Stanley: The robot that won the DARPA Grand Challenge
Open accessAbstract
This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high‐speed desert driving without manual intervention. The robot's software system relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning. This paper describes the major components of this architecture, and discusses the results of the Grand Challenge race. © 2006 Wiley Periodicals, Inc.
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3D-LiDAR has only been used in automobiles for slightly over ten years, but it has already been shown to be crucial to development of AVs and ADS.
“Even though 3D-LiDAR has only been used in automobiles for slightly over ten years, it has already been shown to be crucial to the development of AVs and ADS [36].”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [36]
Section 5.1, Terrain Labeling (page unavailable), says Stanley used roof-mounted laser scanners to generate 3D point clouds and relied on them for short- and medium-range obstacle avoidance. This supports an important role for laser sensing in one autonomous vehicle, but the paper does not establish when 3D-LiDAR first entered automobile use or that it is crucial to AV and ADS development generally. It describes five single-scan range finders, not a dedicated 3D-LiDAR unit. (LLM, conf=0.86)
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Cites
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759 · see citing works
Pattern classification and scene analysis
Duda, R. O., & Hart, P. E. (1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895 · see citing works
New extension of the Kalman filter to nonlinear systems
Julier, S., & Uhlmann, J. K. (1997) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.280797 · Cited by 5,377 · see citing works
Fundamentals of Vehicle Dynamics
Gillespie, T. D. (1992) · SAE International eBooks · doi:10.4271/r-114 · Cited by 3,173 · see citing works
Fundamentals of Vehicle Dynamics
Gillespie, T. (2021) · SAE International eBooks · doi:10.4271/9781468601770 · Cited by 2,521 · see citing works
Pattern Classification and Scene Analysis.
Grenander, U., Duda, R. O., & Hart, P. E. (1974) · Journal of the American Statistical Association · doi:10.2307/2286028 · Cited by 1,018 · see citing works
Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models
van der Merwe, R. (2004) · doi:10.6083/m4z60kz5 · Cited by 1,017 · see citing works
McNeff, J. (2002) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/22.989949 · Cited by 510 · see citing works
The Robot That Won the DARPA Grand Challenge
Thrun, S. (2007) · Cited by 406 · see citing works
Self-citationA task description language for robot control
Simmons, R. G., & Apfelbaum, D. (2002) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.1998.724883 · Cited by 319 · see citing works
Rapidly adapting machine vision for automated vehicle steering
Pomerleau, D., & Jochem, T. M. (1996) · IEEE Expert · doi:10.1109/64.491277 · Cited by 296 · see citing works
SCARF: a color vision system that tracks roads and intersections
Crisman, J. D., & Thorpe, C. (1993) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.210794 · Cited by 244 · see citing works
The seeing passenger car 'VaMoRs-P'
Dickmanns, E. D., Behringer, R., Dickmanns, D., Hildebrandt, T., Maurer, M., Thomanek, F., & Schiehlen, J. (2005) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.1994.639472 · Cited by 224 · see citing works
Vibration-based terrain classification for planetary exploration rovers
Brooks, C. A., & Iagnemma, K. D. (2005) · IEEE Transactions on Robotics · doi:10.1109/tro.2005.855994 · Cited by 223 · see citing works
Sigma-Point Kalman Filters for Integrated Navigation
van der Merwe, R., & Wan, E. A. (2004) · Cited by 186 · see citing works
The lane-curvature method for local obstacle avoidance
Ko, N. Y., & Simmons, R. G. (2002) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.1998.724829 · Cited by 157 · see citing works
Vision-guided flight stability and control for micro air vehicles
Ettinger, S. M., Nechyba, M. C., Ifju, P., et al. (2003) · Advanced Robotics · doi:10.1163/156855303769156983 · Cited by 148 · see citing works
Self-citationIagnemma, K., & Dubowsky, S. (2004) · Springer tracts in advanced robotics · doi:10.1007/b94718 · Cited by 125 · see citing works
Intelligent Unmanned Ground Vehicles
(1997) · Springer eBooks · doi:10.1007/978-1-4615-6325-9 · Cited by 108 · see citing works
Knowledge-Based Training of Artificial Neural Networks for Autonomous Robot Driving
Pomerleau, D. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3184-5_2 · Cited by 102 · see citing works
Intelligent Unmanned Ground Vehicles: Autonomous Navigation Research at Carnegie Mellon
Hebert, M. (1997) · Kluwer Academic Publishers eBooks · Cited by 99 · see citing works
Enhancing Supervised Terrain Classification with Predictive Unsupervised Learning
Happold, M., Ollis, M., & Johnson, N. F. (2006) · doi:10.15607/rss.2006.ii.006 · Cited by 77 · see citing works
Rough Terrain Autonomous Mobility—Part 1: A Theoretical Analysis of Requirements
Kelly, A., & Stentz, A. (1998) · Autonomous Robots · doi:10.1023/a:1008801421636 · Cited by 68 · see citing works
Interacting Markov Random Fields for Simultaneous Terrain Modeling and Obstacle Detection
Wellington, C., Courville, A. C., & Stentz, A. (2005) · doi:10.15607/rss.2005.i.001 · Cited by 65 · see citing works
Vision for ground vehicles: history and prospects
Dickmanns, E. D. (2002) · International Journal of Vehicle Autonomous Systems · doi:10.1504/ijvas.2002.001804 · Cited by 49 · see citing works
(1999) · American Institute of Aeronautics and Astronautics eBooks · doi:10.2514/5.9781600861994.0109.0138 · Cited by 33 · see citing works
Active Learning For Outdoor Obstacle Detection
Dima, C., & Hebert, M. (2005) · doi:10.15607/rss.2005.i.002 · Cited by 21 · see citing works
Using CART to segment road images
Davies, B., & Lienhart, R. W. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.650164 · Cited by 13 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [10]10. Farrell, J., & Barth, M. ͑1999͒. The global positioning sys- tem. New York: McGraw-Hill.
- [11]11. Gat, E. ͑1998͒. Three-layered architectures. In D. Korten- kamp, R. Bonasso, & R. Murphy ͑Eds.͒, AI-based mo- bile robots: Case studies of successful robot systems ͑pp. 195-210͒. Cambridge, MA: MIT Press.
- [21]21. In J.H. Connell & S. Mahadevan ͑Eds.͒, Robot learning ͑pp. 19-43͒. New York: Kluwer Academic.
Cited by
Sorted by citations
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
Wu, B., Wan, A., Yue, X., & Keutzer, K. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8462926 · Cited by 967 · see citing works
Robots that can adapt like animals
Cully, A., Clune, J., Tarapore, D., & Mouret, J.-B. (2015) · Nature · doi:10.1038/nature14422 · Cited by 979 · see citing works
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2017.2749181 · Cited by 885 · see citing works
Making Bertha Drive—An Autonomous Journey on a Historic Route
Ziegler, J., Bender, P., Schreiber, M., Lategahn, H., Strauss, T., Stiller, C., Dang, T., Franke, U., Appenrodt, N., Keller, C. G., et al. (2014) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2014.2306552 · Cited by 854 · 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
A perception‐driven autonomous urban vehicle
Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S., et al. (2008) · Journal of Field Robotics · doi:10.1002/rob.20262 · Cited by 429 · see citing works
A Survey on Multimodal Large Language Models for Autonomous Driving
Cui, C., Ma, Y., Cao, X., Ye, W., Zhou, Y., Liang, K., Chen, J., Lu, J., Yang, Z., Liao, K.-D., et al. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw60836.2024.00106 · Cited by 369 · see citing works
Sensor Technology in Autonomous Vehicles : A review
Campbell, S., O'Mahony, N., Krpalcova, L., Riordan, D., Walsh, J., Murphy, A., & Ryan, C. (2018) · 2018 29th Irish Signals and Systems Conference (ISSC) · doi:10.1109/issc.2018.8585340 · Cited by 241 · see citing works
Nonlinear Constraint Network Optimization for Efficient Map Learning
Grisetti, G., Stachniss, C., & Burgard, W. (2009) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2009.2026444 · Cited by 222 · see citing works
Tahir, N. U. A., Zhang, Z., Asim, M., Chen, J., & ELAffendi, M. (2024) · Algorithms · doi:10.3390/a17030103 · Cited by 145 · see citing works
Little Ben: The Ben Franklin Racing Team’s Entry in the 2007 DARPA Urban Challenge
Bohren, J., Foote, T., Keller, J., Kushleyev, A., Lee, D., Stewart, A., Vernaza, P., Derenick, J., Spletzer, J., & Satterfield, B. (2008) · Journal of Field Robotics · doi:10.1002/rob.20260 · Cited by 135 · see citing works
Watch this: Scalable cost-function learning for path planning in urban environments
Wulfmeier, M., Wang, D. Z., & Posner, I. (2016) · 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759328 · Cited by 116 · see citing works
Automated driving and autonomous functions on road vehicles
Gordon, T. J., & Lidberg, M. (2015) · Vehicle System Dynamics · doi:10.1080/00423114.2015.1037774 · Cited by 120 · 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
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods
Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3076844 · Cited by 105 · see citing works
A review on the self and dual interactions between machine learning and optimisation
Song, H., Triguero, I., & Özcan, E. (2019) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00185-z · Cited by 101 · see citing works
Open Source Integrated Planner for Autonomous Navigation in Highly Dynamic Environments
Darweesh, H., Takeuchi, E., Takeda, K., Ninomiya, Y., Sujiwo, A., Morales, L. Y., Akai, N., Tomizawa, T., Kato, S., Nagoya University Furo-cho, C.-K., et al. (2017) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2017.p0668 · Cited by 81 · 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
Jang, C. H., Kim, C. S., Jo, K. C., & Sunwoo, M. (2017) · International Journal of Automotive Technology · doi:10.1007/s12239-017-0015-7 · Cited by 28 · see citing works
Human-like planning of swerve maneuvers for autonomous vehicles
Gu, T., Dolan, J. M., & Lee, J.-W. (2016) · 2016 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2016.7535466 · Cited by 24 · see citing works
Ethical AI cannot be fostered in a vacuum: why AI ethics research needs industry involvement
Küçükuncular, A. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00182-9 · Cited by 5 · see citing works
Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving
Li, D., & Okhrin, O. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3708405
- Authors
- Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., Lau, K., Oakley, C., Palatucci, M., Pratt, V., Stang, P., Strohband, S., Dupont, C., Jendrossek, L., Koelen, C., Markey, C., Rummel, C., van Niekerk, J., Jensen, E., Alessandrini, P., Bradski, G., Davies, B., Ettinger, S., Kaehler, A., Nefian, A., & Mahoney, P.
- Affiliations
- Stanford University (US)
- Intel (United States) (US)
- Venue
- Journal of Field Robotics
- Publisher
- Wiley
- Published
- 2006-09
- Volume
- 23
- Issue
- 9
- Pages
- 661-692
- ISSN
- 1556-4959 (ISSN-L)
- 1556-4967
- OpenAlex
- W2154844948
- Semantic Scholar
- 9e938cf8a74c2e7415bfd10d66a9ea4b1c3e0e15
- Cited by
- 2,237
- References
- 28
- Citations by year
- 2026: 92 · 2025: 133 · 2024: 124 · 2023: 128 · 2022: 137 · 2021: 165 · 2020: 168 · 2019: 124 · 2018: 126 · 2017: 107 · 2016: 111 · 2015: 127 · 2014: 113 · 2013: 102 · 2012: 79
- FWCI
- 207.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Field Robotics (publishedVersion)
- Funders
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- DARPA Grand Challenge, autonomous driving, desert driving, autonomous vehicles, robot software architecture, machine learning, probabilistic reasoning
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