End-to-end autonomous driving (E2E-AD) has emerged as a compelling alternative to traditional modular pipelines. While recent approaches achieve strong performance on single-domain datasets, their performance degrades significantly when trained jointly across multiple heterogeneous domains. We propose HEAT, a trajectory-driven learning paradigm that organizes training around planning trajectories, enabling the model to capture domain-invariant representations of driving intent.
@article{yang2025heat,title={HEAT: Heterogeneous End-to-End Autonomous Driving via Trajectory-Guided World Models},author={Cho, Hoonhee and Lee, Giwon and Kang, Jae-Young and Yang, Hyemin and Park, Heejun and Yoon, Kuk-Jin},journal={arXiv preprint},year={2026},}
2025
NeurIPS
VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting
Hoonhee Cho, Jae-Young Kang, Giwon Lee, Hyemin Yang, Heejun Park, Seokwoo Jung, and Kuk-Jin Yoon
In Conference on Neural Information Processing Systems, 2025
End-to-end autonomous driving (E2E-AD) has emerged as a promising paradigm that unifies perception, prediction, and planning into a holistic, data-driven framework. However, achieving robustness to varying camera viewpoints, a common real-world challenge due to diverse vehicle configurations, remains an open problem. In this work, we propose VR-Drive, a novel E2E-AD framework that addresses viewpoint generalization by jointly learning 3D scene reconstruction as an auxiliary task to enable planning-aware view synthesis.
@inproceedings{cho2024vrdrive,title={VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting},author={Cho, Hoonhee and Kang, Jae-Young and Lee, Giwon and Yang, Hyemin and Park, Heejun and Jung, Seokwoo and Yoon, Kuk-Jin},booktitle={Conference on Neural Information Processing Systems},year={2025},}