A perception‐driven autonomous urban vehicle
Open accessAbstract
This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant communications and computation bandwidth to capture and process high‐resolution, high‐rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kinodynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three‐point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed‐loop simulation in a rapidly exploring randomized trees algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in global positioning system–denied and highly dynamic environments with poor a priori information. © 2008 Wiley Periodicals, Inc.
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Self-citationToward Design of an Environment-Aware Adaptive Locomotion-Mode-Recognition System
Du, L., Zhang, F., Liu, M., & Huang, H. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2208641 · Cited by 77 · see citing works
Sampling-based algorithms for optimal motion planning using closed-loop prediction
Arslan, O., Berntorp, K., & Panagiotis Tsiotras, B. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989581 · Cited by 77 · see citing works
The MIT–Cornell collision and why it happened
Fletcher, L., Teller, S. J., Olson, E., Moore, D., Kuwata, Y., How, J. P., Leonard, J. J., Miller, I., Campbell, M., Huttenlocher, D. P., et al. (2008) · Journal of Field Robotics · doi:10.1002/rob.20266 · Cited by 77 · see citing works
Self-citationRisk Assessment for Cooperative Automated Driving
Dominic, D., Chhawri, S., Eustice, R. M., Ma, D., & Weimerskirch, A. (2016) · doi:10.1145/2994487.2994499 · Cited by 77 · see citing works
Kumar, S., Shi, L., Ahmed, N., Gil, S., Katabi, D., & Rus, D. L. (2012) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2377677.2377724 · Cited by 75 · see citing works
Kumar, S., Shi, L., Ahmed, N., Gil, S., Katabi, D., & Rus, D. L. (2012) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/2342356.2342403 · Cited by 75 · see citing works
Model-free detection and tracking of dynamic objects with 2D lidar
Wang, D. Z., Posner, I., & Newman, P. (2015) · The International Journal of Robotics Research · doi:10.1177/0278364914562237 · Cited by 73 · see citing works
Wu, W., Deng, X., Jiang, P., Wan, S., & Guo, Y. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3307589 · Cited by 70 · see citing works
Moving Objects Detection by Conflict Analysis in Evidential Grids
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Shi, J., Sun, D., Qin, D., Hu, M., Kan, Y., Ma, K., & Chen, R. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103570 · Cited by 68 · see citing works
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Self-citationDeep learning and control algorithms of direct perception for autonomous driving
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A High-rate, Heterogeneous Data Set From The DARPA Urban Challenge
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Error modeling and calibration of exteroceptive sensors for accurate mapping applications
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Ground estimation and point cloud segmentation using SpatioTemporal Conditional Random Field
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Security Modeling of Autonomous Systems
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A passive solution to the sensor synchronization problem
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Self-citationCombining 3D Shape, Color, and Motion for Robust Anytime Tracking
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The intelligent copilot: A constraint-based approach to shared-adaptive control of ground vehicles
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Optimal Extrinsic Calibration Between a Stereoscopic System and a LIDAR
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Human action segmentation with hierarchical supervoxel consistency
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- Authors
- Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S., Koch, O., Kuwata, Y., Moore, D., Olson, E., Peters, S., Teo, J., Truax, R., Walter, M., Barrett, D., Epstein, A., Maheloni, K., Moyer, K., Jones, T., Buckley, R., Antone, M., Galejs, R., Krishnamurthy, S., & Williams, J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Franklin W. Olin College of Engineering (US)
- Draper Laboratory (US)
- BAE Systems (United States) (US)
- MIT Lincoln Laboratory (US)
- ORCID
- Venue
- Journal of Field Robotics
- Publisher
- Wiley
- Published
- 2008-09-25
- Volume
- 25
- Issue
- 10
- Pages
- 727-774
- ISSN
- 1556-4959 (ISSN-L)
- 1556-4967
- OpenAlex
- W2758107681
- Cited by
- 429
- References
- 18
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 27 · 2023: 19 · 2022: 23 · 2021: 32 · 2020: 34 · 2019: 39 · 2018: 31 · 2017: 20 · 2016: 28 · 2015: 36 · 2014: 23 · 2013: 23 · 2012: 21
- FWCI
- 46.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Field Robotics (publishedVersion)
- Funders
- Charles Stark Draper Laboratory
- Franklin W. Olin College of Engineering
- Defense Advanced Research Projects Agency
- Advanced Research Projects Agency
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- autonomous vehicles, passenger cars, kinodynamic motion planning, rapidly-exploring random tree, closed-loop simulation, heterogeneous sensors, environmental sensing, lane keeping, parking, obstacle avoidance, GPS-denied environments, DARPA Urban Challenge
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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