Motion Prediction Influence on the Pedestrian Intention Estimation Near a Zebra Crossing

J. Škovierová, A. Vobecký, M. Uller, R. Škoviera, V. Hlaváč

4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018)

The reported work contributes to the self-driving car efforts, more specifically to scenario understanding from the ego-car point of view. We focus on estimating the intentions of pedestrians near a zebra crossing. First,we predict the future motion of detected pedestrians in a three seconds time horizon. Second, we estimate the intention of each pedestrian to cross the street using a Bayesian network. Results indicate, that the dependence between the error rate of motion prediction and the intention estimation is sub-linear. Thus, despite the lower performance of motion prediction for the time scope larger than one second, the intention estimation remains relatively stable

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