Contextual Recurrent Predictive Model for Long-Term Intent Prediction of Vulnerable Road Users

Recently, the problem of intent and trajectory prediction of vulnerable road users (VRUs) 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 paper, 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 VRUs' intention. 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 and 3.0 s ahead prediction horizons, respectively. In addition, we compared our framework against other baseline models based on sequence prediction models and planning-based approaches. We have outperformed these approaches with the lowest margin of average displacement error of more than 5 pixels. Furthermore, the performance of the proposed framework was evaluated on an additional vehicle-based video sequence dataset for path prediction of pedestrians and it continued to achieve robust results with higher generalization capabilities.