Long-Term Recurrent Predictive Model for Intent Prediction of Pedestrians via Inverse Reinforcement Learning

Recently, the problem of intent and trajectory prediction of pedestrians in urban traffic environments has got some attention from the intelligent transportation research community. One of the main challenges that make this problem even harder is the uncertainty exists in the actions of pedestrians in urban traffic environments, as well as the difficulty in inferring their end goals. In this work, we are proposing a data-driven framework based on Inverse Reinforcement Learning (IRL) and the bidirectional recurrent neural network architecture (B-LSTM) for long-term prediction of pedestrians' trajectories. We evaluated our framework on real-life datasets for agent behavior modeling in traffic environments and it has achieved an overall average displacement error of only 2.93 and 4.12 pixels over 2.0 secs and 3.0 secs ahead prediction horizons respectively. Additionally, we compared our framework against other baseline models based on sequence prediction models only. We have outperformed these models with the lowest margin of average displacement error of more than 5 pixels.