Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction
Authors: M. Eren Akbiyik, Nedko Savov, Danda Pani Paudel, Nikola Popovic, Christian Vater, Otmar Hilliges, Luc Van Gool, Xi Wang
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations on GEM and DR(eye)VE demonstrate that Route Former significantly outperforms state-of-the-art methods, achieving notable improvements in prediction accuracy across diverse conditions. Ablation studies reveal that incorporating driver field-of-view data yields significantly better average displacement error, especially in challenging scenarios with high PCI scores, underscoring the importance of modeling driver attention. |
| Researcher Affiliation | Academia | 1 ETH Z urich 2 INSAIT, Sofia University St. Kliment Ohridski 3 University of Bern 4 TU Munich |
| Pseudocode | No | The paper describes the RouteFormer architecture and its components in detail using text and diagrams (Figure 1, Figure 2) and mathematical formulations for loss functions, but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | All data and code is available at meakbiyik.github.io/routeformer. |
| Open Datasets | Yes | To tackle data scarcity and enhance diversity, we introduce GEM, a comprehensive dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data. All data and code is available at meakbiyik.github.io/routeformer. We also evaluate Route Former on the DR(eye)VE dataset (Palazzi et al., 2018). |
| Dataset Splits | Yes | We choose different drivers for training and evaluation according to the data splits of either dataset (see Appendix D) to better demonstrate the benefit of FOV. Table 13: Breakdown of our GEM dataset. We report duration, weather condition, and PCI for each participant. ... Split: train, val., test |
| Hardware Specification | No | All experiments are carried out on a machine with an NVIDIA GPU with > 10 GB memory, 64 GB of RAM, and an Intel CPU. |
| Software Dependencies | Yes | The framework is implemented using Py Torch 2.0. |
| Experiment Setup | Yes | Route Former is trained using the Adam W optimizer with a linear warm-up of 2 epochs and cosine annealing, over a total of 200 epochs. Maximum learning rate of 1 10 5 and weight decay of 1 10 4 are used with batch size 16. A full set of hyperparameters can be found in Appendix B.2. |