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End-to-End Driving Via Conditional Imitation Learning
Open accessAbstract
Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands.
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“In this era, IL using real-world demonstration data has become one of the most popular and robust control algorithms in research and industry [59,60].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [60]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718
Self-citationA survey of robot learning from demonstration
Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.10.024 · Cited by 3,227
End to End Learning for Self-Driving Cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016) · arXiv · doi:10.48550/arxiv.1604.07316 · Cited by 3,198
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles
Paden, B. A., Čáp, M., Yong, S. Z., Yershov, D. S., & Frazzoli, E. (2016) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2016.2578706 · Cited by 2,724
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Ross, S., Gordon, G. J., & Bagnell, J. A. (2018) · Carnegie Mellon University · doi:10.1184/r1/6550949 · Cited by 1,941
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.312 · Cited by 1,876
Maximum Entropy Inverse Reinforcement Learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., & Dey, A. K. (2018) · doi:10.1184/r1/6555512 · Cited by 1,868
Research works printed but not linked: 1
- laskey, 2017, Dart: Optimizing noise injection in imitation learning, Conference on Robot Learning (CoRL)
Cited by
Sorted by citations
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718
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Xia, F., Zamir, A. R., He, Z., Sax, A., Malik, J., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00945 · Cited by 763
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
Bansal, M., Krizhevsky, A., & Ogale, A. S. (2019) · Robotics: Science and Systems · doi:10.15607/rss.2019.xv.031 · Cited by 743
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Xiao, Y., Codevilla, F., Gurram, A., Urfalioglu, O., & Lopez, A. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3013234 · Cited by 319
Self-citationLi, G., Yang, Y., Li, S., Qu, X., Lyu, N., & Li, S. E. (2022) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2021.103452 · Cited by 308
A review of motion planning algorithms for intelligent robots
Zhou, C., Huang, B., & Fränti, P. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01867-z · Cited by 290
Learning by CheatingPreprint
Chen, D., Zhou, B., Koltun, V., & Krähenbühl, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12294 · Cited by 256
Self-citationMP3: A Unified Model to Map, Perceive, Predict and Plan
Casas, S., Sadat, A., & Urtasun, R. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01417 · Cited by 228
Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges
Zablocki, É., Ben-Younes, H., Pérez, P., & Cord, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01657-x · Cited by 227
Wu, J., Huang, Z., Hu, Z., & Lv, C. (2023) · Engineering · doi:10.1016/j.eng.2022.05.017 · Cited by 205
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners
Hecker, S., Dai, D., & Van Gool, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_27 · Cited by 191
Urban Driving with Conditional Imitation Learning
Hawke, J., Shen, R., Gurău, C., Sharma, S., Reda, D., Nikolov, N., Mazur, P., Micklethwaite, S., Griffiths, N., Shah, A., et al. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197408 · Cited by 158
Variational End-to-End Navigation and Localization
Amini, A., Rosman, G., Karaman, S., & Rus, D. L. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793579 · Cited by 124
Navigating to objects in the real world
Gervet, T., Chintala, S., Batra, D., Malik, J., & Chaplot, D. S. (2023) · Science Robotics · doi:10.1126/scirobotics.adf6991 · Cited by 125
VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control
Zhang, J., Tai, L., Yun, P., Xiong, Y., Liu, M., Boedecker, J., & Burgard, W. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2894216 · Cited by 109
“s experiments are relateively expensive, we only compare NoGoggles and VR-Goggles in the outdoor autonomous driving scenario. We take the driving policy trained through conditional imitation learning [28] as in Section IV-B. This policy takes as inputs the first person view RGB image and a highlevel command, which fall…”
MethodLikmeta, A., Metelli, A. M., Tirinzoni, A., Giol, R., Restelli, M., & Romano, D. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103568 · Cited by 106
On Offline Evaluation of Vision-Based Driving Models
Codevilla, F., López, A. M., Koltun, V., & Dosovitskiy, A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01267-0_15 · Cited by 103
Self-citationA Survey of Deep Learning Applications to Autonomous Vehicle Control
Kuutti, S., Bowden, R., Jin, Y., Barber, P., & Fallah, S. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2962338 · Cited by 96
Colas, C., Karch, T., Sigaud, O., & Oudeyer, P. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13554 · Cited by 137
Improvisation through Physical Understanding: Using Novel Objects As Tools with Visual Foresight
Xie, A., Ebert, F., Levine, S., & Finn, C. (2019) · Robotics: Science and Systems · doi:10.15607/rss.2019.xv.001 · Cited by 88
Explaining Autonomous Driving by Learning End-to-End Visual Attention
Cultrera, L., Seidenari, L., Becattini, F., Pala, P., & Bimbo, A. D. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00178 · Cited by 71
Run-Time Monitoring of Machine Learning for Robotic Perception: A Survey of Emerging Trends
Rahman, Q. M., Corke, P., & Dayoub, F. (2021) · IEEE Access · doi:10.1109/access.2021.3055015 · Cited by 65
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to ImitationPreprint
Tai, L., Zhang, J., Liu, M., Boedecker, J., & Burgard, W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.07139 · Cited by 111
Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving
Cao, Z., Bıyık, E., Wang, W. Z., Raventos, A., Gaidon, A., Rosman, G., & Sadigh, D. (2020) · Robotics: Science and Systems · doi:10.15607/rss.2020.xvi.039 · Cited by 65
Exploring the Limitations of Behavior Cloning for Autonomous Driving
Codevilla, F., Santana, E., López, A. M., & Gaidon, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00942 · Cited by 608
Self-citationWang, X., Qi, X., Wang, P., & Yang, J. (2021) · Autonomous Intelligent Systems · doi:10.1007/s43684-021-00015-x · Cited by 55
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints
Shah, D., & Levine, S. (2022) · Robotics: Science and Systems · doi:10.15607/rss.2022.xviii.019 · Cited by 54
Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles
Manderson, T., Higuera, J. C. G., Wapnick, S., Tremblay, J.-F., Shkurti, F., Meger, D., & Dudek, G. (2020) · Robotics: Science and Systems · doi:10.15607/rss.2020.xvi.048 · Cited by 50
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research
Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.08710 · Cited by 48
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator
Li, D., & Okhrin, O. (2023) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2022.103987 · Cited by 51
de Morais, G. A. P., Marcos, L. B., Bueno, J. N. A. D., de Resende, N. F., Terra, M. H., & Grassi, V. (2020) · Control Engineering Practice · doi:10.1016/j.conengprac.2020.104630 · Cited by 47
Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State
Cosner, R. K., Singletary, A., Taylor, A. J., Molnár, T. G., Bouman, K. L., & Ames, A. D. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636584 · Cited by 46
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Kim, J., Moon, S., Rohrbach, A., Darrell, T., & Canny, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00968 · Cited by 45
CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-Based Autonomous Urban Driving
Zhao, Y., Wu, K., Xu, Z., Che, Z., Lu, Q., Tang, J., & Liu, C. H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20259 · Cited by 47
VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation
Karnan, H., Warnell, G., Xiao, X., & Stone, P. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812316 · Cited by 42
Zhou, J., Wang, R., Liu, X., Jiang, Y., Jiang, S., Tao, J., Miao, J., & Song, S. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636795 · Cited by 40
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Khoury, M., & Hadfield-Menell, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00525 · Cited by 79
On failures of RGB cameras and their effects in autonomous driving applications
Secci, F., & Ceccarelli, A. (2020) · 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) · doi:10.1109/issre5003.2020.00011 · Cited by 42
A deep learning gated architecture for UGV navigation robust to sensor failures
Patel, N., Choromanska, A., Krishnamurthy, P., & Khorrami, F. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2019.03.001 · Cited by 36
Autonomous Vehicle Control: End-to-End Learning in Simulated Urban Environments
Haavaldsen, H., Aasboe, M., & Lindseth, F. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-35664-4_4 · Cited by 36
Jha, S., Tsai, T. K., Hari, S. K. S., Sullivan, M. B., Kalbarczyk, Z., Keckler, S. W., & Iyer, R. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.01024 · Cited by 34
Li, Z., Motoyoshi, T., Sasaki, K., Ogata, T., & Sugano, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.11100 · Cited by 33
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Toromanoff, M., Wirbel, É., Wilhelm, F., Vejarano, C., Perrotton, X., & Moutarde, F. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594090 · Cited by 36
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
Hoque, R., Balakrishna, A., Novoseller, E., Wilcox, A., Brown, D. S., & Goldberg, K. (2021) · arXiv · doi:10.48550/arxiv.2109.08273 · Cited by 31
OpenBot: Turning Smartphones into Robots
Muller, M., & Koltun, V. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561788 · Cited by 36
Self-citationSoftware-Defined Active LiDARs for Autonomous Driving: A Parallel Intelligence-Based Adaptive Model
Liu, Y., Sun, B., Tian, Y., Wang, X., Zhu, Y., Huai, R., & Shen, Y. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3289540 · Cited by 30
Onishi, T., Motoyoshi, T., Suga, Y., Hiroki, M. O. R. I., & Ogata, T. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852322 · Cited by 24
Toward explainable and advisable model for self‐driving cars
Kim, J., Rohrbach, A., Akata, Z., Moon, S., Misu, T., Chen, Y., Darrell, T., & Canny, J. (2021) · Applied AI Letters · doi:10.1002/ail2.56 · Cited by 24
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
Pulver, H., Eiras, F., Carozza, L., Hawasly, M., Albrecht, S. V., & Ramamoorthy, S. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636862 · Cited by 24
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Capasso, A. P., Maramotti, P., Dell’Eva, A., & Broggi, A. (2021) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv48863.2021.9575135 · Cited by 23
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., & Díaz-Rodríguez, N. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.12.004 · Cited by 593
A novel reservoir simulation model based on physics informed neural networks
Liu, A. C. Y., Li, J., Bi, J., Chen, Z., Wang, Y., Lu, C., Jin, Y., & Lin, B. (2024) · Physics of Fluids · doi:10.1063/5.0239376 · Cited by 27
Learning to Plan with Logical Automata
Arak, B., Vodrahalli, K., Leech, T., Vasile, C.-I., Donahue, M., & Rus, D. L. (2019) · Robotics: Science and Systems · doi:10.15607/rss.2019.xv.064 · Cited by 22
Simple and Effective Synthesis of Indoor 3D Scenes
Koh, J. Y., Agrawal, H., Batra, D., Tucker, R. P., Waters, A., Lee, H., Yang, Y., Baldridge, J., & Anderson, P. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25199 · Cited by 31
PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous drivingPreprint
Buhet, T., Wirbel, É., Bursuc, A., & Perrotton, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.08744 · Cited by 24
Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 27
“In this era, IL using real-world demonstration data has become one of the most popular and robust control algorithms in research and industry [59,60].”
MethodSADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications
Hamdi, A., Mueller, M., & Ghanem, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6722 · Cited by 20
Wang, Y., Zheng, K., Tian, D., Duan, X., & Zhou, J. (2021) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1900637 · Cited by 20
Driving Through Ghosts: Behavioral Cloning with False Positives
Bühler, A., Gaidon, A., Cramariuc, A., Ambruş, R., Rosman, G., & Burgard, W. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340639 · Cited by 18
Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations
Mandlekar, A., Xu, D., Martín-Martín, R., Savarese, S., & Fei-Fei, L. (2020) · arXiv · doi:10.48550/arxiv.2003.06085 · Cited by 17
Chen, J., Wu, T., Shi, M., & Jiang, W. (2020) · Sensors · doi:10.3390/s20195626 · Cited by 16
Uncertainties in Neural Networks : A System Identification Approach
Malmström, M. (2021) · Linköping studies in science and technology. Thesis · doi:10.3384/lic.diva-174720 · Cited by 16
Multi-Task End-to-End Self-Driving Architecture for CAV Platoons
Huch, S., Öngel, A., Betz, J., & Lienkamp, M. (2021) · Sensors · doi:10.3390/s21041039 · Cited by 15
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Brito, B., Agarwal, A., & Alonso–Mora, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.04538 · Cited by 15
Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests
Meyer, M., Sauter, L., Granrath, C., Hadj-amor, H., & Andert, J. (2021) · Automotive Innovation · doi:10.1007/s42154-021-00161-1 · Cited by 14
Adversarial Training with Voronoi ConstraintsPreprint
Khoury, M., & Hadfield-Menell, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01019 · Cited by 20
RLfOLD: Reinforcement Learning from Online Demonstrations in Urban Autonomous Driving
Coelho, D., Oliveira, M., & Santos, V. M. F. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.29049 · Cited by 13
Wang, S., Fujii, H., & Yoshimura, S. (2022) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2022.128172 · Cited by 13
Deep 3D Dynamic Object Detection towards Successful and Safe Navigation for Full Autonomous Driving
Wijesekara, P. A. D. S. N. (2022) · The Open Transportation Journal · doi:10.2174/18744478-v16-e2208191 · Cited by 16
CCIL: Context-conditioned imitation learning for urban driving
Guo, K., Jing, W., Chen, J., & Pan, J. (2023) · Robotics: Science and Systems · doi:10.15607/rss.2023.xix.101 · Cited by 17
ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles
Ginerică, C., Zaha, M. V., Gogianu, F., Buşoniu, L., Trăsnea, B., & Grigorescu, S. M. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3096157 · Cited by 13
Robotics: Science and Systems XV
(2019) · doi:10.15607/rss.2019.xv · Cited by 13
Learning Driving Models with a Surround-View Camera System and a Route Planner.Preprint
Hecker, S., Dai, D., & Gool, L. V. (2018) · arXiv (Cornell University) · Cited by 12
Reinforced Intelligence Through Active Interaction in Real World: A Survey on Embodied AI
Kwon, W., Baek, S., Baek, J., Shin, W., Gwak, M., Park, P., & Lee, S. (2025) · International Journal of Control Automation and Systems · doi:10.1007/s12555-025-0127-1 · Cited by 13
Simitate: A Hybrid Imitation Learning Benchmark
Memmesheimer, R., Kramer, I., Seib, V., & Paulus, D. W. R. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968029 · Cited by 11
Deep Reinforcement Learning for a Self-Driving Vehicle Operating Solely on Visual Information
Audinys, R., Šlikas, Ž., Radkevičius, J., Šutas, M., & Ostreika, A. (2025) · Electronics · doi:10.3390/electronics14050825 · Cited by 12
End-to-end driving simulation via angle branched networkPreprint
Wang, Q., Chen, L. F., & Tian, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.07545 · Cited by 11
Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement LearningPreprint
Cai, P., Wang, S., Wang, H., & Liu, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.08473 · Cited by 10
EARLY FUSION for Goal Directed Robotic Vision
Walsman, A., Bisk, Y., Gabriel, S., Misra, D. K., Artzi, Y., Choi, Y., & Fox, D. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968165 · Cited by 9
Human-in-the-Loop Learning Methods Toward Safe DL-Based Autonomous Systems: A Review
Rajendran, P. T., Espinoza, H., Delaborde, A., & Mraidha, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-83906-2_20 · Cited by 10
Multi-Agent Advisor Q-Learning
Subramanian, S. G., Taylor, M. E., Larson, K. S., & Crowley, M. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13445 · Cited by 9
Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments
Dashora, N., Shin, D., Shah, D., Leopold, H. A., Fan, D. D., Agha–mohammadi, A., Rhinehart, N., & Levine, S. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811540 · Cited by 8
A review of motion planning algorithms for intelligent roboticsPreprint
Zhou, C., Huang, B., & Fränti, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.02376 · Cited by 8
Practical Self-Driving Cars: Survey of the State-of-the-ArtPreprint
Saha, D., & De, S. (2022) · Preprints.org · doi:10.20944/preprints202202.0123.v1 · Cited by 8
Human-guided reinforcement learning: methodology and application to autonomous driving
Wu, J. (2023) · doi:10.32657/10356/169780 · Cited by 8
DeepGoal: Learning to drive with driving intention from human control demonstration
Ma, H., Wang, Y., Xiong, R., Kodagoda, S., & Tang, L. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103477 · Cited by 7
Arizala, A., Lattarulo, R. A., Zubizarreta, A., & Perez, J. (2021) · International Conference on System Theory, Control and Computing (ICSTCC) · doi:10.1109/icstcc52150.2021.9607221 · Cited by 7
Learning from Interventions Using Hierarchical Policies for Safe Learning
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Araki, B., Vodrahalli, K., Leech, T., Vasile, C.-I., Donahue, M., & Rus, D. L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i06.6559 · Cited by 6
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MultiNet: Multi-Modal Multi-Task Learning for Autonomous DrivingPreprint
Chowdhuri, S., Pankaj, T., & Zipser, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.05581 · Cited by 9
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Xiao, Y., Codevilla, F., Pal, C., & López, A. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.09417 · Cited by 6
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Hornauer, S., Zipser, K., & Yu, S. X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11021-5_33 · Cited by 5
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Kassem, N. (2023) · MSA Engineering Journal · doi:10.21608/msaeng.2023.291918 · Cited by 5
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Kuutti, S., Fallah, S., & Bowden, R. (2021) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc48978.2021.9564579 · Cited by 5
Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
Nwadiuto, J., Okuda, H., & Suzuki, T. (2021) · Applied Sciences · doi:10.3390/app11114938 · Cited by 5
Autonomous UAV Navigation with Domain Adaptation.Preprint
Yoo, J., Hong, Y., & Yoon, S. (2017) · arXiv (Cornell University) · Cited by 5
Imitative Planning using Conditional Normalizing FlowPreprint
Agarwal, S., Sikchi, H., Gulino, C., Wilkinson, E., & Gautam, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.16162 · Cited by 5
Mei, X., Sun, Y., Chen, Y., Liu, C., & Liu, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.00675 · Cited by 5
Arnéz, F., Espinoza, H., Radermacher, A., & Terrier, F. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-83906-2_17 · Cited by 4
Metadata
- Authors
- Codevilla, F., Muller, M., Lopez, A., Koltun, V., & Dosovitskiy, A.
- Affiliations
- Universitat Autònoma de Barcelona (ES)
- King Abdullah University of Science and Technology (SA)
- Intel (United States) (US)
- Venue
- 2018 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 4693-4700
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2760878839
- Cited by
- 129
- References
- 41
- Citations by year
- 2025: 3 · 2024: 9 · 2023: 11 · 2022: 18 · 2021: 29 · 2020: 22 · 2019: 23 · 2018: 10
- FWCI
- 21.53
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Centres de Recerca de Catalunya
- Generalitat de Catalunya
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- end-to-end driving, vision-based driving, autonomous vehicles, sensorimotor coordination
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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