The modern era is witnessing a crucial revolution regarding the driving concept, towards a more assisted and automated way of driving. Despite the advantages such conception have been extensively predicted and supported in literature, a direct transition from manual to fully automated operation is at the current moment not feasible, both for technological and ethical restraints, related to lack of driver’s acceptance and trust. Within this frame of reasoning the Advanced Driver-Assistance Systems (ADAS) provide a useful intermediate step on the way towards full vehicle autonomy. Nonetheless, as a bridge in between the old and the new driving era, such systems shall optimize the driving safety and performance and at the same time convey a pleasant driving experience. In this framework the current research proposes to investigate the application of a lane tracing ADAS from a different point of view, shifting the focus from the pure vehicle performance assessment to the evaluation and optimization of the driver-system interaction in the steering task. Considering the state of the art, the research proposes as a resolutive approach the design of a comprehensive experimental methodology, by means of which extensively investigate the supported on-centre steering phenomenon from the haptic point of view and propose a meaningful list of KPIs for the assistance optimization. In this sense, the diversification of the steering feel for the same vehicle dynamic event, in terms of swaying path, reveals crucial: therefore, a modular approach for the definition of the steering strategies is proposed, based on human resource-optimization approach prescribed by the reaching theory of kinaesthetic control. As a testing platform the fixed base driving simulator of the European Toyota facility is chosen, allowing to perform driving experiments with high-end vehicle and steering model. The latter for the research purpose has been integrated with a seamless steering command, validated with proving ground data, to promote the realism of assistance and interaction. In this regard, the choice of an evaluation team composed only of expert drivers aims at fostering the evaluation precision and consistency at all the times, performed by means of a tailored steering feel questionnaire. As main outcome of the experimental activity, the collection of subjective ratings and objective steering characteristics is exploited to identify statistical correlation models, representatives of Driver’s appreciation with respect to steering objective characteristics. Among these the most relevant ones shall be selected as KPIs. The designed methodology is hence validated by means of a Pilot Study, through which the main method flaws, related to dataset dimension and lack of correlation validation, have been highlighted and solved by means of a revised method, and a newly applied driver’s impedance identification scheme. These last two details in the context of a Final Experiment eventually allowed the delineation of a concise list of KPIs, providing design guidelines both in control- and interaction-oriented way. In the acknowledgement of the achieved results and the overall potential of the designed approach, several scenarios for future improvement and application are delineated, in view of an always better rendered driver assistance.