A synthetic-vision based steering approach for crowd simulation

By Jan Ondřej, Julien Pettré, Anne-Hélène Olivier, Stéphane Donikian. ACM Transactions on Graphics, 2010

397 citations recorded by the source corpus.

In the everyday exercise of controlling their locomotion, humans rely on their optic flow of the perceived environment to achieve collision-free navigation. In crowds, in spite of the complexity of the environment made of numerous obstacles, humans demonstrate remarkable capacities in avoiding collisions. Cognitive science work on human locomotion states that relatively succinct information is extracted from the optic flow to achieve safe locomotion. In this paper, we explore a novel vision-based approach of collision avoidance between walkers that fits the requirements of interactive crowd simulation. By simulating humans based on cognitive science results, we detect future collisions as well as the level of danger from visual stimuli. The motor-response is twofold: a reorientation strategy prevents future collision, whereas a deceleration strategy prevents imminent collisions. Several examples of our simulation results show that the emergence of self-organized patterns of walkers is reinforced using our approach. The emergent phenomena are visually appealing. More importantly, they improve the overall efficiency of the walkers' traffic and avoid improbable locking situations.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/8f953e5f8da0fec26f536d167dc3a53e3c952893

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Citations in the corpus

Core Challenges of Social Robot Navigation: A Survey (2021, background): “…crowd behaviors in a critical context, such as the circular aggregation or flow of pedestrians around a narrow passage and lanes’ formation between two or more pedestrians’ flows [81, 92, 93, 169, 173] or on designing more artificial scenarios to test interactions between agents [80, 169, 240].”