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Autonomous or highly automated road vehicles and all related technologies are under intensive research and development. Moreover, internationally a massive investment increase can be observed in the automotive industry. According to this megatrend, new automotive test tracks appear or older ones transform to be capable of testing and proving for autonomous vehicles. Therefore, the question emerges: what are the key areas for automated drive development, which must be financed in case of autonomous proving ground design? It is a real challenge to be able to make the right decisions due to a lack of numerous experiences in this field. In this research, experts of automated driving technology have been surveyed and their opinion and knowledge have been synthesized. As a strong purpose of gaining robust results, the conventional AHP has been amended by the Pareto approach to ensure that the derived weights correspond to the expert scoring intention so perfectly that it cannot be more improved. Since the non-Pareto optimal weight results might cause rank reversal in the final prioritization, the applied Pareto test guarantees that the final outcome reflects the expert evaluators' incentive. The conducted analysis has indicated that the obtained results are robust not only from the sensitivity point of view but also from the Pareto optimality approach. The proposed hierarchical decision model is therefore applicable to assist decision making for autonomous proving ground developments. The main contribution of the article, however, is to present the first reliable prioritization of the autonomous proving ground elements to extend the body of professional knowledge.

Ranking the Key Areas for Autonomous Proving Ground Development Using Pareto Analytic Hierarchy Process