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
Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles
TL;DR: A real-time path-planning algorithm that provides an optimal path for off-road autonomous driving with static obstacles avoidance is presented and was applied to the autonomous vehicle A1, which won the 2010 Autonomous Vehicle Competition organized by the Hyundai-Kia Automotive Group in Korea.
Proceedings ArticleDOI
Towards a viable autonomous driving research platform
Junqing Wei,Jarrod M. Snider,Junsung Kim,John M. Dolan,Ragunathan Rajkumar,Bakhtiar Brian Litkouhi +5 more
TL;DR: An autonomous driving research vehicle with minimal appearance modifications that is capable of a wide range of autonomous and intelligent behaviors, including smooth and comfortable trajectory generation and following; lane keeping and lane changing; intersection handling with or without V2I and V2V; and pedestrian, bicyclist, and workzone detection.
Book ChapterDOI
An Overview of Concept Drift Applications
TL;DR: This chapter provides an application oriented view towards concept drift research, with a focus on supervised learning tasks, and constructs a reference framework for positioning application tasks within a spectrum of problems related to concept drift.
Public Perceptions of Self-Driving Cars: The Case of Berkeley, California
Daniel Howard,Danielle Dai +1 more
TL;DR: In this paper, the authors investigate public attitudes toward self-driving cars using the responses of 107 likely adopters in Berkeley, California as a case study, and find that individuals are most attracted to potential safety benefits, the convenience of not having to find parking, and amenities such as multitasking while en route; conversely, individuals were most concerned with liability, the cost of the technology, and losing control of the vehicle.
Journal ArticleDOI
An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios
TL;DR: This paper formulates the vehicle navigation task as a constrained optimal control problem with constraints bounding a traversable region of the environment and uses a model predictive controller to establish the minimum threat posed to the vehicle given its current state and driver inputs.
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