Stanley: The robot that won the DARPA Grand Challenge

By Sebastian Thrun, Michael Montemerlo, Hendrik Dahlkamp, David Stavens, Andrei Aron, James Diebel, Philip W. L. Fong, J. Gale, Morgan Halpenny, G. Hoffmann and 20 more. 2006

2,648 citations recorded by the source corpus.

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

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Automotive Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Robots that can adapt like animals (2014, background): “3C, undam-aged: 24 [16; 41] seconds, 3 [2; 5] physical trials, repaired: 29 [16; 82] seconds, 3.5 [2; 10] trials).”
Safe Visual Navigation via Deep Learning and Novelty Detection (2017, background): “image-based guidance can significantly extend the effective perceptual range and improve navigation performance [13, 34].”
Contextual Sequence Prediction with Application to Control Library Optimization (2012, methodology): “Such methods are widely used in modern, autonomous ground robots including the two highest placing teams for DARPA Urban Challenge and Grand Challenge [28, 17, 27, 26], LAGR [12], UPI [1], and Perceptor [14] programs.”
Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning (2020, background): “M OST of today’s autonomous driving systems are using a highly modularized hand-engineered approach, for example, perception, localization, behavior prediction, decision making and motion control, etc [1], [2].”
Multiple Futures Prediction (2019, background): “For example, in the autonomous driving domain, motion prediction is central both to the ability to make high level decisions, such as when to perform maneuvers, as well as to low level path planning optimizations [34, 28].”
Hierarchical Control Using Networks Trained with Higher-Level Forward Models (2014, methodology): “This is the case in Stanley, the self-driving car that won the DARPA Grand Challenge (Thrün et al., 2006), which was modeled on the three-layer architectures of Gat (1998).”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “approach is chosen by the winner of the challenge [247].”
Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving (2017, background): “Although the hierarchical approach makes the control problem tractable, and has many successful applications to autonomous vehicles [3]–[7], the decomposition into planning and execution phases introduces inherent limitations.”
"Why Did You Do That?" Explainable Intelligent Robots (2017, background): “This is in contrast to terrain that can be classiﬁed into terrain that is undriveable and terrain that forms a drivable surface, perhaps with some simple modiﬁers such as maximum speed (Thrun et al. 2006).”