# End-to-End Pedestrian Collision Warning System based on a Convolutional Neural Network with Semantic Segmentation Source: [End-to-End Pedestrian Collision Warning System based on a Convolutional Neural Network with Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:32:19.733Z Authors: Heechul Jung, Min-Kook Choi, Kwon Soon, Woo Young Jung Published: 2016-12-20T09:10:30.000Z ## Abstract Traditional pedestrian collision warning systems sometimes raise alarms even when there is no danger (e.g., when all pedestrians are walking on the sidewalk). These false alarms can make it difficult for drivers to concentrate on their driving. In this paper, we propose a novel framework for an end-to-end pedestrian collision warning system based on a convolutional neural network. Semantic segmentation information is used to train the convolutional neural network and two loss functions, such as cross entropy and Euclidean losses, are minimized. Finally, we demonstrate the effectiveness of our method in reducing false alarms and increasing warning accuracy compared to a traditional histogram of oriented gradients (HoG)-based system. ## 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. ## Original sources and implementation [Original paper]() [PDF]()