Pedestrian collision avoidance with a local dynamic goal

Abstract

We present here a general formalism for equipping simulated pedestrians with an avoidance mechanism. The central idea is to use a short-range target which is adjusted dynamically depending on the environment and thus modulating the desired velocity of the agent. This formulation can be implemented over any type of existing pedestrian model, being force-based or rule-based. As an example, we implement a simple instance of the formulation which is adjusted to reproduce previous reported and available experimental data of collision avoidance in scenarios of low density. The proposed minimal model shows good agreement with the real trajectories and other macroscopic observables.

Physical distance characterization using pedestrian dynamics simulation

“…The general idea is that the self-propelled particle will produce an action only by changing its desired velocity v i (t), as stated in Ref. [11]. In this case, any change in the direction of desired velocity v through the new mechanism will depend on the neighbor particles and obstacles.…”

Data-driven simulation of pedestrian collision avoidance with a nonparametric neural network

“…Our general framework [1] postulates that a particle i, with position r i , has a temporary and short-range goal T t i (t) that is dynamically placed depending on the environment. T t i will produce a detour in the trajectory in order to avoid any collision.…”

“…We recently proposed a general framework of pedestrian simulation [1] in which the surroundings of a virtual pedestrian, i.e., obstacles and other noncontacting particles, can only influence its trajectory by modifying its desired velocity.…”