Unveiling Topics from Scientific Literature on the Subject of Self-driving Cars using Latent Dirichlet Allocation

Abstract

Self-driving cars are becoming popular topics in academia. Consumers of self-driving cars and vehicles have different concerns, for example, safety and security, to name a few. Also, the public sector has interests in self-driving cars such as amending policies to enable the management of self-driving vehicles in cities, urban planning, traffic management and, etc. In this paper, more than 2700 corpus are extracted from literature from several subject areas to identify latent (hidden) topics of self-driving cars. Latent Dirichlet Allocation (LDA) is used for topic identification. The result of this study shows that topics identified are valid research areas such as urban planning, driver car (computer) interaction, self-driving control and system design, ethics in self-driving cars, safety and risk assessment, training dataset quality and machine learning in self-driving cars are among the topics identified. Furthermore, the network visualization of association graph of terms shows that the most frequently discussed concepts reveal that control of self-driving cars is based on algorithms, data, design, method, and model. The methods used in this study and the results can be used as decision tools, if carefully applied, in diverse disciplines that are disrupted by the introduction of self-driving cars. For future study, we plan to extend this study with a larger dataset and other data mining techniques.