TY - GEN
T1 - Temporal preview estimation for design of a low cost lane-following system using a forward-facing monocular camera
AU - Brown, Alexander
AU - Brennan, Sean
PY - 2014
Y1 - 2014
N2 - Computer-based guidance of passenger vehicles is a common reality today, but cost, computation, and robustness challenges remain to obtain accurate vehicle state estimates. This study builds on previous work by the authors towards the development of a vehicle state estimation framework that uses optimal preview control theory to fuse map, GPS, inertial, and forward-looking camera information in a linear filter that offers a-priori predictions of state estimate accuracy. By designing an optimal preview controller around a preview filter designed to make full use of a test vehicle's low-cost sensors, on-board map, and available visibility, a matched perception and control system is obtained. The resulting preview-based guidance system has a structure similar to LQG algorithms, and is tested both in simulation and on a real vehicle. The closed loop system provides lane-level tracking performance with low cost sensors.
AB - Computer-based guidance of passenger vehicles is a common reality today, but cost, computation, and robustness challenges remain to obtain accurate vehicle state estimates. This study builds on previous work by the authors towards the development of a vehicle state estimation framework that uses optimal preview control theory to fuse map, GPS, inertial, and forward-looking camera information in a linear filter that offers a-priori predictions of state estimate accuracy. By designing an optimal preview controller around a preview filter designed to make full use of a test vehicle's low-cost sensors, on-board map, and available visibility, a matched perception and control system is obtained. The resulting preview-based guidance system has a structure similar to LQG algorithms, and is tested both in simulation and on a real vehicle. The closed loop system provides lane-level tracking performance with low cost sensors.
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U2 - 10.1109/IVS.2014.6856606
DO - 10.1109/IVS.2014.6856606
M3 - Conference contribution
AN - SCOPUS:84905392123
SN - 9781479936380
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 1457
EP - 1462
BT - 2014 IEEE Intelligent Vehicles Symposium, IV 2004 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE Intelligent Vehicles Symposium, IV 2014
Y2 - 8 June 2014 through 11 June 2014
ER -