Using Quantum hybrid join prediction model to predict future trajectory motion during transit train movement

Abstract

Predicting future train motion from the yard to the main track via an autonomous driving train is an essential task in a terminal railway station. Existing models fail to address the multiple agents that cross the track as the train transits. Still, there is available research in this area, such as how to predict scene-compliant trajectories across multiple agents jointly. The number of agents significantly increases prediction space as the number of challenges grows exponentially. This study focuses on joint motion prediction between an interacting agent and a transit train. The complex joint prediction problem is divided into marginal sub-prediction issues. In the joint prediction space where the marginal prediction model and conditional prediction model are processed, our Quantum Hybrid Joint Prediction (QHJP) model effectively classifies both influencers and reactors. The proposed model combined the interacting agent features to improve prediction likelihood at the joint relative motion. We test and analyse our model's effectiveness as subjected to the acquired database from publicly real-world surveillance camera records from terminal railway junctions. We recreate a simulation view of an authentic, interactive agent scenario and compare the evaluation result with the well-known prediction benchmark Waymo Open Motion (WOM) dataset for experimental purposes.