| Finite-time H∞ control of switched systems with mode-dependent average dwell time S Shi, Z Fei, J Li Journal of the Franklin Institute 353 (1), 221-234, 2016 | 37 | 2016 |
| Conditional Generative Neural System for Probabilistic Trajectory Prediction J Li, H Ma, M Tomizuka IEEE/RSJ International Conference on Robotics and Systems (IROS), 2019 | 33 | 2019 |
| Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning J Li, H Ma, M Tomizuka 2019 IEEE International Conference on Robotics and Automation (ICRA), 2019 | 28 | 2019 |
| Towards a Fatality-Aware Benchmark of Probabilistic Reaction Prediction in Highly Interactive Driving Scenarios W Zhan, L Sun, Y Hu, J Li, M Tomizuka 2018 IEEE Intelligent Transportation Systems Conference (ITSC), 2018 | 22 | 2018 |
| Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real J Li, H Ma, W Zhan, M Tomizuka 2018 IEEE Intelligent Transportation Systems Conference (ITSC), 2018 | 22 | 2018 |
| Recent advances on the reduction of CO2 to important C2+ oxygenated chemicals and fuels J Li, L Wang, Y Cao, C Zhang, P He, H Li Chinese Journal of Chemical Engineering 26 (11), 2266-2279, 2018 | 20 | 2018 |
| Generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter J Li, W Zhan, M Tomizuka 2018 IEEE Intelligent Vehicles Symposium (IV), 936-942, 2018 | 20 | 2018 |
| Social-wagdat: Interaction-aware trajectory prediction via wasserstein graph double-attention network J Li, H Ma, Z Zhang, M Tomizuka arXiv preprint arXiv:2002.06241, 2020 | 19 | 2020 |
| Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving J Li, W Zhan, Y Hu, M Tomizuka IEEE Transactions on Intelligent Transportation Systems, 2019 | 18 | 2019 |
| EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning J Li, F Yang, M Tomizuka, C Choi Advances in Neural Information Processing Systems (NeurIPS) 2020, 2020 | 17* | 2020 |
| Wasserstein Generative Learning with Kinematic Constraints for Probabilistic Interactive Driving Behavior Prediction H Ma, J Li, W Zhan, M Tomizuka IEEE Intelligent Vehicles Symposium (IV), 2019 | 16 | 2019 |
| A novel variable selection approach for redundant information elimination purpose of process control J Li, C Duan, Z Fei IEEE Transactions on Industrial Electronics 63 (3), 1737-1744, 2015 | 12 | 2015 |
| Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling J Li, H Ma, W Zhan, M Tomizuka IEEE Intelligent Vehicles Symposium (IV), 2019 | 11 | 2019 |
| Safe and feasible motion generation for autonomous driving via constrained policy net W Zhan, J Li, Y Hu, M Tomizuka Industrial Electronics Society, IECON 2017-43rd Annual Conference of the …, 2017 | 11 | 2017 |
| Shared Cross-Modal Trajectory Prediction for Autonomous Driving C Choi, JH Choi, J Li, S Malla | 6 | 2020 |
| Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships X Ma, J Li, MJ Kochenderfer, D Isele, K Fujimura arXiv preprint arXiv:2011.04251, 2020 | 2 | 2020 |
| A variable selection aided residual generator design approach for process control and monitoring C Duan, Z Fei, J Li Neurocomputing 171, 1013-1020, 2016 | 2 | 2016 |
| Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking J Li, H Ma, Z Zhang, J Li, M Tomizuka arXiv preprint arXiv:2102.09117, 2021 | | 2021 |
Masayoshi TomizukaMechaniccal Engineering, University of CaliforniaBestätigte E-Mail-Adresse bei me.berkeley.edu
