Using the TOSS method in semi-autonomous passenger car selection

Road traffic accidents have been on the rise worldwide, leading to significant economic and societal costs. According to the World Health Organization, over 1.3 million people die annually due to road traffic accidents, and 50 million people are injured. The economic cost of road collisions is estimated to be between 2% and 7% of the global Gross Domestic Product. One of the potential remedies to revolutionize road safety and transform the transportation landscape is the integration of autonomous features in passenger cars. Self-driving cars can process vast amounts of data in real-time using sensors, cameras, and algorithms to detect and avoid potential hazards on the road. The selection of a passenger car to purchase is a complex decision-making problem that is influenced by various factors. The introduction of electric and autonomous vehicles extends the considered decision criteria even further. A sustainable assessment of autonomous cars requires a range of scientific research tools and methodologies. Multi-Criteria Decision Analysis (MCDA) is a powerful research tool in this domain as it allows evaluation considering multiple factors and criteria, including environmental impacts, economic costs, and social benefits. However, many MCDA methods are susceptible to the linear compensation effect. While linear compensation can be useful for decision-making, it may not be consistent with the strong sustainability paradigm that seeks to balance economic, social, and environmental considerations equally. The paper presents several important contributions to the field of MCDA and sustainability assessment of passenger cars with autonomous features. Firstly, the paper introduces the TOSS method (TOPSIS with Strong Sustainability), an extension of the Technique for Order Preference by Similarity to Ideal Solution method, which supports the strong sustainability paradigm. Secondly, the paper constructs a multi-criteria decision model for sustainable assessment and selection of a passenger car with autonomous features, which avoids the limitations of linear compensation. Thirdly, the paper verifies the robustness of the produced rankings to ensure that the method is reliable and consistent. Finally, the paper benchmarks the TOSS method.