Deep Learning-based Trajectory Prediction for Autonomous Vehicles

Abstract

Autonomous driving heavily relies on accurate trajectory prediction to optimize route planning and enhance vehicle safety. Current deep learning-based trajectory models have demonstrated remarkable success on public datasets but often fall short in real-time applications due to computational limitations in vehicles. In this research, we propose LaneFormer, an optimized trajectory prediction framework designed to balance high predictive accuracy with computational efficiency, ensuring its suitability for real-time deployment in autonomous systems. Our model introduces an efficient attention mechanism to capture complex interactions between agents and road structures, outperforming state-of-the-art methods while using fewer resources. We evaluate LaneFormer on the Argoverse dataset, demonstrating its robustness in predicting future trajectories with competitive metrics across multimodal scenarios.