VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation [Paper Explained]

Future motion prediction is a task of paramount importance for autonomous driving. For a self-driving car to safely operate it is crucial to anticipate the actions of other agents on the road. In this video, I explain one of the methods for future motion prediction based on the vectorized representation of the scene instead of RGB images. Paper: Waymo blogpost Abstract: Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road components, including moving agents (e.g. pedestrians and vehicles) and road context information (e.g. lanes, traffic lights). This paper introduces VectorNet, a hierarchical graph neural network that first exploits the spatial locality of individual road components represented by vectors and then models the high-order interactions among all components. In contrast to most recent approaches, wh