Deep Learning-based Trajectory Prediction for Autonomous Vehicles / (Record no. 612914)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01468nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Nauman, Muhammad |
| 245 ## - TITLE STATEMENT | |
| Title | Deep Learning-based Trajectory Prediction for Autonomous Vehicles / |
| Statement of responsibility, etc. | Muhammad Nauman |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 54p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Autonomous driving heavily relies on accurate trajectory prediction to optimize route planning<br/>and enhance vehicle safety. Current deep learning-based trajectory models have demonstrated<br/>remarkable success on public datasets but often fall short in real-time applications due to<br/>computational limitations in vehicles. In this research, we propose LaneFormer, an optimized<br/>trajectory prediction framework designed to balance high predictive accuracy with<br/>computational efficiency, ensuring its suitability for real-time deployment in autonomous<br/>systems. Our model introduces an efficient attention mechanism to capture complex interactions<br/>between agents and road structures, outperforming state-of-the-art methods while using fewer<br/>resources. We evaluate LaneFormer on the Argoverse dataset, demonstrating its robustness in<br/>predicting future trajectories with competitive metrics across multimodal scenarios. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Shahbaz Khan |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/49538">http://10.250.8.41:8080/xmlui/handle/123456789/49538</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 02/19/2025 | 629.8 | SMME-TH-1111 | Thesis |