Little Ben: The Ben Franklin Racing Team’s Entry in the 2007 DARPA Urban Challenge
Abstract
Abstract This paper describes “Little Ben,” an autonomous ground vehicle constructed by the Ben Franklin Racing Team for the 2007 DARPA Urban Challenge in under a year and for less than $250,000. The sensing, planning, navigation, and actuation systems for Little Ben were carefully designed to meet the performance demands required of an autonomous vehicle traveling in an uncertain urban environment. We incorporated an array of a global positioning system (GPS)/inertial navigation system, LIDARs, and stereo cameras to provide timely information about the surrounding environment at the appropriate ranges. This sensor information was integrated into a dynamic map that could robustly handle GPS dropouts and errors. Our planning algorithms consisted of a high‐level mission planner that used information from the provided route network definition and mission data files to select routes, whereas the lower level planner used the latest dynamic map information to optimize a feasible trajectory to the next waypoint. The vehicle was actuated by a cost‐based controller that efficiently handled steering, throttle, and braking maneuvers in both forward and reverse directions. Our software modules were integrated within a hierarchical architecture that allowed rapid development and testing of the system performance. The resulting vehicle was one of six to successfully finish the Urban Challenge. © 2008 Wiley Periodicals, Inc.
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Even after applying Velodyne's correction factors and distance offset calibrated using readings from another reference LiDAR sensor, points with uncertainties of the order of 30 cm were noted.
“For example, even after applying Velodyne's correction factors and distance offset calibrated using readings from another reference LiDAR sensor, points with uncertainties of the order of 30 cm were noted [76].”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [76]
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- Authors
- Bohren, J., Foote, T., Keller, J., Kushleyev, A., Lee, D., Stewart, A., Vernaza, P., Derenick, J., Spletzer, J., & Satterfield, B.
- Affiliations
- University of Pennsylvania (US)
- Lehigh University (US)
- Lockheed Martin (United States) (US)
- Corresponding author
- Lee, Daniel
- Venue
- Journal of Field Robotics
- Publisher
- Wiley
- Published
- 2008-08-25
- Volume
- 25
- Issue
- 9
- Pages
- 598-614
- ISSN
- 1556-4959 (ISSN-L)
- 1556-4967
- OpenAlex
- W2399732523
- Cited by
- 135
- References
- 7
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 7 · 2023: 8 · 2022: 13 · 2021: 7 · 2020: 7 · 2019: 11 · 2018: 12 · 2017: 4 · 2016: 9 · 2015: 9 · 2014: 6 · 2013: 8 · 2012: 9
- FWCI
- 15.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Field Robotics (publishedVersion)
- 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 › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Keywords
- autonomous ground vehicles, DARPA Urban Challenge, urban driving, sensor fusion, LiDAR, stereo vision, dynamic maps, mission planning, trajectory optimization, hierarchy, low-cost robotics
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.
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