000			01468nam a22001577a 4500
082			_a629.8
100			_aNauman, Muhammad _918171
245			_aDeep Learning-based Trajectory Prediction for Autonomous Vehicles / _cMuhammad Nauman
264			_aIslamabad : _bSMME- NUST; _c2025.
300			_a54p. _bSoft Copy _c30cm
500			_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.
650			_aMS Robotics and Intelligent Machine Engineering _9119486
700			_aSupervisor : Dr. Shahbaz Khan _9125085
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/49538
942			_2ddc _cTHE
999			_c612914 _d612914