Pedestrian Intention and Trajectory Prediction in Unstructured Traffic Using IDD-PeD
This addresses the problem of pedestrian safety in autonomous driving for unstructured traffic conditions, providing a new benchmark but is incremental as it focuses on dataset creation.
The paper introduces an Indian driving pedestrian dataset (IDD-PeD) to model pedestrian behavior in unstructured traffic environments, revealing that state-of-the-art intention prediction methods suffer a 15% performance drop and trajectory prediction methods show up to 1208 MSE increase on this dataset.
With the rapid advancements in autonomous driving, accurately predicting pedestrian behavior has become essential for ensuring safety in complex and unpredictable traffic conditions. The growing interest in this challenge highlights the need for comprehensive datasets that capture unstructured environments, enabling the development of more robust prediction models to enhance pedestrian safety and vehicle navigation. In this paper, we introduce an Indian driving pedestrian dataset designed to address the complexities of modeling pedestrian behavior in unstructured environments, such as illumination changes, occlusion of pedestrians, unsignalized scene types and vehicle-pedestrian interactions. The dataset provides high-level and detailed low-level comprehensive annotations focused on pedestrians requiring the ego-vehicle's attention. Evaluation of the state-of-the-art intention prediction methods on our dataset shows a significant performance drop of up to $\mathbf{15\%}$, while trajectory prediction methods underperform with an increase of up to $\mathbf{1208}$ MSE, defeating standard pedestrian datasets. Additionally, we present exhaustive quantitative and qualitative analysis of intention and trajectory baselines. We believe that our dataset will open new challenges for the pedestrian behavior research community to build robust models. Project Page: https://cvit.iiit.ac.in/research/projects/cvit-projects/iddped