Abstract
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 kino-dynamic 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 (RRT) 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 GPS-denied and highly dynamic environments with poor a priori information.
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Leonard, J. et al. (2009). A Perception-Driven Autonomous Urban Vehicle. In: Buehler, M., Iagnemma, K., Singh, S. (eds) The DARPA Urban Challenge. Springer Tracts in Advanced Robotics, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03991-1_5
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DOI: https://doi.org/10.1007/978-3-642-03991-1_5
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