Hybrid Motion Prediction for Autonomous Vehicles using GNN-Transformer Architecture

Abstract

Accurate perception and scene understanding are pivotal in enabling autonomous vehicles to navigate safely and intelligently. This paper presents an integrated perception module comprising three core subcomponents: real-time object detection using YOLOv5, lane-keeping using a CNN-based steering predictor, and a novel motion prediction architecture based on a hybrid Graph Neural Network (GNN) and Transformer design. The system is deployed and validated within the CARLA simulation environment, with custom data generation pipelines designed to mimic real-world behavioral patterns of nearby agents. The novelty lies in the hybrid GNN-Transformer model, which effectively captures both spatial and temporal interactions of dynamic objects for behavior classification. Experimental results demonstrate a high accuracy of 98.75% in classifying behaviors into four categories: Going, Coming, Crossing, and Stopped. This paper details the architecture, dataset creation, training methodology, and performance evaluation, highlighting the hybrid model's potential to improve trajectory planning modules in autonomous systems.