A Systematic Review of Driver Behavior Studies to Improve Road Safety and Eco-Sustainability

Abstract

Road safety and environmental pollution are two major issues in road transportation. Driver behavior, understood as a set of events (accelerations, braking, pedal use, lane changes, speed, etc.) describing driving styles and driver states, is central to both reducing crash risks and achieving environmental sustainability. Improving behavior through profiling, feedback, technological support (e.g., ADAS), and incentive schemes can both save lives and reduce fuel consumption and greenhouse gas emissions. This dual benefit makes it a crucial topic for governments, insurers, fleet managers, and automotive engineers. This paper presents a Systematic Literature Review (SLR) aimed at describing the state of knowledge on the classification of driving styles. It examines the different forms of driver behavior, along with the study types, data sources, datasets, features, preprocessing methods, and artificial intelligence algorithms employed to classify such behaviors and assess their effectiveness. Based on the reviewed studies, the goal is to support future experiments and the development of new behavioral models for driver assistance and autonomous driving systems, enhancing both safety and environmental performance. The main novelty of this study lies in its comprehensive and updated perspective on driver behavior research. Unlike previous reviews, which were often limited to naturalistic studies or specific data collection tools, this work considers a wider range of approaches, including naturalistic studies, site-based observations, and driving simulator experiments. Furthermore, the review is updated through 2024 and includes a large number of papers, providing broader and more comprehensive insights.