# Traffic Control Gesture Recognition for Autonomous Vehicles Source: [Traffic Control Gesture Recognition for Autonomous Vehicles]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:19:20.109Z Authors: Julian Wiederer, Arij Bouazizi, Ulrich Kressel, Vasileios Belagiannis Published: 2020-07-31T13:40:41.000Z ## Abstract A car driver knows how to react on the gestures of the traffic officers. Clearly, this is not the case for the autonomous vehicle, unless it has road traffic control gesture recognition functionalities. In this work, we address the limitation of the existing autonomous driving datasets to provide learning data for traffic control gesture recognition. We introduce a dataset that is based on 3D body skeleton input to perform traffic control gesture classification on every time step. Our dataset consists of 250 sequences from several actors, ranging from 16 to 90 seconds per sequence. To evaluate our dataset, we propose eight sequential processing models based on deep neural networks such as recurrent networks, attention mechanism, temporal convolutional networks and graph convolutional networks. We present an extensive evaluation and analysis of all approaches for our dataset, as well as real-world quantitative evaluation. The code and dataset is publicly available. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Gesture Recognition on TCG cross-subject (test)](): Gesture Recognition; TCG cross-subject (test); Accuracy: 87.24. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()