Cyclist Intention Detection: A Probabilistic Approach

• URL: http://arxiv.org/abs/2104.09176v1
• Date: Mon, 19 Apr 2021 09:59:04 GMT
• Title: Cyclist Intention Detection: A Probabilistic Approach
• Authors: Stefan Zernetsch, Hannes Reichert, Viktor Kress, Konrad Doll, Bernhard Sick
• Abstract summary: This article presents a holistic approach for probabilistic cyclist intention detection. A basic movement detection based on motion history images (MHI) and a residual convolutional neural network (ResNet) are used to estimate probabilities for the current cyclist motion state. These probabilities are used as weights in a probabilistic ensemble trajectory forecast.
• Score: 2.984037222955095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article presents a holistic approach for probabilistic cyclist intention detection. A basic movement detection based on motion history images (MHI) and a residual convolutional neural network (ResNet) are used to estimate probabilities for the current cyclist motion state. These probabilities are used as weights in a probabilistic ensemble trajectory forecast. The ensemble consists of specialized models, which produce individual forecasts in the form of Gaussian distributions under the assumption of a certain motion state of the cyclist (e.g. cyclist is starting or turning left). By weighting the specialized models, we create forecasts in the from of Gaussian mixtures that define regions within which the cyclists will reside with a certain probability. To evaluate our method, we rate the reliability, sharpness, and positional accuracy of our forecasted distributions. We compare our method to a single model approach which produces forecasts in the form of Gaussian distributions and show that our method is able to produce more reliable and sharper outputs while retaining comparable positional accuracy. Both methods are evaluated using a dataset created at a public traffic intersection. Our code and the dataset are made publicly available.

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