction of pedestrians from image sequences has gained increasing interest over the past few years. More specifically, in the context of autonomous cars in urban traffic environments, this problem is still an active research area due to its complexity and importance: while action recognition consists of using a complete sequence to label an action, intentions prediction predicts from an incomplete sequence to label an intention (i.e., before the pedestrian crosses). For intention prediction tasks, it is common to split the intention as a combination of high-level discrete behaviors as well as continuous trajectories describing the expected future movement of the pedestrian. In this paper, we address the Crossing/Not Crossing discrete intention task in realistic conditions. We, therefore, review published state-of-the-art machine-learning approaches for the prediction of pedestrian's discrete intentions in the context of autonomous driving. [PERSON] et al. [15] and [PERSON] et al. [50] formulated the problem as an image classification problem based on Alexnet [51]. Given a single position image of a pedestrian in a traffic scene, they classify whether the pedestrian is crossing or not. Afterward, they extend their model in order to take as input a sequence of consecutive cropped images of the pedestrians before they cross in order to consider the temporal coherence in short-term motions (\(\approx\)0.5 s). Similarly, [PERSON] et al. [52] propose to predict the intended actions of pedestrians based on a spatio-temporal DenseNet model. [PERSON] et al. [8]propose to extract spatial information with convolutive layers, then consider temporal dynamics with recurrent layers and propose a new metric for pedestrians dynamics evaluation: the time to cross (TTC) prediction. Some works are based on state-of-the-art generative methods in deep-learning, focusing on the future representation of the action, and then classify it in its globality: [PERSON] et al. [53] and [PERSON] et al. [54] process the crossing actions classification by feeding the predicted frames of their future frame prediction auto-encoder network into a classification network. However, those kinds of approaches have a major drawback: since background context is included, they are noise sensitive. Moreover, predicting future frames of a given scene can be time-consuming considering the type and the structure of the approach proposed, which can be a bit delicate in a real-time scenario. To overcome these issues, intention prediction based on 2D skeletal pose sequences has also been explored. Most of those approaches are currently based on the assumption that one can link the position of a pedestrian's joints previous to his action to an intention. Consequently, the problem of pedestrian discrete intention prediction is, therefore, dealt with as an action recognition task prior to the action and the action label becomes the intention. [PERSON] et al. [55] combined CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. [PERSON] et al. [56] and [PERSON] et al. [57] explore the pedestrian intention prediction task with pose estimation algorithms combined with recurrent networks. However, in [57], sequences in the wild are used, which makes it difficult to evaluate their approach on the JAAD data set. [PERSON] et al. [58] predict intentions of pedestrians crossing based on 2D skeletal pose estimation and a Graph Convolutional Network that preserves coherent spatial semantics for the pedestrian skeleton. As noted by [PERSON] et al. [59], a further step to improve the state-of-the-art of pedestrian intention prediction would be the introduction of dynamics and contextual scene information, jointly with pedestrian-specific characteristics. However, the generated context features are not always applicable to current data sets and require different kinds of modalities or hardware constraints [60; 61]. In that regard, [PERSON] et al.