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
Robot Navigation in Multi-terrain Outdoor Environments
Guilherme A. S. Pereira,Luciano C. A. Pimenta,Alexandre R. Fonseca,Leonardo de Q. Corrêa,Renato C. Mesquita,Luiz Chaimowicz,Daniel S. C. de Almeida,Mario F. M. Campos +7 more
TL;DR: In this article, the authors present a methodology for motion planning in outdoor environments that takes into account specific characteristics of the terrain and measure the robot's vertical acceleration, which reflects the terrain roughness.
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A Survey of Deep RL and IL for Autonomous Driving Policy Learning.
Zeyu Zhu,Huijing Zhao +1 more
TL;DR: In this paper, a comprehensive survey of deep reinforcement learning (DRL) and deep imitation learning (DIL) techniques for autonomous driving policy learning is presented, which is addressed simultaneously from the system, task-driven and problem-driven perspectives.
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Re-Plannable Automated Parking System With a Standalone Around View Monitor for Narrow Parking Lots
TL;DR: A re-plannable automated parking system with a standalone around view monitor that can constantly reflect several errors and risks of perception, positioning, and control in real-life situations, and then re-generate the parking path to improve the parking precision and avoid any collisions.
Proceedings ArticleDOI
A Computationally Efficient Model for Pedestrian Motion Prediction
TL;DR: In this article, a mathematical model is presented to predict pedestrian motion over a finite horizon, intended for use in collision avoidance algorithms for autonomous driving, based on a road map structure, and assumes a rational pedestrian behavior.
Book ChapterDOI
Design and Development of an Optimal-Control-Based Framework for Trajectory Planning, Threat Assessment, and Semi-autonomous Control of Passenger Vehicles in Hazard Avoidance Scenarios
TL;DR: Simulation and experimental results are presented here to demonstrate the framework’s ability to incorporate configurable intervention laws while sharing control with a human driver.
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