End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Robust automated driving in urban environments has not been achieved yet.
“However, robust automated driving in urban environments has not been achieved yet [6].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [6]
The possibility of jackknifing increases the difficulty of the tractor-trailer automated driving task.
“The possibility of jackknifing, the collision of the truck and the trailer with each other, increases the difficulty of the task [108].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [108]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · see citing works
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913 · see citing works
Cognitive maps in rats and men
Tolman, E. C. (1948) · Psychological Review · doi:10.1037/h0061626 · Cited by 6,641 · see citing works
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228 · see citing works
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 · see citing works
Multi-view 3D Object Detection Network for Autonomous Driving
Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.691 · Cited by 3,450 · see citing works
OpenStreetMap: User-Generated Street Maps
Haklay, M., & Weber, P. (2008) · IEEE Pervasive Computing · doi:10.1109/mprv.2008.80 · Cited by 3,208 · see citing works
Advances in Neural Information Processing Systems 27 (NIPS 2014)
Zhong, M., Goddard, N., & Sutton, C. A. (2014) · Cited by 2,440 · see citing works
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Shah, S., Dey, D., Lovett, C., & Kapoor, A. (2018) · Springer Proceedings in Advanced Robotics · doi:10.1007/978-3-319-67361-5_40 · Cited by 2,254 · see citing works
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 · see citing works
Domain Adaptive Faster R-CNN for Object Detection in the Wild
Chen, Y., Li, W., Sakaridis, C., Dai, D., & Van Gool, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00352 · Cited by 1,672 · see citing works
Self-citation1 year, 1000 km: The Oxford RobotCar dataset
Maddern, W., Pascoe, G., Linegar, C., & Newman, P. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364916679498 · Cited by 1,632 · see citing works
Autonomous driving in urban environments: Boss and the Urban Challenge
Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M. N., Dolan, J., Duggins, D., Galatali, T., Geyer, C., et al. (2008) · Journal of Field Robotics · doi:10.1002/rob.20255 · Cited by 1,555 · see citing works
Towards fully autonomous driving: Systems and algorithms
Levinson, J., Askeland, J., Becker, J., Dolson, J., Held, D., Kammel, S., Kolter, J. Z., Langer, D., Pink, O., Pratt, V., et al. (2011) · 2011 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2011.5940562 · Cited by 1,374 · see citing works
Semantic Foggy Scene Understanding with Synthetic Data
Sakaridis, C., Dai, D., & Gool, L. V. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1072-8 · Cited by 1,354 · see citing works
Self-citationKnowing Where and Getting There: A Human Navigation Network
Maguire, E. A., Burgess, N., Donnett, J. G., Frackowiak, R. S. J., Frith, C. D., & O'Keefe, J. (1998) · Science · doi:10.1126/science.280.5365.921 · Cited by 1,303 · see citing works
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., & Darrell, T. (2014) · doi:10.21236/ada623249 · Cited by 1,081 · see citing works
Deep Reinforcement Learning framework for Autonomous Driving
Sallab, A. E., Abdou, M., Perot, E., & Yogamani, S. (2017) · Electronic Imaging · doi:10.2352/issn.2470-1173.2017.19.avm-023 · Cited by 1,045 · see citing works
Sivaraman, S., & Trivedi, M. M. (2013) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2013.2266661 · Cited by 911 · see citing works
End-to-End Learning of Driving Models from Large-Scale Video Datasets
Xu, H., Gao, Y., Yu, F., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.376 · Cited by 884 · see citing works
Driving with knowledge from the physical world
Yuan, J. Y., Zheng, Y., Xie, X., & Sun, G. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020462 · Cited by 870 · see citing works
Map-matching for low-sampling-rate GPS trajectories
Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., & Huang, Y. (2009) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1653771.1653820 · Cited by 817 · see citing works
Recent progress in road and lane detection: A survey
Bar Hillel, A., Lerner, R., Levi, D., & Raz, G. (2014) · Machine Vision and Applications · doi:10.1007/s00138-011-0404-2 · Cited by 775 · see citing works
(2018) · arXiv (Cornell University) · Cited by 679 · see citing works
Perception, Planning, Control, and Coordination for Autonomous Vehicles
Pendleton, S., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y., Rus, D., & Ang, M. (2017) · Machines · doi:10.3390/machines5010006 · Cited by 657 · see citing works
Autonomous Vehicle Technology: A Guide for Policymakers
Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C., & Oluwatola, O. (2016) · doi:10.7249/rr443-2 · Cited by 653 · see citing works
Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars
Maqueda, A. I., Loquercio, A., Gallego, G., Garcia, N., & Scaramuzza, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00568 · Cited by 624 · see citing works
Route Planning in Transportation Networks
Bast, H., Delling, D., Goldberg, A., Müller-Hannemann, M., Pajor, T., Sanders, P., Wagner, D., & Werneck, R. F. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-49487-6_2 · Cited by 610 · see citing works
Autonomous Vehicle Technology: A Guide for Policymakers
Anderson, J. M., Kalra, N., Stanley, K. D., Sorensen, P. A., Samaras, C., & Oluwatola, O. A. (2014) · RAND Corporation eBooks · doi:10.7249/rr443 · Cited by 590 · see citing works
An Empirical Evaluation of Deep Learning on Highway Driving
Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., Andriluka, M., Rajpurkar, P., Migimatsu, T., Cheng-Yue, R., et al. (2015) · arXiv · doi:10.48550/arxiv.1504.01716 · Cited by 537 · see citing works
Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars
Maqueda, A. I., Loquercio, A., Gallego, G., Garcia, N. N., & Scaramuzza, D. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-175994 · Cited by 493 · see citing works
Real-time routing with OpenStreetMap data
Luxen, D., & Vetter, C. (2011) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2093973.2094062 · Cited by 484 · see citing works
(2005) · neural information processing systems · Cited by 446 · see citing works
Geometry-Aware Learning of Maps for Camera Localization
Brahmbhatt, S., Gu, J., Kim, K., Hays, J. H., & Kautz, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00277 · Cited by 421 · see citing works
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a CarPreprint
Bojarski, M., Yeres, P., Choromanska, A., Choromański, K., Firner, B., Jackel, L. D., & Müller, U. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.07911 · Cited by 414 · see citing works
3D Traffic Scene Understanding From Movable Platforms
Geiger, A., Lauer, M., Wojek, C., Stiller, C., & Urtasun, R. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.185 · Cited by 397 · see citing works
Fast Optical Flow Using Dense Inverse Search
Kroeger, T., Timofte, R., Dai, D., & Gool, L. V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_29 · Cited by 375 · see citing works
Self-citationAutonomous Ground Vehicles—Concepts and a Path to the Future
Luettel, T., Himmelsbach, M., & Wuensche, H. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2189803 · Cited by 339 · see citing works
An Interactive-Voting Based Map Matching Algorithm
Yuan, J., Zheng, Y., Zhang, C., Xie, X., & Sun, G. (2010) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2010.14 · Cited by 326 · see citing works
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
Jain, A., Koppula, H. S., Raghavan, B., Soh, S., & Saxena, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.364 · Cited by 274 · see citing works
Lanelets: Efficient map representation for autonomous driving
Bender, P., Ziegler, J., & Stiller, C. (2014) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2014.6856487 · Cited by 266 · see citing works
Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives
Jensen, M. B., Philipsen, M. P., Møgelmose, A., et al. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2509509 · Cited by 252 · see citing works
Fast LIDAR-based road detection using fully convolutional neural networks
Caltagirone, L., Scheidegger, S., Svensson, L., & Wahde, M. (2017) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2017.7995848 · Cited by 246 · see citing works
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning
McAllister, R., Gal, Y., Kendall, A., van der Wilk, M., Shah, A., Cipolla, R., & Weller, A. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/661 · Cited by 245 · see citing works
Looking at Humans in the Age of Self-Driving and Highly Automated Vehicles
Ohn-Bar, E., & Trivedi, M. M. (2016) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2016.2571067 · Cited by 212 · see citing works
Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers
Kasper, D., Weidl, G., Dang, T., Breuel, G., Tamke, A., Wedel, A., & Rosenstiel, W. (2012) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2012.2203229 · Cited by 188 · see citing works
Map-matching algorithm for large-scale low-frequency floating car data
Chen, B. Y., Yuan, H., Li, Q., et al. (2013) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2013.816427 · Cited by 174 · see citing works
Automated driving: The role of forecasts and uncertainty—A control perspective
Carvalho, A., Lefèvre, S., Schildbach, G., Kong, J., & Borrelli, F. (2015) · European Journal of Control · doi:10.1016/j.ejcon.2015.04.007 · Cited by 158 · see citing works
TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Crowdsourced Digital Footprints
Chen, C., Zhang, D., Guo, B., et al. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2357835 · Cited by 145 · see citing works
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning
McAllister, R., Gal, Y., Kendall, A., van der Wilk, M., Shah, A., Cipolla, R., & Weller, A. (2017) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.12760 · Cited by 143 · see citing works
Autonomous robot navigation based on OpenStreetMap geodata
Hentschel, M., & Wagner, B. (2010) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2010.5625092 · Cited by 131 · see citing works
LiDAR-Video Driving Dataset: Learning Driving Policies Effectively
Chen, Y., Wang, J., Li, J., Lu, C., Luo, Z., Xue, H., & Wang, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00615 · Cited by 131 · see citing works
End-to-End Driving Via Conditional Imitation Learning
Codevilla, F., Muller, M., Lopez, A., Koltun, V., & Dosovitskiy, A. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460487 · Cited by 129 · see citing works
Surround vehicles trajectory analysis with recurrent neural networks
Khosroshahi, A., Ohn-Bar, E., & Trivedi, M. M. (2016) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795922 · Cited by 124 · see citing works
Semiautonomous Vehicular Control Using Driver Modeling
Shia, V. A., Gao, Y., Vasudevan, R., Campbell, K. D., Lin, T., Borrelli, F., & Bajcsy, R. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2325776 · Cited by 114 · see citing works
Combining neural networks and tree search for task and motion planning in challenging environments
Paxton, C., Raman, V., Hager, G. D., & Kobilarov, M. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206505 · Cited by 113 · see citing works
Bird’s-Eye View Vision System for Vehicle Surrounding Monitoring
Liu, J. Y.-C., Lin, K. K., & Chen, Y. (2008) · Springer eBooks · doi:10.1007/978-3-540-78157-8_16 · Cited by 111 · see citing works
Mapping and localization using GPS, lane markings and proprioceptive sensors
Tao, Z., Bonnifait, P., Frémont, V., & Ibanez-Guzman, J. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696383 · Cited by 99 · see citing works
Formalising Traffic Rules for Accountability of Autonomous Vehicles
Rizaldi, A., & Althoff, M. (2015) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2015.269 · Cited by 93 · see citing works
Toward personalized, context-aware routing
Yang, B., Guo, C., Ma, Y., et al. (2015) · The VLDB Journal · doi:10.1007/s00778-015-0378-1 · Cited by 90 · see citing works
Nedevschi, S., Popescu, V., Danescu, R. G., Mariţa, T., & Oniga, F. (2013) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2012.2228191 · Cited by 84 · see citing works
High-accurate vehicle localization using digital maps and coherency images
Mattern, N., Schubert, R., & Wanielik, G. (2010) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5547974 · Cited by 78 · see citing works
High-precision lane-level road map building for vehicle navigation
Chen, A., Ramanandan, A., & Farrell, J. A. (2010) · IEEE/ION Position Location and Navigation Symposium · doi:10.1109/plans.2010.5507331 · Cited by 68 · see citing works
Failure Prediction for Autonomous Driving
Hecker, S., Dai, D., & Gool, L. V. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500495 · Cited by 66 · see citing works
Self-citation360° Detection and tracking algorithm of both pedestrian and vehicle using fisheye images
Bertozzi, M., Castangia, L., Cattani, S., et al. (2015) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2015.7225675 · Cited by 65 · see citing works
PathTrack: Fast Trajectory Annotation with Path Supervision
Manén, S., Gygli, M., Dai, D., & Gool, L. V. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.40 · Cited by 64 · see citing works
Self-citationLIDAR-based driving path generation using fully convolutional neural networks
Caltagirone, L., Bellone, M., Svensson, L., & Wahde, M. (2017) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317618 · Cited by 63 · see citing works
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime
Dai, D., & Van Gool, L. (2018) · ETH Zurich · doi:10.3929/ethz-b-000310974 · Cited by 60 · see citing works
Self-citationZheng, Y.-T., Yan, S., Zha, Z.-J., et al. (2013) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2422956.2422959 · Cited by 57 · see citing works
3-D Surround View for Advanced Driver Assistance Systems
Gao, Y. D., Lin, C., Zhao, Y., et al. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2750087 · Cited by 52 · see citing works
Estimation of Driver Reaction Time from Car-Following Data
Ma, X., & Andreasson, I. J. (2006) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198106196500114 · Cited by 49 · see citing works
Trajectories and Maneuvers of Surrounding Vehicles With Panoramic Camera Arrays
Dueholm, J. V., Kristoffersen, M. S., Satzoda, R. K., Moeslund, T. B., & Trivedi, M. M. (2016) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2016.2622921 · Cited by 46 · see citing works
Generation of high precision digital maps using circular arc splines
Schindler, A., Maier, G., & Janda, F. (2012) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2012.6232124 · Cited by 39 · see citing works
Ma, X., & Andreasson, I. J. (2006) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1965-14 · Cited by 39 · see citing works
Lane detection system with around view monitoring for intelligent vehicle
Kum, C.-H., Cho, D.-C., Ra, M., & Kim, W.-Y. (2013) · International SoC Design Conference · doi:10.1109/isocc.2013.6864011 · Cited by 32 · see citing works
MapNet: Geometry-Aware Learning of Maps for Camera Localization
Brahmbhatt, S., Gu, J., Kim, K., et al. (2017) · Cited by 17 · see citing works
A visual parking guidance for surround view monitoring system
Yu, M., & Ma, G. (2015) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2015.7225662 · Cited by 12 · see citing works
Multi-perspective vehicle detection and tracking: Challenges, dataset, and metrics
Dueholm, J. V., Kristoffersen, M. S., Satzoda, R. K., Ohn-Bar, E., Moeslund, T. B., & Trivedi, M. M. (2016) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795671 · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889 · see citing works
“However, robust automated driving in urban environments has not been achieved yet [14].”
BackgroundA survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020) · Journal of Field Robotics · doi:10.1002/rob.21918 · Cited by 1,809 · see citing works
Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I., & Lungren, M. P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00341-z · Cited by 934 · see citing works
“While selecting features known to be associated with disease is meaningful, future work may further bene fi t from utilizing large volumes of feature-rich data, as seen in fi elds outside medicine such as autonomous driving 44,45 .”
BackgroundMethodArtificial intelligence-based methods for fusion of electronic health records and imaging data
Mohsen, F., Ali, H., El Hajj, N., & Shah, Z. (2022) · Scientific Reports · doi:10.1038/s41598-022-22514-4 · Cited by 250 · see citing works
A 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 · see citing works
Advisable Learning for Self-Driving Vehicles by Internalizing Observation-to-Action Rules
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 · see citing works
- Authors
- Hecker, S., Dai, D., & Van Gool, L.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Hecker, Simon
- ORCID
- Dai, Dengxin
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 449-468
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2887286974
- Cited by
- 191
- References
- 82
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 21 · 2023: 16 · 2022: 27 · 2021: 36 · 2020: 39 · 2019: 33 · 2018: 2
- FWCI
- 28.20
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- arXiv (Cornell University)
- ETH Zürich Research Collection
- Funders
- Nvidia
- Toyota Motor Europe
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
- Keywords
- driving model, surround-view cameras, route planning, autonomous driving, driving data, steering angle prediction, OpenStreetMap, CAN bus, urban driving, intersection scenarios
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 ›
Are you an author, or do you see a problem with this page? Request a review