Generalized Regressive Motion: a Visual Cue to Collision

TL;DR

This paper introduces Generalized Regressive Motion (GRM), a new visual cue for collision detection that outperforms looming in dynamic scenarios involving moving animals, supported by geometric analysis and agent-based modeling.

Contribution

The paper proposes and validates GRM as a novel visual cue for collision avoidance, extending understanding beyond traditional looming detection.

Findings

GRM reliably indicates potential collisions among moving animals.

GRM outperforms looming in detecting approach and preventing collisions.

Agent-based models show GRM enhances mobility and collision avoidance.

Abstract

Brains and sensory systems evolved to guide motion. Central to this task is controlling the approach to stationary obstacles and detecting moving organisms. Looming has been proposed as the main monocular visual cue for detecting the approach of other animals and avoiding collisions with stationary obstacles. Elegant neural mechanisms for looming detection have been found in the brain of insects and vertebrates. However, looming has not been analyzed in the context of collisions between two moving animals. We propose an alternative strategy, Generalized Regressive Motion (GRM), which is consistent with recently observed behavior in fruit flies. Geometric analysis proves that GRM is a reliable cue to collision among conspecifics, whereas agent-based modeling suggests that GRM is a better cue than looming as a means to detect approach, prevent collisions and maintain mobility.