A formal road lane network model is presented and proves that any structured road scene can be represented by a directed acyclic graph of at most depth three while retaining the notion of intersection regions, and that this is the most compressed representation.
Our results showed that multi-task learning using binary classification and metric learning to consider the distance from each class centroid in the feature space is effective, and performance can be significantly improved by using even a small amount of anomalous data during training.
And this provides us a great opportunity to think about how shall these data be organized and exploited.
Extracting the rules of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields.
In this work, we present a detailed comparison of ten different 3D LiDAR sensors, covering a range of manufacturers, models, and laser configurations, for the tasks of mapping and vehicle localization, using as common reference the Normal Distributions Transform (NDT) algorithm implemented in the self-driving open source platform Autoware.
In this work, we present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors, covering a range of manufacturers, models, and laser configurations.
Furthermore, the unified design enables the integration of ASR functions with TTS, e. g., ASR-based objective evaluation and semi-supervised learning with both ASR and TTS models.
Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience.
Robotics Systems and Control Systems and Control
The best result, with a 0. 937 AUC score, was obtained with the proposed network.
This paper deals with a multichannel audio source separation problem under underdetermined conditions.
In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals.
This paper presents a new network architecture called multi-head decoder for end-to-end speech recognition as an extension of a multi-head attention model.
In this paper, we analyze the causes of task completion errors in spoken dialog systems, using a decision tree with N-gram features of the dialog to detect task-incomplete dialogs.