GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction
This addresses the problem of accurate and context-aware pedestrian trajectory prediction for autonomous driving systems, representing a novel method rather than an incremental improvement.
The paper tackles pedestrian trajectory prediction for autonomous driving by first predicting goal points using scene context and observed trajectories, then generating future trajectories from these goals, which reduces uncertainty to a few target areas. Experimental results show that GoalNet improves state-of-the-art performance by 48.7% on JAAD and 40.8% on PIE datasets.
Predicting the future trajectories of pedestrians on the road is an important task for autonomous driving. The pedestrian trajectory prediction is affected by scene paths, pedestrian's intentions and decision-making, which is a multi-modal problem. Most recent studies use past trajectories to predict a variety of potential future trajectory distributions, which do not account for the scene context and pedestrian targets. Instead of predicting the future trajectory directly, we propose to use scene context and observed trajectory to predict the goal points first, and then reuse the goal points to predict the future trajectories. By leveraging the information from scene context and observed trajectory, the uncertainty can be limited to a few target areas, which represent the "goals" of the pedestrians. In this paper, we propose GoalNet, a new trajectory prediction neural network based on the goal areas of a pedestrian. Our network can predict both pedestrian's trajectories and bounding boxes. The overall model is efficient and modular, and its outputs can be changed according to the usage scenario. Experimental results show that GoalNet significantly improves the previous state-of-the-art performance by 48.7% on the JAAD and 40.8% on the PIE dataset.