Abstract-Road accidents, besides being one of the main causes of mortality, have an economic impact on vehicle owners. Several conditions as driver imprudence, road conditions and obstacles are the main factor that will cause accidents. The most important automotive industries are incorporating technology to reduce risk in vehicles. In this way, lane detection systems have important attention, because from this data is possible to determine risk situations such as presence of obstacles, incorrect lane changes or lane departures. This paper proposes a technique for lane detection, based on image processing, which allows identifying the position of lateral lanes and their type. The method is composed of four stages: edge enhancement, potential lanes detection, post-processing and color lane estimation. The method was proved using image dataset and video captures over 12.000 frames. The accuracy of the system was of 91.9%.

Several conditions as driver imprudence, road conditions and obstacles are the main factors that will cause road accidents. The most important automotive industries are incorporating technology to reduce risk in vehicles. Their products are expensive and lack flexibility to incorporate new features. This work presented a first approach to increase vehicle safety based on regional features. A framework was implemented, incorporating lane analysis and obstacle detection through image processing. The framework was tested using image datasets and real captures with satisfactory results.