A low complexity contextual stacked ensemble-learning approach for pedestrian intent prediction
This work addresses the problem of computational efficiency in pedestrian intent prediction for autonomous vehicles, offering an incremental improvement over existing methods.
The paper tackles pedestrian crossing intent prediction for autonomous vehicles by proposing a low-complexity ensemble-learning method that uses contextual data and skeletonization, achieving similar performance to state-of-the-art approaches with a 99.7% reduction in computational complexity.
Walking as a form of active travel is essential in promoting sustainable transport. It is thus crucial to accurately predict pedestrian crossing intention and avoid collisions, especially with the advent of autonomous and advanced driver-assisted vehicles. Current research leverages computer vision and machine learning advances to predict near-misses; however, this often requires high computation power to yield reliable results. In contrast, this work proposes a low-complexity ensemble-learning approach that employs contextual data for predicting the pedestrian's intent for crossing. The pedestrian is first detected, and their image is then compressed using skeleton-ization, and contextual information is added into a stacked ensemble-learning approach. Our experiments on different datasets achieve similar pedestrian intent prediction performance as the state-of-the-art approaches with 99.7% reduction in computational complexity. Our source code and trained models will be released upon paper acceptance