Mechanisms and implications of autonomous vehicle market penetration: Insights from a Markov forecasting model

Abstract

Due to the rapid evolution of autonomous driving technology and the complexity of market penetration mechanisms, establishing a reliable quantitative research approach for measuring autonomous vehicle (AV) penetration and effectively validating forecasted outcomes poses significant challenges. To address this issue, this paper overcomes data limitations by starting from the perspective of Chinese automotive market. It introduces a quantifiable Markov forecasting model that establishes the link between transition probabilities and penetration influencing factors. Through a penetration network, it visually represents the correlation and evolutionary states of AVs. Building upon the model, a framework for data quantification and analysis is formed. By quantifying model indicators with market data such as car performance and historical sales, the network parameters and transition probabilities are continuously updated in real-time. This drives the model to output short-term forecasts for AV penetration in the automotive market. In addition, we devise a two-stage simulation algorithm to accomplish parameter calibration and model validation. Through validation and comparative analysis, it is observed that, compared to direct learning from historical data, our model can more accurately forecast real market penetration trends. Furthermore, sensitivity analysis experiments on market strategies indicate that, compared to technical investment, the market exhibits a higher sensitivity to price adjustments. A strategy combination of increased technical investment in high-level vehicles and judiciously raising prices proves more advantageous for intelligent transformation in the automotive sector than a singular strategy. Additionally, as the AV market evolves, the sensitivity to favorable strategies will gradually increase. Therefore, the developmental stage of the market is a crucial factor for both car companies and investors to consider. The insights gleaned from this paper offer actionable guidance for policymakers and automotive corporations in shaping future market strategies, thereby fostering the continued growth of autonomous driving technologies within the industry.