Towards fully autonomous driving: Systems and algorithms
Abstract
In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control. In addition, a robust vehicle platform with appropriate sensors, computational hardware, networking, and software infrastructure is essential. We previously published an overview of Junior, Stanford's entry in the 2007 DARPA Urban Challenge. This race was a closed-course competition which, while historic and inciting much progress in the field, was not fully representative of the situations that exist in the real world. In this paper, we present a summary of our recent research towards the goal of enabling safe and robust autonomous operation in more realistic situations. First, a trio of unsupervised algorithms automatically calibrates our 64-beam rotating LIDAR with accuracy superior to tedious hand measurements. We then generate high-resolution maps of the environment which are subsequently used for online localization with centimeter accuracy. Improved perception and recognition algorithms now enable Junior to track and classify obstacles as cyclists, pedestrians, and vehicles; traffic lights are detected as well. A new planning system uses this incoming data to generate thousands of candidate trajectories per second, choosing the optimal path dynamically. The improved controller continuously selects throttle, brake, and steering actuations that maximize comf…
Integrity 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?
A survey covering present challenges, available and emerging high-level system architectures, individual core functions such as localization, mapping, perception, planning, vehicle control, and human-machine interface altogether does not exist.
“However, a survey that covers: present challenges, available and emerging high-level system architectures, individual core functions such as localization, mapping, perception, planning, vehicle control, and human-machine interface altogether does not exist.”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [15]
Ego-only is the most common approach amongst state-of-the-art ADSs.
“Ego-only is the most common approach amongst the state-of-the-art ADSs [15], [47], [51]- [56], [56]- [58].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [15]
These design philosophies are realized with two alternative approaches: modular or end-to-end driving.
“Furthermore, these design philosophies are realized with two alternative approaches: modular [15], [47], [51]- [58] or endto-end driving [59]- [67].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [15]
ADSs are designed either as standalone, ego-only systems or connected multi-agent systems.
“ADSs are designed either as standalone, ego-only systems [15], [47] or connected multi-agent systems [48]- [50].”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [15]
Carnegie Mellon's Boss and Stanford's Junior both utilized a priori information.
“Whereas, the winner, Carnegie Mellon's Boss [47] and the runnerup, Stanford's Junior [15], both utilized a priori information.”
From A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i> (2020) · cites this paper as [15]
Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
Map-Based Precision Vehicle Localization in Urban Environments
Levinson, J., Montemerlo, M., & Thrun, S. (2007) · Robotics: Science and Systems III · doi:10.15607/rss.2007.iii.016 · Cited by 477 · see citing works
Self-citation
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
“[20] X X X X X [21] X X X [22] X X [23] X [24] X X X X [25] X X X [26] X [27] X X X X X [28] X X X X Ours X X X X X X X X X”
BackgroundMethodInfluentialSimultaneous 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
Autonomous vehicle perception: The technology of today and tomorrow
Van Brummelen, J., O’Brien, M., Gruyer, D., & Najjaran, H. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.02.012 · Cited by 869 · see citing works
Autonomous Vehicle Safety: An Interdisciplinary Challenge
Koopman, P., & Wagner, M. (2017) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2016.2583491 · Cited by 688 · see citing works
Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J.-M., Lam, V.-D., Bewley, A., & Shah, A. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8793742 · Cited by 694 · 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
Deep learning-based image recognition for autonomous driving
Fujiyoshi, H., Hirakawa, T., & Yamashita, T. (2019) · IATSS Research · doi:10.1016/j.iatssr.2019.11.008 · Cited by 439 · see citing works
Shi, Z., Yao, W., Li, Z., Zeng, L., Zhao, Y., Zhang, R., Tang, Y., & Wen, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115733 · Cited by 327 · 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
From Machine Learning to Explainable AI
Holzinger, A. (2018) · 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) · doi:10.1109/disa.2018.8490530 · Cited by 306 · see citing works
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
Perdomo-Ortiz, A., Benedetti, M., Realpe-Gómez, J., & Biswas, R. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab859 · Cited by 274 · see citing works
Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction
Vineet, V., Miksik, O., Lidegaard, M., Niebner, M., Golodetz, S., Prisacariu, V. A., Kahler, O., Murray, D. W., Izadi, S., Peerez, P., et al. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7138983 · Cited by 200 · see citing works
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 · see citing works
Firnkorn, J., & Müller, M. (2015) · Environmental Science & Policy · doi:10.1016/j.envsci.2014.09.005 · Cited by 187 · see citing works
From Responsibility to Reason-Giving Explainable Artificial Intelligence
Baum, K., Mantel, S., Schmidt, E., & Speith, T. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00510-w · Cited by 103 · see citing works
Analyzing machine learning models to accelerate generation of fundamental materials insights
Umehara, M., Stein, H. S., Guevarra, D., Newhouse, P. F., Boyd, D. A., & Gregoire, J. M. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0172-5 · Cited by 91 · see citing works
Martins, N. R. B., Angelica, A., Chakravarthy, K., Svidinenko, Y., Boehm, F. J., Opris, I., Lebedev, M. A., Swan, M., Garan, S. A., Rosenfeld, J. V., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00112 · Cited by 91 · see citing works
Beam Alignment in mmWave V2X Communications: A Survey
Tan, J., Luan, T. H., Guan, W., Wang, Y., Peng, H., Zhang, Y., Zhao, D., & Lu, N. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3383093 · Cited by 75 · see citing works
CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles
Wang, Y., Liu, S., Wu, X., & Shi, W. (2018) · 2018 IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2018.00010 · Cited by 63 · see citing works
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 · 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
EA-FUSION: A multimodal emotion recognition model using EEG and facial expression data
Xu, C., Li, W., Li, S., Zhao, P., Xing, F., & Song, A. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108629 · Cited by 6 · see citing works
- Authors
- Levinson, J., Askeland, J., Becker, J., Dolson, J., Held, D., Kammel, S., Kolter, J. Z., Langer, D., Pink, O., Pratt, V., Sokolsky, M., Stanek, G., Stavens, D., Teichman, A., Werling, M., & Thrun, S.
- Affiliations
- Volkswagen Group (United States) (US)
- Robert Bosch (United States) (US)
- Massachusetts Institute of Technology (US)
- BMW (Germany) (DE)
- Stanford University (US)
- Venue
- 2011 IEEE Intelligent Vehicles Symposium (IV)
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 163-168
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W2139137304
- Cited by
- 1,374
- References
- 2
- Citations by year
- 2026: 36 · 2025: 89 · 2024: 116 · 2023: 131 · 2022: 153 · 2021: 154 · 2020: 127 · 2019: 148 · 2018: 127 · 2017: 85 · 2016: 63 · 2015: 65 · 2014: 37 · 2013: 24 · 2012: 18
- FWCI
- 40.26
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Intelligent Vehicles Symposium (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
- Keywords
- autonomous driving, urban driving, DARPA Urban Challenge, lidar calibration, unsupervised calibration, online localization, obstacle recognition, traffic light detection, trajectory planning, vehicle control, sensor fusion
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
