Uncertainty and Traffic Light Aware Pedestrian Crossing Intention Prediction
Keywords: deep learning, computer vision, recurrent neural networks, uncertainty estimation, intention prediction, attention mechanism, autonomous driving
TL;DR: We improve pedestrian crossing intention model performance and robustness using traffic light status and predicting uncertainty estimation.
Abstract: Predicting Vulnerable Road User (VRU) crossing intention is one of the major challenges in automated driving. Crossing intention prediction systems trained only on pedestrian features underperform in situations that are most obvious to humans, as the latter take additional context features into consideration. Moreover, such systems tend to be over-confident for out-of-distribution samples, therefore making them less reliable to be used by downstream tasks like sensor fusion and trajectory planning for automated vehicles. In this work, we demonstrate that the results of crossing intention prediction systems can be improved by incorporating traffic light status as an additional input. Further, we make the model robust and interpretable by estimating uncertainty. Experiments on the PIE dataset show that the F1-score improved from 0.77 to 0.82 and above for three different baseline systems when considering traffic-light context. By adding uncertainty, we show increased uncertainty values for out-of-distribution samples, therefore leading to interpretable and reliable predictions of crossing intention.
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