TY - JOUR
T1 - Grid-Based Multi-Road-Course Estimation Using Motion Planning
AU - Tanzmeister, Georg
AU - Wollherr, Dirk
AU - Buss, Martin
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2016/4
Y1 - 2016/4
N2 - Knowing the course of the road, together with the corresponding road boundaries is an essential component of many advanced driver-assistance systems and of autonomous vehicles. This work presents an indirect grid-based approach for road course estimation. Due to the grid representation, it is independent of specific features or particular sensors and is able to handle continuous as well as sparse road boundaries of arbitrary shape. Furthermore, the number of road courses in the scene is determined to detect road junctions and forks in the road, and the boundaries of each road course are individually estimated. The approach is based on local path planning and path clustering to find the principal moving directions through the environment. They separate the boundaries and are used for their extraction. The set of local paths and principal moving directions is reduced with approximate knowledge of the road velocity paired with system constraints, and validation and tracking assure the required robustness. Experimental results from autonomous navigation of a vehicle through an unmapped road construction site as well as quantitative evaluations demonstrate the performance of the method.
AB - Knowing the course of the road, together with the corresponding road boundaries is an essential component of many advanced driver-assistance systems and of autonomous vehicles. This work presents an indirect grid-based approach for road course estimation. Due to the grid representation, it is independent of specific features or particular sensors and is able to handle continuous as well as sparse road boundaries of arbitrary shape. Furthermore, the number of road courses in the scene is determined to detect road junctions and forks in the road, and the boundaries of each road course are individually estimated. The approach is based on local path planning and path clustering to find the principal moving directions through the environment. They separate the boundaries and are used for their extraction. The set of local paths and principal moving directions is reduced with approximate knowledge of the road velocity paired with system constraints, and validation and tracking assure the required robustness. Experimental results from autonomous navigation of a vehicle through an unmapped road construction site as well as quantitative evaluations demonstrate the performance of the method.
KW - Autonomous vehicles
KW - Drivable-region detection
KW - Road boundary detection
KW - Road course estimation
UR - http://www.scopus.com/inward/record.url?scp=84964905784&partnerID=8YFLogxK
U2 - 10.1109/TVT.2015.2420752
DO - 10.1109/TVT.2015.2420752
M3 - Article
AN - SCOPUS:84964905784
SN - 0018-9545
VL - 65
SP - 1924
EP - 1935
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 4
M1 - 7081371
ER -