Találati lista | Tudóstér

001-es BibID:	BIBFORM124169
Első szerző:	Neamah, Husam A. (mérnök)
Cím:	Multi-Agents Trajectory Prediction for Autonomous Vehicles with Multi-Modal Predictions / Husam A. Neamah, Mohammad Alghazawi, Peter Korondi
Dátum:	2024
Megjegyzések:	This paper presents an advanced deep learning framework designed to improve motion prediction in autonomous driving, enhancing safety through better environmental understanding. The Crat-Pred model is enhanced, demonstrating superior performance over existing benchmarks in both qualitative and quantitative analyses, with improved training times and computational efficiencies. The proposed approach addresses a critical research gap by predicting the motion of both vehicles and pedestrians. By utilizing convolutional neural networks and replacing Long-Short-Term Memory (LSTM) networks with Temporal Convolutional Networks (TCN), significant improvements in computational speed and efficiency are achieved. The primary objective is to develop a unified prediction model for all road agents, advancing autonomous navigation. Experiments using the Argoverse 2 dataset validate the model's performance in real-world conditions, showcasing its superiority in forward motion prediction and computational efficiency.
ISBN:	9798350378245
Tárgyszavak:	Műszaki tudományok Informatikai tudományok tanulmány, értekezés könyvrészlet autonomous driving motion prediction Temporal Convolutional Network (TCN) multi-agents multi-modal
Megjelenés:	IEEE CogInfoCom 2024: 2024 15th IEEE International Conference on Cognitive Infocommunications / . - p. 259-264. -
További szerzők:	Alghazawi, Mohammad Korondi Péter (1960-) (villamosmérnök)
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: