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
The Social Force Model has been widely used to simulate pedestrian dynamics. Its simplicity and abil...
In urban traffic, accurate prediction of pedestrian trajectory and advanced collision avoidance stra...
This dissertation addresses the modeling of pedestrians in dynamic and urban environments interactin...
Abstract. Numerical simulation of human crowds is a challenging task and a number of models to simul...
We propose in this paper a minimal speed-based pedestrian model for which particle dynamics are intr...
In the present paper, the avoidance behavior of pedestrians was characterized by controlled experime...
This paper addresses the theoretical foundations of pedestrian models for crowd dynamics. While the ...
Motion planning for multiple entities or a crowd is a challenging problem in today’s virtual environ...
In this paper, the mathematical model of an electric vehicle, as well as the control system for avoi...
Pedestrian simulation provides interesting challenges in the area of 3D visualization, computer anim...
AbstractThis article introduces a new collision avoidance model enabling the design of efficient rea...
We present a mathematical model to predict pedestrian motion over a finite horizon, intended for use...
We introduce a new specification of the social force model in which pedestrians explicitly predict t...
Pedestrian distraction may provoke severe difficulties in automated vehicle (AV) control, which may ...
Collision is one of the main problems in distributed task cooperation involving multiple moving agen...
The Social Force Model has been widely used to simulate pedestrian dynamics. Its simplicity and abil...
In urban traffic, accurate prediction of pedestrian trajectory and advanced collision avoidance stra...
This dissertation addresses the modeling of pedestrians in dynamic and urban environments interactin...
Abstract. Numerical simulation of human crowds is a challenging task and a number of models to simul...
We propose in this paper a minimal speed-based pedestrian model for which particle dynamics are intr...
In the present paper, the avoidance behavior of pedestrians was characterized by controlled experime...
This paper addresses the theoretical foundations of pedestrian models for crowd dynamics. While the ...
Motion planning for multiple entities or a crowd is a challenging problem in today’s virtual environ...
In this paper, the mathematical model of an electric vehicle, as well as the control system for avoi...
Pedestrian simulation provides interesting challenges in the area of 3D visualization, computer anim...
AbstractThis article introduces a new collision avoidance model enabling the design of efficient rea...
We present a mathematical model to predict pedestrian motion over a finite horizon, intended for use...
We introduce a new specification of the social force model in which pedestrians explicitly predict t...
Pedestrian distraction may provoke severe difficulties in automated vehicle (AV) control, which may ...
Collision is one of the main problems in distributed task cooperation involving multiple moving agen...
The Social Force Model has been widely used to simulate pedestrian dynamics. Its simplicity and abil...
In urban traffic, accurate prediction of pedestrian trajectory and advanced collision avoidance stra...
This dissertation addresses the modeling of pedestrians in dynamic and urban environments interactin...