Department of Computer Science & Engineering

This report critically evaluates coaching methodologies for enhancing fleet driver safety, engagement, and overall organizational performance. Drawing on empirical research across education, behavioral science, and fleet management, this analysis identifies key dimensions of effective commercial driver coaching, highlights significant limitations of current practices, and outlines strategic recommendations focused explicitly on differentiation and optimization. The findings emphasize the superior effectiveness of personalized, manager-led coaching methods, yet also show how manager-led coaching alone is challenging to implement at scale given the scarce availability of expert coaches. On the other hand, this report shows that in-cabin automated warning systems and self-coaching offer strengths in scalability and flexible learning. However, without integration with more personalized and interactive feedback, these approaches alone do not achieve the same long-term safety outcomes as when combined with human-led coaching elements. As such, this report recommends a mix of the three approaches for future optimization, especially focusing on (1) strategic integration of AI-driven platforms, and (2) complementing existing risk identification coaching programs with targeted positive reinforcement mechanisms and regular short-session, in-person coaching. Ultimately, the provided evidence-based recommendations offer a potential pathway for achieving measurable improvements in driver safety outcomes, operational efficiency, and competitive differentiation.