Quantifying the “Mechanicalness” of Autonomous Trajectory Tracking: A Real-Vehicle Comparison with Human Drivers

Abstract

Autonomous driving systems often exhibit trajectory tracking behavior that differs markedly from human drivers, a phenomenon intuitively described as “mechanicalness.” This study moves beyond the traditional focus on tracking accuracy to systematically analyze these behavioral differences through a real-vehicle comparative experiment. Using a steer-by-wire vehicle equipped with the open-source Autoware platform, trajectory data were collected on a closed campus road under straight and curved conditions. A five-dimensional evaluation framework is established to quantify control continuity, prediction horizon, error response mode, style adaptability, and interaction friendliness. Results show that Autoware exhibits a “high-precision, low-smoothness, zero-tolerance” mechanical style, characterized by high-frequency micro-corrections, reactive curve entry, rigid speed tracking, and segmented braking. Human drivers, in contrast, employ an organic mode with discrete corrections, elastic path tolerance, and anticipatory coordination. The technical origins of mechanicalness are identified as four algorithmic paradigms: geometric tracking, decoupled control, error-driven logic, and limited prediction horizon. The findings further reveal a systematic safety–comfort trade-off inherent to mechanical control, and suggest optimization directions including adaptive dead-zone mechanisms, extended prediction horizons, and lateral–longitudinal coordination. These insights provide theoretical and engineering foundations for developing more human-like autonomous driving control strategies.