| Combining planning and deep reinforcement learning in tactical decision making for autonomous driving CJ Hoel, K Driggs-Campbell, K Wolff, L Laine, MJ Kochenderfer IEEE transactions on intelligent vehicles 5 (2), 294-305, 2019 | 153 | 2019 |
| Semiautonomous vehicular control using driver modeling VA Shia, Y Gao, R Vasudevan, KD Campbell, T Lin, F Borrelli, R Bajcsy IEEE Transactions on Intelligent Transportation Systems 15 (6), 2696-2709, 2014 | 137 | 2014 |
| Hg-dagger: Interactive imitation learning with human experts M Kelly, C Sidrane, K Driggs-Campbell, MJ Kochenderfer 2019 International Conference on Robotics and Automation (ICRA), 8077-8083, 2019 | 114 | 2019 |
| Data-driven probabilistic modeling and verification of human driver behavior D Sadigh, K Driggs-Campbell, A Puggelli, W Li, V Shia, R Bajcsy, ... AAAI Spring Symposium-Technical Report, 56-61, 2014 | 102 | 2014 |
| Adaptive stress testing with reward augmentation for autonomous vehicle validation A Corso, P Du, K Driggs-Campbell, MJ Kochenderfer 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 163-168, 2019 | 91 | 2019 |
| Ensembledagger: A bayesian approach to safe imitation learning K Menda, K Driggs-Campbell, MJ Kochenderfer 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 86* | 2019 |
| A wireless hybrid chemical sensor for detection of environmental volatile organic compounds C Chen, F Tsow, KD Campbell, R Iglesias, E Forzani, N Tao IEEE sensors journal 13 (5), 1748-1755, 2013 | 80 | 2013 |
| A new sensor for the assessment of personal exposure to volatile organic compounds C Chen, KD Campbell, I Negi, RA Iglesias, P Owens, N Tao, F Tsow, ... Atmospheric environment 54, 679-687, 2012 | 60 | 2012 |
| Integrating intuitive driver models in autonomous planning for interactive maneuvers K Driggs-Campbell, V Govindarajan, R Bajcsy IEEE Transactions on Intelligent Transportation Systems 18 (12), 3461-3472, 2017 | 59 | 2017 |
| Towards trustworthy automation: User interfaces that convey internal and external awareness T Rezvani, K Driggs-Campbell, D Sadigh, SS Sastry, SA Seshia, R Bajcsy 2016 IEEE 19th International conference on intelligent transportation …, 2016 | 57 | 2016 |
| Simulating emergent properties of human driving behavior using multi-agent reward augmented imitation learning RP Bhattacharyya, DJ Phillips, C Liu, JK Gupta, K Driggs-Campbell, ... 2019 International Conference on Robotics and Automation (ICRA), 789-795, 2019 | 49 | 2019 |
| Improved robustness and safety for autonomous vehicle control with adversarial reinforcement learning X Ma, K Driggs-Campbell, MJ Kochenderfer 2018 IEEE Intelligent Vehicles Symposium (IV), 1665-1671, 2018 | 49 | 2018 |
| Improved driver modeling for human-in-the-loop vehicular control K Driggs-Campbell, V Shia, R Bajcsy 2015 IEEE International Conference on Robotics and Automation (ICRA), 1654-1661, 2015 | 47 | 2015 |
| A taxonomy and review of algorithms for modeling and predicting human driver behavior K Brown, K Driggs-Campbell, MJ Kochenderfer arXiv preprint arXiv:2006.08832, 2020 | 44 | 2020 |
| Robust, informative human-in-the-loop predictions via empirical reachable sets K Driggs-Campbell, R Dong, R Bajcsy IEEE Transactions on Intelligent Vehicles 3 (3), 300-309, 2018 | 37 | 2018 |
| Identifying modes of intent from driver behaviors in dynamic environments K Driggs-Campbell, R Bajcsy 2015 IEEE 18th International Conference on Intelligent Transportation …, 2015 | 37 | 2015 |
| Decentralized structural-rnn for robot crowd navigation with deep reinforcement learning S Liu, P Chang, W Liang, N Chakraborty, K Driggs-Campbell 2021 IEEE International Conference on Robotics and Automation (ICRA), 3517-3524, 2021 | 30 | 2021 |
| Improving human-in-the-loop decision making in multi-mode driver assistance systems using hidden mode stochastic hybrid systems CP Lam, AY Yang, K Driggs-Campbell, R Bajcsy, SS Sastry 2015 IEEE/RSJ international conference on intelligent robots and systems …, 2015 | 27 | 2015 |
| Dynamic environment prediction in urban scenes using recurrent representation learning M Itkina, K Driggs-Campbell, MJ Kochenderfer 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2052-2059, 2019 | 26 | 2019 |
| Long-term pedestrian trajectory prediction using mutable intention filter and warp LSTM Z Huang, A Hasan, K Shin, R Li, K Driggs-Campbell IEEE Robotics and Automation Letters 6 (2), 542-549, 2020 | 25* | 2020 |
Mykel J. KochenderferAssociate Professor, Stanford Universitystanford.edu의 이메일 확인됨
