This is the official python implementation for paper, "Visual-Motion-Interaction Guided Pedestrian Intention Prediction Framework". authored by Neha Sharma, Chhavi Dhiman, S. Indu

The capability to comprehend the intention of pedestrians on road is one of the most crucial skills that the current autonomous vehicles (AVs) are striving for, to become fully autonomous. In recent years, various multimodal methods have become popular in predicting pedestrian crossing intention that utilizes information from different modalities like trajectory, appearance, context, etc. However, most existing research works still lag rich feature representational ability in a multimodal scenario, restricting the performance of these works. Moreover, less emphasis is put on pedestrian interactions with the surroundings for predicting short-term pedestrian intention in a challenging ego-centric vision. To address these challenges, an efficient Visual Motion Interaction guided intention prediction framework has been proposed in this work. This framework combines three divisions namely, Visual Encoder (VE), Motion Encoder (ME) and Interaction Encoder (IE) to capture rich multimodal features of the pedestrian and its interactions with the surroundings, followed by temporal attention and adaptive fusion module to integrate these multimodal features efficiently. The proposed framework outperforms several SOTAs on benchmark datasets: PIE/JAAD with Accuracy, AUC, F1-score, Precision and Recall as 0.92/0.89, 0.91/0.90, 0.87/0.81, 0.86/0.79, 0.88/0.83 respectively. Furthermore, extensive experiments are carried out to investigate the effect of different fusion architectures and design parameters of all encoders. The proposed VMI framework is able to predict the pedestrian crossing intention 2.5 sec ahead of the crossing event.

The proposed framework is trained and tested on the benchmark PIE and JAAD datasets. The precomputed pose features for both the datasets are available inside folder data/features/pie/poses/ and data/features/jaad/poses/.