Browsing by Subject "automated vehicles"
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Item Automotive Collision Risk Estimation Under Cooperative Sensing(IEEE, 2020) LaChapelle, Daniel; Humphreys, Todd; Narula, Lakshay; Iannucci, Peter; Moradi-Pari, EhsanThis paper offers a technique for estimating collision risk for automated ground vehicles engaged in cooperative sensing. The technique allows quantification of (i) risk reduced due to cooperation, and (ii) the increased accuracy of risk assessment due to cooperation. If either is significant, cooperation can be viewed as a desirable practice for meeting the stringent risk budget of increasingly automated vehicles; if not, then cooperation—with its various drawbacks—need not be pursued. Collision risk is evaluated over an ego vehicle’s trajectory based on a dynamic probabilistic occupancy map and a loss function that maps collision-relevant state information to a cost metric. The risk evaluation framework is demonstrated using real data captured from two cooperating vehicles traversing an urban intersection.Item Automotive-Radar-Based 50-cm Urban Positioning(IEEE, 2020) Narula, Lakshay; Iannucci, Peter A.; Humphreys, Todd E.Deployment of automated ground vehicles (AGVs) beyond the confines of sunny and dry climes will require sub-lane-level positioning techniques based on radio waves rather than near-visible-light radiation. Like human sight, lidar and cameras perform poorly in low-visibility conditions. This paper develops and demonstrates a novel technique for robust 50-cm-accurate urban ground positioning based on commercially-available low-cost automotive radars. The technique is computationally efficient yet obtains a globally-optimal translation and heading solution, avoiding local minima caused by repeating patterns in the urban radar environment. Performance is evaluated on an extensive and realistic urban data set. Comparison against ground truth shows that, when coupled with stable short-term odometry, the technique maintains 95-percentile errors below 50 cm in horizontal position and 1 degree in heading.Item Dense RTK: Mass-Market Positioning for Automated Vehicles(2016-09-15) Humphreys, Todd E.; Pesyna, Ken; Shepard, Daniel; Murrian, Matthew; Kerns, Andrew