no code implementations • 2 May 2024 • Yin Huang, Yongqi Dong, Youhua Tang, Li Li
The escalation in urban private car ownership has worsened the urban parking predicament, necessitating effective parking availability prediction for urban planning and management.
no code implementations • 7 Dec 2023 • Lanxin Zhang, Yongqi Dong, Haneen Farah, Arkady Zgonnikov, Bart van Arem
Moreover, previous ML-based approaches predominantly utilize basic vehicle motion features (such as velocity and acceleration) to label and detect abnormal driving behaviors, while this study seeks to introduce Surrogate Safety Measures (SSMs) as the input features for ML models to improve the detection performance.
no code implementations • 7 Dec 2023 • Yongqi Dong, Xingmin Lu, Ruohan Li, Wei Song, Bart van Arem, Haneen Farah
In conclusion, the proposed pipeline, with its incorporation of self-supervised pre-training using MiM and other advanced deep learning techniques, emerges as a robust solution for enhancing the accuracy and efficiency of lane rendering image anomaly detection in digital navigation systems.
1 code implementation • 20 Jun 2023 • Yongqi Dong, Tobias Datema, Vincent Wassenaar, Joris van de Weg, Cahit Tolga Kopar, Harim Suleman
Furthermore, to train a uniform driving model that can tackle various driving maneuvers besides the specific ones, this study expanded the highway-env and developed an extra customized training environment, namely, ComplexRoads, integrating various driving maneuvers and multiple road scenarios together.
no code implementations • 20 Jun 2023 • Henan Yuan, Penghui Li, Bart van Arem, Liujiang Kang, Yongqi Dong
Experimental results on various testing scenarios reveal that the TRPO algorithm outperforms DDPG and PPO in terms of safety and efficiency, and PPO performs best in terms of comfort level.
no code implementations • 5 Jun 2023 • Yongqi Dong, KeJia Chen, Zhiyuan Ma
This study systematically compares semi-supervised learning methods applied for anomaly detection in hydraulic condition monitoring systems.
no code implementations • 30 May 2023 • Chaozhong Xue, Yongqi Dong, Jiaqi Liu, Yijun Liao, Lingbo Li
To tackle the emerging challenges, this study designs a reverse logistics system architecture with three modules, i. e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module.
no code implementations • 26 May 2023 • Ruohan Li, Yongqi Dong
The masked sequential autoencoders are adopted to pre-train the neural network models with reconstructing the missing pixels from a random masked image as the objective.
no code implementations • 9 Feb 2023 • Chaozhong Xue, Yongqi Dong, Jiaqi Liu, Yijun Liao, Lingbo Li
To tackle the challenges, this study proposes a reverse logistics system architecture with three modules, i. e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module.
no code implementations • 21 Jul 2022 • Yongqi Dong, KeJia Chen, Yinxuan Peng, Zhiyuan Ma
To enhance the security of in-vehicle networks and promote the research in this area, based upon a large scale of CAN network traffic data with the extracted valuable features, this study comprehensively compared fully-supervised machine learning with semi-supervised machine learning methods for CAN message anomaly detection.
no code implementations • 5 Oct 2021 • Yongqi Dong, Sandeep Patil, Bart van Arem, Haneen Farah
Since lane markings are continuous lines, the lanes that are difficult to be accurately detected in the current single image can potentially be better deduced if information from previous frames is incorporated.