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A perception‐driven autonomous urban vehicle
Author(s) -
Leonard John,
How Jonathan,
Teller Seth,
Berger Mitch,
Campbell Stefan,
Fiore Gaston,
Fletcher Luke,
Frazzoli Emilio,
Huang Albert,
Karaman Sertac,
Koch Olivier,
Kuwata Yoshiaki,
Moore David,
Olson Edwin,
Peters Steve,
Teo Justin,
Truax Robert,
Walter Matthew,
Barrett David,
Epstein Alexander,
Maheloni Keoni,
Moyer Katy,
Jones Troy,
Buckley Ryan,
Antone Matthew,
Galejs Robert,
Krishnamurthy Siddhartha,
Williams Jonathan
Publication year - 2008
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.20262
Subject(s) - computer science , planner , key (lock) , a priori and a posteriori , obstacle , motion planning , real time computing , process (computing) , software , visualization , obstacle avoidance , distributed computing , control engineering , robot , human–computer interaction , artificial intelligence , mobile robot , engineering , philosophy , computer security , epistemology , political science , law , programming language , operating system
This paper describes the architecture and implementation of an autonomous passenger vehicle designed to navigate using locally perceived information in preference to potentially inaccurate or incomplete map data. The vehicle architecture was designed to handle the original DARPA Urban Challenge requirements of perceiving and navigating a road network with segments defined by sparse waypoints. The vehicle implementation includes many heterogeneous sensors with significant communications and computation bandwidth to capture and process high‐resolution, high‐rate sensor data. The output of the comprehensive environmental sensing subsystem is fed into a kinodynamic motion planning algorithm to generate all vehicle motion. The requirements of driving in lanes, three‐point turns, parking, and maneuvering through obstacle fields are all generated with a unified planner. A key aspect of the planner is its use of closed‐loop simulation in a rapidly exploring randomized trees algorithm, which can randomly explore the space while efficiently generating smooth trajectories in a dynamic and uncertain environment. The overall system was realized through the creation of a powerful new suite of software tools for message passing, logging, and visualization. These innovations provide a strong platform for future research in autonomous driving in global positioning system–denied and highly dynamic environments with poor a priori information. © 2008 Wiley Periodicals, Inc.