Lane-change decision aid system based on motion-driven vehicle tracking

Overtaking and lane changing are very dangerous driving maneuvers due to possible driver distraction and blind spots. We propose an aid system based on image processing to help the driver in these situations. The main purpose of an overtaking monitoring system is to segment the rear view and track the overtaking vehicle. We address this task with an optic-flow-driven scheme, focusing on the visual field in the side mirror by placing a camera on top of it. When driving a car, the ego-motion optic-flow pattern is very regular, i.e., all the static objects (such as trees, buildings on the roadside, or landmarks) move backwards. An overtaking vehicle, on the other hand, generates an optic-flow pattern in the opposite direction, i.e., moving forward toward the vehicle. This well-structured motion scenario facilitates the segmentation of regular motion patterns that correspond to the overtaking vehicle. Our approach is based on two main processing stages: First, the computation of optical flow in real time uses a customized digital signal processor (DSP) particularly designed for this task and, second, the tracking stage itself, based on motion pattern analysis, which we address using a standard processor. We present a validation benchmark scheme to evaluate the viability and robustness of the system using a set of overtaking vehicle sequences to determine a reliable vehicle-detection distance.