6 papers
ASTAD: Asymmetric Style Transfer for Synthetic-to-Real Adaptation in Autonomous Driving
Dingyi Yao, Xinqi Zhang, Lihui Peng +3
Synthetic data mitigates the data scarcity problem in autonomous driving perception. However, the synthetic-to-real gap leads to performance degradation, hindering real-world model…
EEG-Based Brain-LLM Interface for Human Preference Aligned Generation
Junzi Zhang, Jianing Shen, Weijie Tu +5
Large language models (LLMs) are becoming an increasingly important component of human--computer interaction, enabling users to coordinate a wide range of intelligent agents throug…
A Style-Based Profiling Framework for Quantifying the Synthetic-to-Real Gap in Autonomous Driving Datasets
Dingyi Yao, Xinyao Han, Ruibo Ming +5
Ensuring the reliability of autonomous driving perception systems requires extensive environment-based testing, yet real-world execution is often impractical. Synthetic datasets ha…
A re-calibration method for object detection with multi-modal alignment bias in autonomous driving
Zhihang Song, Dingyi Yao, Ruibo Ming +3
Multi-modal object detection in autonomous driving has achieved great breakthroughs due to the usage of fusing complementary information from different sensors. The calibration in…
Synthetic Dataset Evaluation Based on Generalized Cross Validation
Zhihang Song, Dingyi Yao, Ruibo Ming +3
With the rapid advancement of synthetic dataset generation techniques, evaluating the quality of synthetic data has become a critical research focus. Robust evaluation not only dri…
Timealign: A multi-modal object detection method for time misalignment fusing in autonomous driving
Zhihang Song, Lihui Peng, Jianming Hu +2
The multi-modal perception methods are thriving in the autonomous driving field due to their better usage of complementary data from different sensors. Such methods depend on calib…