Odin: Team VictorTango's entry in the DARPA Urban Challenge

By A. Bacha, Cheryl Bauman, Ruel Faruque, M. Fleming, Christoph Terwelp, C. Reinholtz, D. Hong, A. Wicks, Thomas Alberi, David Anderson and 10 more. J. Field Robotics, 2008

314 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/aaeaa58bedf6fa9b42878bf5914f55f48cf26209

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Citations in the corpus

Self-Driving Cars: A Survey (2019, background): “llotted time limit. The Carnegie Mellon University’s car, “Boss” [URM08], claimed first place, the Stanford University’s car, “Junior” [MON08], finished in second, and the Virginia Tech’s car, “Odin” [BAC08], came in third place. Even though these competitions presented challenges much simpler than ”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “Decisions were made onboard most DUC vehicles through Finite State Machines (FSMs) of varying complexity to dictate actions in response to specific perceived driving contexts [42,185,187,197].”
A Review of Motion Planning Techniques for Automated Vehicles (2016, background): “Spline Curves: A spline is a piecewise polynomial parametric curve divided in sub-intervals that can be defined as polynomial curves [28], [76], b-splines [88], [89] (that can also”
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles (2016, methodology): “Tech constructs a graph discretization of possible maneuvers and searches the graph with the A* algorithm [123].”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “Dijkstra’s algorithm is a widely-used graph search algorithm, which can ﬁnd a single-source shortest path in a graph, and it is a special form of dynamic programming [27], [388], [389].”
Autonomous driving in urban environments: approaches, lessons and challenges (2010, methodology): “The 2007 DUC was won by Carnegie Mellon University (Urmson et al. 2008), with second place going to Stanford (Montemerlo et al. 2008) and third place to Virginia Tech (Bacha et al. 2008).”
GPT-Driver: Learning to Drive with GPT (2023, methodology): “The rule-based methods Treiber et al. (2000); Thrun et al. (2006); Bacha et al. (2008); Leonard et al. (2008); Urmson et al. (2008); Chen et al. (2015); Sauer et al. (2018); Fan et al. (2018) designed explicit rules to determine driving trajectories.”
Argoverse: 3D Tracking and Forecasting With Rich Maps (2019, background): “While high deﬁnition (HD) maps are widely used by motion planning systems, few works explore the use of this strong prior in perception systems [60] despite the fact that the three winning entries of the 2007 DARPA Urban Challenge relied on a DARPA-supplied map – the Route Network Deﬁnition File (RN”