no code implementations • 17 Sep 2023 • Ruochen Jiao, YiXuan Wang, Xiangguo Liu, Chao Huang, Qi Zhu
However, it remains a challenging problem for these methods to ensure that the generated/predicted trajectories are physically realistic.
no code implementations • 9 Mar 2023 • Ruochen Jiao, Juyang Bai, Xiangguo Liu, Takami Sato, Xiaowei Yuan, Qi Alfred Chen, Qi Zhu
We conduct extensive experiments to demonstrate that our supervised method based on contrastive learning and unsupervised method based on reconstruction with semantic latent space can significantly improve the performance of anomalous trajectory detection in their corresponding settings over various baseline methods.
1 code implementation • ICCV 2023 • Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu
In this paper, we present a novel adversarial training method for trajectory prediction.
no code implementations • 27 May 2022 • Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu
In addition, experiments show that our method can significantly improve the system's robust generalization to unseen patterns of attacks.
no code implementations • 2 Mar 2022 • Ruochen Jiao, Xiangguo Liu, Bowen Zheng, Dave Liang, Qi Zhu
Our model addresses trajectory generation and prediction in a unified architecture and benefits both tasks: the model can generate diverse, controllable and realistic trajectories to enhance planner optimization in safety-critical and long-tailed scenarios, and it can provide prediction of critical behavior in addition to the final trajectories for decision making.
no code implementations • 15 Feb 2021 • Xiangguo Liu, Baiting Luo, Ahmed Abdo, Nael Abu-Ghazaleh, Qi Zhu
While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them could be devastating.
Cryptography and Security