The Bio-inspired Perception Sensor (BIPS) component is a small and low power bio-inspired on-chip device which has been used in different computer vision applications (traffic analysis, driving assistance , object tracking). It caught the attention of the embedded vision community, since its specifications could help overcoming the time, size, weight and energy bottlenecks that still limits the current development of computer vision systems. For a long time, the lack of mathematical and algorithmic models of the component has prevented it to spread among the research community. But the recent formalization of BIPS basic functions and mechanisms has allowed to develop numerical models and simulators, in order to better evaluate the advantages and limitations of the concept. In this paper, we experimentally address the generalization capability of the BIPS concept, by evaluating it on the road lane detection application. This allows to illustrate how its parameters can be adapted for a specific vision task. This approach permits to automatically instantiate the main parameters, which stabilizes the system output and improves its performance. The obtained results reach the level of the caltech-lanes reference.