Pedestrian Trajectory Prediction for Real-Time Autonomous Systems via Context-Augmented Transformer Networks.

Forecasting the trajectory of pedestrians in shared urban traffic environments from non-invasive sensor modalities is still considered one of the challenging problems facing the development of autonomous vehicles (AVs). In the literature, this problem is often tackled using recurrent neural networks (RNNs). Despite the powerful capabilities of RNNs in capturing the temporal dependency in the pedestrians' motion trajectories, they were argued to be challenged when dealing with longer sequential data. Additionally, whilst the accommodation for contextual information (such as scene semantics and agents interactions) was shown to be effective for robust trajectory prediction, they can also impact the overall real-time performance of prediction system. Thus, in this work, we are introducing a framework based on the transformer networks that were demonstrated recently to be more efficient and outperformed RNNs in many sequential-based tasks. We relied on a fusion of sensor modalities, namely the past positional information, agent interactions information and scene physical semantics information as an input to our framework in order to not only provide a robust trajectory prediction of pedestrians, but also achieve real-time performance for multi-pedestrians' trajectory prediction. We have evaluated our framework on three real-life datasets of pedestrians in shared urban traffic environments and it has outperformed the compared baseline approaches in both short-term and long-term prediction horizons. For the short-term prediction horizon, our approach has achieved lower scores according to the average displacement error and the root-mean squared error (ADE/RMSE) of predictions over the state-of-the art (SOTA) approach by more than 11 cm and 23 cm, respectively. While for the long-term prediction horizon, our approach has achieved lower ADE and FDE over the SOTA approach by more than 62 cm and 165 cm, respectively. Additionally, our approach has achieved superior real time performance by scoring only 0.025 s (i.e., it can provide 40 individual trajectory predictions per second).