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In the context of highly automated driving an important aspect is the understanding of Vulnerable Road Users' behavior. In this article we concentrate on starting cyclists at an urban intersection and investigate 104 trajectories of uninstructed cyclists' heads, feet, and bikes. A detailed analysis shows that on average cyclists' heads start moving 0.33 s earlier than the bike. Even 0.31 s before...
This paper focuses on forecasting of pedestrian's short-time trajectories up to 2.5 s for traffic safety applications. We present a self-learning approach based on artificial neural network movement models and compare it to traditional constant velocity Kalman Filter prediction and extrapolation of polynomials fitted using a least-squares error. Trajectories of uninstructed pedestrians in public traffic...
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