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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In the Passenger Seat: Investigating Ride Comfort Measures in Autonomous Cars
TL;DR: The novel concept of the loss of driver controllability is introduced here, and traditional comfort measures are examined and autonomous passenger awareness factors are proposed and path-planning methods are categorized in light of the offered factors.
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Fast segmentation of 3D point clouds: A paradigm on LiDAR data for autonomous vehicle applications
TL;DR: The proposed algorithm first extracts the ground surface in an iterative fashion using deterministically assigned seed points, and then clusters the remaining non-ground points taking advantage of the structure of the LiDAR point cloud.
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Nonlinear Constraint Network Optimization for Efficient Map Learning
TL;DR: This paper addresses the so-called graph-based formulation of simultaneous localization and mapping (SLAM) and can be seen as an extension of Olson's algorithm toward non-flat environments and applies a novel parameterization of the nodes of the graph that significantly improves the performance of the algorithm and can cope with arbitrary network topologies.
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A Survey of Deep Learning Applications to Autonomous Vehicle Control
TL;DR: The strengths and limitations of available deep learning methods are identified through comparative analysis and the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety are discussed.
Journal ArticleDOI
Extensive Tests of Autonomous Driving Technologies
Alberto Broggi,Michele Buzzoni,Stefano Debattisti,Paolo Grisleri,Maria Chiara Laghi,Paolo Medici,Pietro Versari +6 more
TL;DR: The vision of the Artificial Vision and Intelligent Systems Laboratory (VisLab) on future automated vehicles, ranging from sensor selection up to their extensive testing, is presented, and VisLab's design choices are explained using the BRAiVE autonomous vehicle prototype as an example.
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