Grid-Based Multi-Road-Course Estimation Using Motion Planning | IEEE Journals & Magazine | IEEE Xplore

Grid-Based Multi-Road-Course Estimation Using Motion Planning


Abstract:

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 veh...Show More

Abstract:

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.
Published in: IEEE Transactions on Vehicular Technology ( Volume: 65, Issue: 4, April 2016)
Page(s): 1924 - 1935
Date of Publication: 07 April 2015

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I. Introduction

The road course is one of the building blocks of autonomous-vehicle environment perception. It represents where the road is heading and where the road boundaries are, and thus, it defines the area in which the vehicle ought to drive. Particularly in situations without lane markings, such as on many urban roads, or when lane-marking information is ambiguous, such as in road construction sites, the road boundaries are indispensable for automated vehicles.

References

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