A Perception-Driven Autonomous Urban Vehicle

DOI: 10.1007/978-3-642-03991-1_5
Source: dx.doi.org


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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    • "The state of the art in high-speed 3D sensing for mobile robots is achieved with the Velodyne sensor. This device, in its HDL32 and HDL64 versions, is tailored to scan wide areas at high frame rates, and is commonplace in research in autonomous vehicles (Leonard et al., 2008). The HDL64 device provides point clouds with over 1.3 M points per second on a 360 degree horizontal view of the scene with a limited vertical field of view of 26.8 degrees and up to 120 m (Spinello, Luber, & Arras, 2011). "
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    • "DARPA evaluated capabilities in different kinds of environments, urban and rural ones. Several cars were developed for this challenges [2] [3] [4]. This challenge consists of a strong step for algorithms evaluation, where the winner was the car that reaches first its target location. "
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    • "Vehicle autonomy is considered as an efficient solution to automatically rebalance vehicles among stations with asymmetric resources, and enable a one-way vehicle sharing option in MoD systems. The ability of vehicles to drive autonomously in urban scenarios has matured as evidenced by the results of the DARPA Urban Challenge (DUC) [2]. However, the developed vehicles depend on a variety of sensors, some of which are prohibitively expensive while others are highly specialized, causing the deployment to be economically infeasible. "
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