## 1 Introduction  
Within the context of autonomous vehicle development and the field of Advanced Driver Assistance Systems (ADAS), determining the pedestrians' discrete intention is mandatory. From this information, their trajectory can be further estimated to understand the pedestrians' next actions or positions, which can greatly reduce the risk of accidents. For instance, knowing the intention of pedestrians to cross the road before they actually set foot on the road would allow the vehicle to warn the driver or automatically perform maneuvers. Therefore, preserving the pedestrians' integrity in a more efficient way than when triggered by an emergency stop once the pedestrians have moved on to the road and become a direct obstacle for the vehicle would be safer for all actors. In such decisive applications, a desirable intention prediction model should run efficiently for real-time usage and should also be robust to a multitude of complexities and conditions (e.g., weather, location). Human action recognition applied to video is a difficult research topic due to the great variation and complexity of the input data. Currently, the main modalities used for these tasks include RGB videos in their entirety [1; 2; 3; 4], optical flow [5; 6; 7; 8] and skeleton form modeling [9; 10; 11; 12]. The latter requires,prior to the action classification, an approach to estimate the human pose [13]. By focusing our work on the pedestrian skeleton structure shown in Figure 1 and since no background context is included, we are able to represent the invariant information of an action. Moreover, we are also reducing the size of the input data, which makes our approach more efficient once the pedestrian body pose is inferred. In this paper, we propose a real-time and context-invariant approach based on 2D pedestrian body poses to address the Crossing/Not Crossing (C/NC) prediction in realistic driving conditions. We evaluate the proposed approach on the Joint Attention in Autonomous Driving (JAAD) [15; 16] public data set, a standard benchmark in the matter of pedestrian behaviors prediction. To proceed, we collect human key-points for all video frames in the JAAD data set thanks to skeleton IDs and associated spatial coordinates defined in JAAD annotations. Thereafter, we develop SPI-Net: a representation-focused multi-branch network that combines Cartesian features and location-invariant geometric skeleton features. Skeleton features are provided by the Cascaded Pyramid Network (CPN) algorithm [14]. The network is divided into two branches: one focuses on the evolution of Euclidean distances relative to certain identified key-points over time, the other focuses on the evolution of the spatial representation of skeletal key-points as a function of time in the Cartesian coordinate system. The first branch corresponds to the encoder part of an auto-encoder initially trained to reconstruct an action according to the evolution over time of selected key-point distances. We add to the auto-encoder cost function a statistical supervised separability constraint to perform better separation between instances according to their class in the latent space. We obtain, in addition to a reduction of the action representation size, a first draft of class separability in the latent space. In the second branch, a 2D convolutional network, we represent a skeleton sequence as a pseudo-image. This allows us to extract spatio-temporal features using standard computer vision deep-learning methods. We then perform a late fusion on those two branches and fine-tune the entire approach in order to evaluate the model. According to experiments, SPI-Net achieved 94.4% accuracy in pedestrian crossing prediction. To the best of our knowledge, SPI-Net is at the moment, the state-of-the-art for the C/NC task on the JAAD data set. Figure 1: This figure is a non-weighted and undirected graph representation of the human skeleton, with all the selected key-points used for our pedestrian crossing prediction approach.