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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Energy and flow effects of optimal automated driving in mixed traffic: Vehicle-in-the-loop experimental results
Tyler Ard,Longxiang Guo,Robert Austin Dollar,S. Alireza Fayazi,Nathan Goulet,Yunyi Jia,Beshah Ayalew,Ardalan Vahidi +7 more
TL;DR: The effectiveness of an anticipative car-following algorithm in reducing energy use of gasoline engine and electric Connected and Automated Vehicles (CAV), without sacrificing safety and traffic flow is demonstrated.
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Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction
TL;DR: In this paper, the authors present an end-to-end future-prediction model that focuses on pedestrian safety by using previous video frames, recorded from the perspective of the vehicle, to predict if a pedestrian will cross in front of a vehicle.
Journal ArticleDOI
Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights.
Rodrigo Carvalho Barbosa,Muhammad Ayub,Renata Lopes Rosa,Demostenes Zegarra Rodriguez,Lunchakorn Wuttisittikulkij +4 more
TL;DR: This work proposes a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time, a traffic control algorithm based on the Brazilian Traffic Code, and a database containing Brazilian vehicle images.
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Multiple Futures Prediction
TL;DR: In this paper, the authors introduce a probabilistic framework that efficiently learns latent variables to jointly model the multi-step future motions of agents in a scene, without requiring explicit labels.
Proceedings ArticleDOI
Evaluation of autonomy in recent ground vehicles using the autonomy levels for unmanned systems (ALFUS) framework
George T. McWilliams,Michael Brown,Ryan D. Lamm,Christopher J. Guerra,Paul A. Avery,Kristopher C. Kozak,Bapiraju Surampudi +6 more
TL;DR: Some of the major accomplishments made in the field of ground vehicle autonomy are highlighted and the capabilities of these ground vehicles to the ALFUS framework are mapped and the resulting trends that occur from this mapping are summarized.
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