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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Demonstration of a time-efficient mobility system using a scaled smart city
Logan E. Beaver,Behdad Chalaki,A. M. I. Mahbub,Liuhui Zhao,Raymond M. Zayas,Andreas A. Malikopoulos +5 more
TL;DR: In this article, the authors propose a computational framework to deliver real-time control actions that optimise travel time, energy, and safety for connected and automated vehicle (CAV) technologies.
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A Reinforcement Learning-Based Adaptive Path Tracking Approach for Autonomous Driving
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Physics Based Path Planning for Autonomous Tracked Vehicle in Challenging Terrain
Bijo Sebastian,Pinhas Ben-Tzvi +1 more
TL;DR: Inferences based on the results from simulations and experiments show that the proposed planner is more effective in providing an optimal feasible path as compared to existing methodologies, demonstrating clear advantages for rough, unstructured terrain planning.
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Computational Intelligence and Games: Challenges and Opportunities
TL;DR: Some of the recent developments in applying computational intelligence (CI) methods to games are reviewed, some of the potential pitfalls are pointed out, and some fruitful directions for future research are suggested.
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Map-aided localization in sparse global positioning system environments using vision and particle filtering
TL;DR: The algorithm is shown to statistically outperform a tightly coupled GPS/inertial navigation solution both in full GPS coverage and in extended GPS blackouts, and as a function of road type, filter likelihood models, bias models, and filter integrity tests.
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