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

Area of Science:

• Human locomotion
• Robotics
• Human-computer interaction

Background:

• Collision avoidance is crucial for autonomous systems and pedestrian navigation.
• Understanding human reciprocal interaction during locomotion is key to developing safe navigation strategies.

Purpose of the Study:

• To investigate the conditions and mechanisms of collision avoidance between two walkers.
• To analyze the role of a mutual variable, minimum predicted distance (MPD), in mediating avoidance maneuvers.

Main Methods:

• 30 participants walked in pairs, performing tasks that simulated potential collisions.
• The minimum predicted distance (MPD) between walkers was continuously monitored and analyzed.
• Locomotor trajectories and motion adaptations were recorded to understand interaction dynamics.

Main Results:

• Walkers adapted their motion only when the MPD fell below a critical threshold (1 meter).
• The evolution of MPD revealed three distinct phases: observation, reaction, and regulation.
• The regulation phase indicated anticipatory adjustments for collision avoidance.

Conclusions:

• Humans accurately estimate reciprocal distances and mutually adapt their trajectories to avoid collisions.
• Collision avoidance is a proactive process involving observation, reaction, and anticipatory regulation.
• Future research should explore individual motion adaptations in relation to MPD variations.