Long-range Collision Avoidance for Shared Space Simulation based on Social Forces

Anvari, Bani; Bell, Michael G.H.; Angeloudis, Panagiotis; Ochieng, Washington Y. · 2014 · Transportation Research Procedia

DOI: 10.1016/j.trpro.2014.09.023

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Summary

This paper addresses the challenge of simulating realistic interactions between pedestrians and vehicles in "shared space" environments, where traditional traffic controls are removed to balance priority. The authors identify a limitation in the standard Social Force Model (SFM): its reliance on short-range repulsive forces causes agents to make excessive, urgent detours, resulting in unrealistic trajectories. To resolve this, the study proposes an extension to the SFM that incorporates a long-range collision detection and resolution (CDCR) strategy, enabling agents to anticipate and avoid conflicts before they become imminent. The methodology models pedestrians as circles and cars as ellipses, applying force-based dynamics to simulate movement. The core innovation involves using Principal Component Analysis (PCA) on a set of 50 prior velocity vectors for each agent to predict future speed and direction, thereby reducing uncertainty in trajectory forecasting. The CDCR strategy defines a "shadow" zone for each agent based on these PCA-derived predictions. A potential conflict is detected when another agent intersects this shadow. Upon detection, an optimization process calculates the minimum necessary change in velocity and direction for both agents to avoid collision while staying as close as possible to their desired paths. This resolution accounts for constraints such as maximum speed limits and minimum safe distances at the Closest Point of Approach (CPA). The results demonstrate that the proposed long-range strategy produces more realistic behavior than the standard SFM. Simulations show that without the CDCR strategy, agents react only when within immediate interaction range, leading to abrupt deceleration and deviation. In contrast, with the long-range avoidance mechanism, pedestrians and cars begin to adjust their speed and heading significantly earlier, resulting in smoother, conflict-free trajectories. The optimization successfully minimizes velocity changes, ensuring that interactions remain natural while preventing physical contact. The significance of this work lies in its contribution to microscopic simulation tools for shared space design. By improving the realism of heterogeneous traffic interactions, the model allows urban planners and traffic engineers to better evaluate the trade-offs between travel times, safety, and capacity in shared environments. The integration of PCA for predictive shadow detection offers a robust method for handling complex, mixed-mode traffic dynamics, addressing a gap in existing literature regarding the interaction laws between pedestrians and vehicles.

Key finding

Extending the Social Force Model with a long-range collision avoidance strategy based on Principal Component Analysis results in more realistic pedestrian and vehicle trajectories by enabling earlier and smoother conflict resolution.

Methodology

simulation_modeling

Provenance

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Topics

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• driver vru interaction