Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge

Abstract

This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that successfully entered the finals of the 2007 DARPA Urban Challenge competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. Environmental perception mainly relies on a recent laser scanner that delivers both range and reflectivity measurements. Whereas range measurements are used to provide three-dimensional scene geometry, measuring reflectivity allows for robust lane marker detection. Mission and maneuver planning is conducted using a hierarchical state machine that generates behavior in accordance with California traffic laws. We conclude with a report of the results achieved during the competition. C 2008 Wiley Periodicals, Inc.

A multi-objective evolutionary algorithm for the tuning of fuzzy rule bases for uncoordinated intersections in autonomous driving

“…This information can be received by the autonomous vehicle by communications, both vehicle-to-vehicle and vehicle-to-infrastructure [14,34], or by its own sensing, which can be based on cameras, radar, or lidar, among others [20].…”

Cognitive Navigation

“…The reactive navigation paradigm can easily be combined with both global navigation and guided navigation. The DARPA Urban Challenge 2007 system of team "AnnieWay" was based on such a combination [17,26]. Here, the reactive part got the control choices of the global navigation module, a verification system ensured that the space that would be covered according to the choice was actually clear, and only if not, the reactive mechanism became active, overwriting the control of the upper system.…”

“…by an approach like the "tentacle"-approach [26]. Although this approach was combined with GPS at competitions like the DARPA Urban Challenge 2007 [17] and the European Land Robot Trials (ELROB) from 2007-2010 [13][14][15]18], the approach can be used on its own for exploring off-road terrain. At the fair "Hannover Messe 2010" the approach has indeed been demonstrated on its own, though on a humanoid robot [4] and for the exploration of an indoor environment.…”

Corner Detection for Intelligent Vehicles Based on a Four-Layer Radar

“…Things become different when outside environment is considered. University of Karlsruhe developed an intelligent vehicle which took part in 2007 DARPA Urban Challenge, and the corner detection task was completed by lane-marks finding using an onboard camera [6]- [7] . Dae-Woon Lim concentrated on judging passing permission at an intersection, basis not only include corner detection, but also contain violation signals [8] .…”