Zobrazeno 1 - 10
of 874
pro vyhledávání: '"Wong, Anthony"'
Autor:
Sun, Jiawei, Li, Jiahui, Liu, Tingchen, Yuan, Chengran, Sun, Shuo, Huang, Zefan, Wong, Anthony, Tee, Keng Peng, Ang Jr, Marcelo H.
We introduce RMP-YOLO, a unified framework designed to provide robust motion predictions even with incomplete input data. Our key insight stems from the observation that complete and reliable historical trajectory data plays a pivotal role in ensurin
Externí odkaz:
http://arxiv.org/abs/2409.11696
Autor:
Yuan, Chengran, Zhang, Zhanqi, Sun, Jiawei, Sun, Shuo, Huang, Zefan, Lee, Christina Dao Wen, Li, Dongen, Han, Yuhang, Wong, Anthony, Tee, Keng Peng, Ang Jr, Marcelo H.
Motion planning is a challenging task to generate safe and feasible trajectories in highly dynamic and complex environments, forming a core capability for autonomous vehicles. In this paper, we propose DRAMA, the first Mamba-based end-to-end motion p
Externí odkaz:
http://arxiv.org/abs/2408.03601
Autor:
Sun, Jiawei, Yuan, Chengran, Sun, Shuo, Wang, Shanze, Han, Yuhang, Ma, Shuailei, Huang, Zefan, Wong, Anthony, Tee, Keng Peng, Ang Jr, Marcelo H.
The ability to accurately predict feasible multimodal future trajectories of surrounding traffic participants is crucial for behavior planning in autonomous vehicles. The Motion Transformer (MTR), a state-of-the-art motion prediction method, alleviat
Externí odkaz:
http://arxiv.org/abs/2404.10295
This paper presents a novel evaluation framework for Out-of-Distribution (OOD) detection that aims to assess the performance of machine learning models in more realistic settings. We observed that the real-world requirements for testing OOD detection
Externí odkaz:
http://arxiv.org/abs/2211.10892
Autor:
Sun, Jiawei, Yuan, Chengran, Sun, Shuo, Liu, Zhiyang, Goh, Terence, Wong, Anthony, Tee, Keng Peng, Ang Jr, Marcelo H.
Accurately predicting interactive road agents' future trajectories and planning a socially compliant and human-like trajectory accordingly are important for autonomous vehicles. In this paper, we propose a planning-centric prediction neural network,
Externí odkaz:
http://arxiv.org/abs/2211.06031
Anomaly detection is to recognize samples that differ in some respect from the training observations. These samples which do not conform to the distribution of normal data are called outliers or anomalies. In real-world anomaly detection problems, th
Externí odkaz:
http://arxiv.org/abs/2207.01112
Anomaly detection is to identify samples that do not conform to the distribution of the normal data. Due to the unavailability of anomalous data, training a supervised deep neural network is a cumbersome task. As such, unsupervised methods are prefer
Externí odkaz:
http://arxiv.org/abs/2207.01106
Autor:
Herlemann, Annika, Cowan, Janet E., Washington, Samuel L., 3rd, Wong, Anthony C., Broering, Jeanette M., Carroll, Peter R., Cooperberg, Matthew R.
Publikováno v:
In European Urology June 2024 85(6):565-573
Autor:
Wong, Anthony, Lam, Francis Y.T., Hernandez, Matthew, Lara, Jaden, Trinh, T. Michael, Kelly, Rory P., Ochiai, Tatsumi, Rao, Guodong, Britt, R. David, Kaltsoyannis, Nikolas, Arnold, Polly L.
Publikováno v:
In Chem Catalysis 16 May 2024 4(5)
Autor:
Thompson, Lisa, Patrianakos, Thomas, Garcia, James, Wadhwa, Arshia, Yang, Ellen, Thomas, Catherine, Schneider, Robin, Stern, Hudson, Palma, Camille, Hill, Kyle, Barquet, Viviana, Anderson-Nelson, Susan, McMahon, Kenneth, Patel, Umangi, Ghadiali, Quraish, Danek, Dagmara, Larsen, Brian, Wong, Anthony, Dawood, Sherif, Ludington, Maxine, Zhang, George, Garakani, Roya, Dwarakanathan, Surendar, Simotas, Athina, Sangal, Kajal, Phelps, Paul, Ashourian, Taylor, Darwish, Dana, Bamba, Sonya, Zein, Michael, Nichols, Jeffrey, To, Josiah, Sarmiento, Angelo, Chaudhary, Shweta, Breshears, Brett, Nelson, Josh, Giovingo, Michael
Publikováno v:
In Disease-a-Month May 2024 70(5)