Minimal predicted distance: a common metric for collision avoidance during pairwise interactions between walkers.

This study investigated collision avoidance between two walkers by focusing on the conditions that lead to avoidance manoeuvres in locomotor trajectories. Following the hypothesis of a reciprocal interaction, we suggested a mutual variable as a continuous function of the two walkers' states, denoted minimum predicted distance (MPD). This function predicts the risk of collision, and its evolution over time captures the motion adaptations performed by the walkers. By groups of two, 30 walkers were assigned locomotion tasks which lead to potential collisions. Results showed that walkers adapted their motions only when required, i.e., when MPD is too low (<1 m). We concluded that walkers are able (i) to accurately estimate their reciprocal distance at the time the crossing will occur, and (ii) to mutually adapt this distance. Furthermore, the study of MPD evolution showed three successive phases in the avoidance interaction: observation where MPD(t) is constant, reaction where MPD(t) increases to acceptable values by adapting locomotion and regulation where MPD(t) reaches a plateau and slightly decreases. This final phase demonstrates that collision avoidance is actually performed with anticipation. Future work would consist in inspecting individual motion adaptations and relating them with the variations of MPD.