01380nam a22001337a 4500082001000000100002200010245009000032264003700122300002600159500091400185650005501099700003501154856005701189  a629.8  aNauman, Muhammad   aDeep Learning-based Trajectory Prediction for Autonomous Vehicles /cMuhammad Nauman   aIslamabad : bSMME- NUST;c2025.  a54p.bSoft Copyc30cm  aAutonomous 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.   aMS Robotics and Intelligent Machine Engineering     aSupervisor : Dr. Shahbaz Khan   uhttp://10.250.8.41:8080/xmlui/handle/123456789/49538