PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D

Understand

Predicting the behavior of road users, particularly pedestrians, is vital for safe motion planning in the context of autonomous driving systems.

• Traditionally, pedestrian behavior prediction has been realized in terms of forecasting future trajectories.
• However, recent evidence suggests that predicting higher-level actions, such as crossing the road, can help improve trajectory forecasting and planning tasks accordingly.
• There are a number of existing datasets that cater to the development of pedestrian action prediction algorithms, however, they lack certain characteristics, such as bird's eye view semantic map information, 3D locations of objects in the scene, etc., which are crucial in the autonomous driving context.