Vehicle Local Position Estimation System
This work addresses incremental improvements in autonomous driving systems for enhanced safety and navigation.
The paper tackles vehicle localization on roads by combining a single camera and GPS, using modified Inverse Perspective Mapping, illuminant invariant techniques, and object detection to estimate lane position, distance from boundaries, and relative positions to other vehicles, achieving reliable detection of all lanes and obstacle types.
In this paper, a robust vehicle local position estimation with the help of single camera sensor and GPS is presented. A modified Inverse Perspective Mapping, illuminant Invariant techniques and object detection based approach is used to localize the vehicle in the road. Vehicles current lane, its position from road boundary and other cars are used to define its local position. For this purpose Lane markings are detected using a Laplacian edge feature, robust to shadowing. Effect of shadowing and extra sun light are removed using Lab color space and illuminant invariant techniques. Lanes are assumed to be as parabolic model and fitted using robust RANSAC. This method can reliably detect all lanes of the road, estimate lane departure angle and local position of vehicle relative to lanes, road boundary and other cars. Different type of obstacle like pedestrians, vehicles are detected using HOG feature based deformable part model.