TY - GEN
T1 - High integrity localization with multi-lane camera measurements
AU - Al Hage, Joelle
AU - Xu, Philippe
AU - Bonnifait, Philippe
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - Localization with high integrity is crucial for highly autonomous vehicles. This requires that the localization system send a warning to a client application when it should not be used. The concept of integrity was firstly developed for aviation applications and recently became an active research area for autonomous vehicles. GNSS information merged with dead reckoning sensors is not sufficient for lane level localization in all navigation environments. Map-aided localization with vision sensors is essential to provide redundant and complementary information. In this work, a multi-sensor data fusion method that takes advantage of a high definition (HD) map is presented and the integrity of the obtained solution is quantified. A Fault Detection and Exclusion (FDE) step is added to exclude the faulty measurements from the fusion procedure. A second step is to bound the estimation errors in the Along Track (AT) and Cross Track (CT) directions through Protection Levels (PL). For this step, the usual Gaussian distribution is replaced by a Student's distribution with an adapted degree of freedom chosen according to the navigation environment. The performance of the approach is evaluated with an experimental vehicle equipped with a camera able to detect up to four lane markings simultaneously.
AB - Localization with high integrity is crucial for highly autonomous vehicles. This requires that the localization system send a warning to a client application when it should not be used. The concept of integrity was firstly developed for aviation applications and recently became an active research area for autonomous vehicles. GNSS information merged with dead reckoning sensors is not sufficient for lane level localization in all navigation environments. Map-aided localization with vision sensors is essential to provide redundant and complementary information. In this work, a multi-sensor data fusion method that takes advantage of a high definition (HD) map is presented and the integrity of the obtained solution is quantified. A Fault Detection and Exclusion (FDE) step is added to exclude the faulty measurements from the fusion procedure. A second step is to bound the estimation errors in the Along Track (AT) and Cross Track (CT) directions through Protection Levels (PL). For this step, the usual Gaussian distribution is replaced by a Student's distribution with an adapted degree of freedom chosen according to the navigation environment. The performance of the approach is evaluated with an experimental vehicle equipped with a camera able to detect up to four lane markings simultaneously.
UR - https://www.scopus.com/pages/publications/85072299260
U2 - 10.1109/IVS.2019.8813988
DO - 10.1109/IVS.2019.8813988
M3 - Conference contribution
AN - SCOPUS:85072299260
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 1232
EP - 1238
BT - 2019 IEEE Intelligent Vehicles Symposium, IV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE Intelligent Vehicles Symposium, IV 2019
Y2 - 9 June 2019 through 12 June 2019
ER -