Saleh, Khaled (The University of Newcastle), Mihaita, Adriana-Simona (University of Technology in Sydney), Chalup, Stephan (The University of Newcastle)

Agent Trajectory Prediction in Urban Traffic Environments Via Deep Reward Learning

Scheduled for presentation during the Regular Session "Modeling, Simulation, and Control of Pedestrians and Cyclists I" (WeAT8), Wednesday, September 25, 2024, 11:10−11:30, Salon 16

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

In this paper, we address the problem of learning and modelling the behaviours of agents in urban traffic environments such as pedestrians using their trajectories. Existing state-of-the-art methods primarily rely on data-driven approaches to predict future trajectories. However, these approaches often overlook the influence of the physical environment on agents' decisions and struggle to model longer sequential trajectory data effectively. To overcome these limitations, we propose a novel hybrid framework in this paper that uses the attributes of the physical environment to predict the future trajectory that a travel agent might take on the road. First, we capture agents' preferences in various urban traffic environments using a deep reward learning technique. Next, leveraging the learned reward map and short past motion trajectories of the agents, we employ a probabilistic data-driven sequential model based on transformer networks to provide robust long-term forecasting of agents' trajectories. In our experiments, the proposed framework was evaluated on a large-scale real-world dataset of agents in urban traffic environments. Compared to state-of-the-art techniques, our framework achieves a substantial improvement by a significant margin.