Different road users follow different behaviors and intentions in the trajectories that they traverse. Predicting the intent of these road users at intersections would not only help increase the comfort of drive in autonomous vehicles, but also help detect potential accidents. In this thesis, the research objective is to build models that predicts future positions of road users (pedestrians,cyclists and autonomous shuttles) by capturing behaviors endemic to different road users.  Firstly, a constant velocity state space model is used as a benchmark for intent prediction, with a fresh approach to estimate parameters from the data through the EM algorithm. Then, a neural network based LSTM sequence modeling architecture is used to better capture the dynamics of road user movement and their dependence on the spatial area. Inspired by the recent success of transformers and attention in text mining, we then propose a mechanism to capture the road users' social behavior amongst their neighbors. To achieve this, past trajectories of different road users are forward propagated through the LSTM network to obtain representative feature vectors for each road users' behaviour. These feature vectors are then passed through an attention-layer to obtain representations that incorporate information from other road users' feature vectors, which are in-turn used to predict future positions for every road user in the frame. It is seen that the attention based LSTM model slightly outperforms the plain LSTM models, while both substantially outperform the constant velocity model. A comparative qualitative analysis is performed to assess the behaviors that are captured/missed by the different models. The thesis concludes with a dissection of the behaviors captured by the attention module.