4 accepted papers
Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects. This leads to performance degradation when 3D detectors trained for one lidar are tested on other types of lidars. Remarkable progress in lidar manufacturing has brou
Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex when we consider the uncertainty and multimodality of pedestrian motion, as well as the implicit interactions between m
Semantic segmentation using deep neural networks has been widely explored to generate high-level contextual information for autonomous vehicles. To acquire a complete 180° semantic understanding of the forward surroundings, we propose to stitch semantic images from multiple cameras with varying orie
In this letter, we present a system capable of inferring intent from observed vehicles traversing an unsignalized intersection, a task critical for the safe driving of autonomous vehicles, and beneficial for advanced driver assistance systems. We present a prediction method based on recurrent neural