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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Journal ArticleDOI
Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality
Malik Haris,Adam Glowacz +1 more
TL;DR: Whether combining the RGB from the camera and active depth information from LiDAR has better results in end-to-end artificial driving than using only a single modality is examined.
Proceedings Article
A Flexible Real-Time Control System for Autonomous Vehicles
Johannes Meyer,Armin Strobel +1 more
TL;DR: This paper presents a framework for the real-time control of lightweight autonomous vehicles which comprehends a proposed hard- and software design and offers high computing power and flexibility in respect of the control algorithms and additional application dependent tasks.
Proceedings ArticleDOI
Dynamic obstacle avoidance based on curvature arcs
TL;DR: A new method based on the well known Curvature Velocity Method (CVM) and a probabilistic 3D occupancy and velocity grid, developed by the authors, that can deal with dynamic scenarios.
Proceedings ArticleDOI
Prediction-Based Reachability for Collision Avoidance in Autonomous Driving
TL;DR: In this article, the authors leverage the power of trajectory prediction and propose a prediction-based reachability framework to compute safety controllers, instead of always assuming the worst case, they cluster the car's behaviors into multiple driving modes, e.g. left turn or right turn.
Book ChapterDOI
Visual Navigation for Mobile Robots
TL;DR: This chapter presents a number of visual methods that has been experimentally verified: artificial visual landmarks, corridor following using vanishing point, and road following using terrain classification based on data fusion of laser scanner and vision.
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