  Autonomous driving systems are broadly used equipment in the industries and
in our daily lives, they assist in production, but are majorly used for
exploration in dangerous or unfamiliar locations. Thus, for a successful
exploration, navigation plays a significant role. Road detection is an
essential factor that assists autonomous robots achieved perfect navigation.
Various techniques using camera sensors have been proposed by numerous scholars
with inspiring results, but their techniques are still vulnerable to these
environmental noises: rain, snow, light intensity and shadow. In addressing
these problems, this paper proposed to enhance the road detection system with
filtering algorithm to overcome these limitations. Normalized Differences Index
(NDI) and morphological operation are the filtering algorithms used to address
the effect of shadow and guidance and re-guidance image filtering algorithms
are used to address the effect of rain and/or snow, while dark channel image
and specular-to-diffuse are the filters used to address light intensity
effects. The experimental performance of the road detection system with
filtering algorithms was tested qualitatively and quantitatively using the
following evaluation schemes: False Negative Rate (FNR) and False Positive Rate
(FPR). Comparison results of the road detection system with and without
filtering algorithm shows the filtering algorithm's capability to suppress the
effect of environmental noises because better road/non-road classification is
achieved by the road detection system. with filtering algorithm. This
achievement has further improved path planning/region classification for
autonomous driving system
