Multi-Agents Trajectory Prediction for Autonomous Vehicles with Multi-Modal Predictions.

dc.contributor.advisorAlmusawi, Husam
dc.contributor.authorAlghazawi, Mohammad
dc.contributor.departmentDE--Műszaki Kar
dc.date.accessioned2025-09-04T16:40:11Z
dc.date.available2025-09-04T16:40:11Z
dc.date.created2024
dc.description.abstract1. A comprehensive model to predict the motion of multi-agents on the road. 2. The model is designed to capture social interactions between agents, without relying on the map information. 3. Each actor is encoded by a Temporal Convolutional Network (TCN) to capture temporal interactions. 4. Applies a graph convolution method and combines it with multi-head self-attention. 5. Apply multi-modal predictions to determine the probability of each individual mode.
dc.description.courseMechatronical Engineeringen
dc.description.degreeMSc/MA
dc.format.extent76
dc.identifier.urihttps://hdl.handle.net/2437/397321
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectAutonomous Driving, Motion Prediction, Temporal Convolutional Network (TCN), Multi-Agents, Multi-Modal.
dc.subject.dspaceEngineering Sciences
dc.titleMulti-Agents Trajectory Prediction for Autonomous Vehicles with Multi-Modal Predictions.
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