| Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network X Mo, Z Huang, Y Xing, C Lv IEEE Transactions on Intelligent Transportation Systems 23 (7), 9554-9567, 2022 | 147* | 2022 |
| Learning to compose and reason with language tree structures for visual grounding R Hong, D Liu, X Mo, X He, H Zhang IEEE transactions on pattern analysis and machine intelligence 44 (2), 684-696, 2019 | 120 | 2019 |
| Multi-modal motion prediction with transformer-based neural network for autonomous driving Z Huang, X Mo, C Lv 2022 International Conference on Robotics and Automation (ICRA), 2605-2611, 2022 | 88 | 2022 |
| Interaction-aware trajectory prediction of connected vehicles using CNN-LSTM networks X Mo, Y Xing, C Lv IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics …, 2020 | 64 | 2020 |
| Toward safe and smart mobility: Energy-aware deep learning for driving behavior analysis and prediction of connected vehicles Y Xing, C Lv, X Mo, Z Hu, C Huang, P Hang IEEE Transactions on Intelligent Transportation Systems 22 (7), 4267-4280, 2021 | 53 | 2021 |
| Effect of adding edges to consensus networks with directed acyclic graphs HT Zhang, Z Chen, X Mo* IEEE Transactions on Automatic Control 62 (9), 4891-4897, 2017 | 48 | 2017 |
| Graph and recurrent neural network-based vehicle trajectory prediction for highway driving X Mo, Y Xing, C Lv 2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021 | 45 | 2021 |
| Recog: A deep learning framework with heterogeneous graph for interaction-aware trajectory prediction X Mo, Y Xing, C Lv arXiv preprint arXiv:2012.05032, 2020 | 38 | 2020 |
| Secure pose estimation for autonomous vehicles under cyber attacks Q Liu, Y Mo, X Mo, C Lv, E Mihankhah, D Wang 2019 IEEE Intelligent Vehicles Symposium (IV), 1583-1588, 2019 | 37 | 2019 |
| Effects of adding a reverse edge across a stem in a directed acyclic graph X Mo, Z Chen, HT Zhang Automatica 103, 254-260, 2019 | 24 | 2019 |
| Recoat: A deep learning-based framework for multi-modal motion prediction in autonomous driving application Z Huang, X Mo, C Lv 2022 IEEE 25th International Conference on Intelligent Transportation …, 2022 | 14 | 2022 |
| Augmenting reinforcement learning with transformer-based scene representation learning for decision-making of autonomous driving H Liu, Z Huang, X Mo, C Lv IEEE Transactions on Intelligent Vehicles, 2024 | 12 | 2024 |
| Multi-modal interactive agent trajectory prediction using heterogeneous edge-enhanced graph attention network X Mo, Z Huang, C Lv Workshop on Autonomous Driving, CVPR 6, 7, 2021 | 10 | 2021 |
| Recoat: A deep learning framework with attention mechanism for multi-modal motion prediction Z Huang, X Mo, C Lv Workshop on Autonomous Driving, CVPR, 2021 | 7 | 2021 |
| Map-adaptive multimodal trajectory prediction using hierarchical graph neural networks X Mo, Y Xing, H Liu, C Lv IEEE Robotics and Automation Letters, 2023 | 6 | 2023 |
| Stochastic multimodal interaction prediction for urban driving X Mo, Z Huang, C Lv 2022 IEEE 25th International Conference on Intelligent Transportation …, 2022 | 5 | 2022 |
| Predictive neural motion planner for autonomous driving using graph networks X Mo, C Lv IEEE Transactions on Intelligent Vehicles 8 (2), 1983-1993, 2023 | 3 | 2023 |
| Multi-task driver steering behaviour modeling using time-series transformer Y Xing, W Li, X Mo, C Lv arXiv preprint arXiv:2207.00484, 2022 | 2 | 2022 |
| Secure State Estimation for Linear Time-varying Processes via Local Estimators L Xu, X Mo, Y Mo, X Liu 2019 Chinese Control Conference (CCC), 8876-8881, 2019 | 2 | 2019 |
| Distributed function calculation over noisy networks Z Zeng, X Yang, Z Zhang, X Mo, Z Long Mathematical Problems in Engineering 2016, 2016 | 2 | 2016 |
Chen Lv (吕辰)Associate Professor, Nanyang Technological UniversityVerified email at ntu.edu.sg
