TY - GEN
T1 - Lane-Level Matching Algorithm Based on GNSS, IMU and Map Data
AU - Kreibich, Julian
AU - Brenner, Frederic
AU - Lienkamp, Markus
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - High-precision map services are an indispensable basis of many innovative in-vehicle applications. Safety and comfort can be improved even further by inputting contextual information, such as road surface. Correct allocation requires lane-accurate vehicle localization. This paper presents a novel algorithm for lane-level map-matching that integrates a global navigation satellite system, an inertial measurement unit, and map data. The implemented algorithm consists of five consecutive modules: map generation, sensor-data pre-processing, road assignment, maneuver recognition, and information fusion. The approach was tested over a total distance of 245 km, involving 237 lane changes, within the metropolitan area of Munich. A total accuracy of 82% correctly classified lanes was achieved in test drives recorded with smartphones. Both the implementation and part of the data set of this paper are publicly available (https://github.com/TUMFTM/Lane_Level_Matching).
AB - High-precision map services are an indispensable basis of many innovative in-vehicle applications. Safety and comfort can be improved even further by inputting contextual information, such as road surface. Correct allocation requires lane-accurate vehicle localization. This paper presents a novel algorithm for lane-level map-matching that integrates a global navigation satellite system, an inertial measurement unit, and map data. The implemented algorithm consists of five consecutive modules: map generation, sensor-data pre-processing, road assignment, maneuver recognition, and information fusion. The approach was tested over a total distance of 245 km, involving 237 lane changes, within the metropolitan area of Munich. A total accuracy of 82% correctly classified lanes was achieved in test drives recorded with smartphones. Both the implementation and part of the data set of this paper are publicly available (https://github.com/TUMFTM/Lane_Level_Matching).
KW - HD map
KW - IMU
KW - lane-change detection
KW - lane-matching
KW - map-matching
KW - smartphone
UR - https://www.scopus.com/pages/publications/85124380586
U2 - 10.1109/ISCMI53840.2021.9654917
DO - 10.1109/ISCMI53840.2021.9654917
M3 - Conference contribution
AN - SCOPUS:85124380586
T3 - 2021 8th International Conference on Soft Computing and Machine Intelligence, ISCMI 2021
SP - 211
EP - 218
BT - 2021 8th International Conference on Soft Computing and Machine Intelligence, ISCMI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Soft Computing and Machine Intelligence, ISCMI 2021
Y2 - 26 November 2021 through 27 November 2021
ER -