  Dynamic obstacle avoidance is a popular research topic for autonomous
systems, such as micro aerial vehicles and service robots. Accurately
evaluating the performance of dynamic obstacle avoidance methods necessitates
the establishment of a metric to quantify the environment's difficulty, a
crucial aspect that remains unexplored. In this paper, we propose four metrics
to measure the difficulty of dynamic environments. These metrics aim to
comprehensively capture the influence of obstacles' number, size, velocity, and
other factors on the difficulty. We compare the proposed metrics with existing
static environment difficulty metrics and validate them through over 1.5
million trials in a customized simulator. This simulator excludes the effects
of perception and control errors and supports different motion and gaze
planners for obstacle avoidance. The results indicate that the survivability
metric outperforms and establishes a monotonic relationship between the success
rate, with a Spearman's Rank Correlation Coefficient (SRCC) of over 0.9.
Specifically, for every planner, lower survivability leads to a higher success
rate. This metric not only facilitates fair and comprehensive benchmarking but
also provides insights for refining collision avoidance methods, thereby
furthering the evolution of autonomous systems in dynamic environments.
