Human error is the main cause of the many road traffic accidents which every year take the lives of millions of people and injure many more. Driving safety is thus a major concern leading to research in autonomous driving systems. One project at the Centre for Computational Intelligence (C2i), NTU, aims at using fuzzy neural architectures such as GenSoFNN-Yager to realize intelligent driving i.e., to learn to autonomously park, make U-turns, drive, and even decide when to change lane, overtake, etc. This paper presents recent work on Intelligent Speed Adaptation and Steering Control (ISASC), a novel feature of which is the ability to anticipate the road profile and negotiate curves safely. The proposed system was developed and tested on a driving simulator. Experimental results show the robustness of the system in learning from example the desired human driving expertise and applying this knowledge to negotiate new unseen roads.