  We propose a simple and effective method to derive the Fundamental Diagram
(FD) from platoon vehicle trajectories. Average traffic state variables are
computed using Edie's generalized definitions within time-dependent trapezoidal
space-time areas. To obtain a clear FD, we employ a bivariate data aggregation
technique to eliminate scatter. Our findings are as follows: (i) The proposed
method demonstrates a remarkably consistent relation between the traffic
variables and a clear triangular shape for autonomously-driven vehicles. (ii)
The FDs are invariant to several factors of heterogeneity such as the platoon
length, vehicle characteristics, road particularities, and data acquisition
accuracy. (iii) ACC-driven vehicle platoons with minimum headway setting
achieve much higher capacity, roughly 90\% than those with a large headway
setting. (iv) Connectivity might increase capacity. (v) Human drivers have a
wider near-capacity operation area, showing different behaviors at high speeds
than low ones, and (vi) Safety concerns might arise due to high values of
backward wave speed for ACC-driven vehicles. Comparative analysis with the
state-of-the-art confirms the validity of our approach. The proposed method
stands out due to its simplicity and accuracy, which paves the way for
practical applications in real-time traffic flow monitoring and control within
modern intelligent transportation systems.
