Stanley: The Robot that Won the DARPA Grand Challenge
Sebastian Thrun,Michael Montemerlo,Hendrik Dahlkamp,David Stavens,Andrei Aron,James Diebel,Philip Fong,John Gale,Morgan Halpenny,Gabriel M. Hoffmann,Kenny Lau,Celia M. Oakley,Mark Palatucci,Vaughan R. Pratt,Pascal Stang,Sven Strohband,Cedric Dupont,Lars-Erik Jendrossek,Christian Koelen,Charles Markey,Carlo Rummel,Joe van Niekerk,Eric Jensen,Philippe Alessandrini,Gary Bradski,Bob Davies,Scott M. Ettinger,Adrian Kaehler,Ara V. Nefian,Pamela Mahoney +29 more
TLDR
The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high‐speed desert driving without manual intervention and relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning.Abstract:
This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without human intervention. The robot’s software system relied predominately on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning. This article describes the major components of this architecture, and discusses the results of the Grand Challenge race.read more
Citations
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Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models
Nicholas Rhinehart,Jeff He,Charles Packer,Matthew A. Wright,Rowan McAllister,Joseph E. Gonzalez,Sergey Levine +6 more
TL;DR: In this paper, a general-purpose contingency planner is developed using high-dimensional scene observations and low-dimensional behavioral observations, which can tractably learn contingencies from behavioral observations.
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Safe and Reliable Path Planning for the Autonomous Vehicle Verdino
Rafael Arnay,Nestor Morales,Antonio Morell,Javier Hernández-Aceituno,Daniel Perea,Jonay Toledo,A. Hamilton,Javier Sanchez-Medina,Leopoldo Acosta +8 more
TL;DR: A local planner which computes a set of commands, allowing an autonomous vehicle to follow a given trajectory, using a schema based on a Frenet frame obtained from the global planner.
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Robust hierarchical controller with conditional integrator based on small gain theorem for reference trajectory tracking of autonomous vehicles
TL;DR: In this paper, a robust trajectory tracking controller is designed for autonomous vehicles based on a hierarchical architecture to make the autonomous vehicle track a given reference trajectory, which can be used to control the vehicle's acceleration and braking.
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A probabilistic optimization approach for motion planning of autonomous vehicles
TL;DR: The results show that the proposed method provides an integrated probabilistic interface between the perception and the planning but also results in an excellent performance in terms of the computation efficiency.
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Runtime-bounded tunable motion planning for autonomous driving
TL;DR: This paper addresses the issue of planning tunability in trajectory planning by proposing a framework with multiple tunable phases of planning, along with two novel techniques: Optimization-free trajectory smoothing/nudging and Sampling-based trajectory search with cascaded ranking.
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