4,090 research outputs found
Local Gaussian processes for efficient fine-grained traffic speed prediction
National Research Foundation (NRF) Singapore under International Research Centre @ Singapore Funding Initiativ
Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing
Short-term traffic speed prediction has been an important research topic in
the past decade, and many approaches have been introduced. However, providing
fine-grained, accurate, and efficient traffic-speed prediction for large-scale
transportation networks where numerous traffic detectors are deployed has not
been well studied. In this paper, we propose DistPre, which is a distributed
fine-grained traffic speed prediction scheme for large-scale transportation
networks. To achieve fine-grained and accurate traffic-speed prediction,
DistPre customizes a Long Short-Term Memory (LSTM) model with an appropriate
hyperparameter configuration for a detector. To make such customization process
efficient and applicable for large-scale transportation networks, DistPre
conducts LSTM customization on a cluster of computation nodes and allows any
trained LSTM model to be shared between different detectors. If a detector
observes a similar traffic pattern to another one, DistPre directly shares the
existing LSTM model between the two detectors rather than customizing an LSTM
model per detector. Experiments based on traffic data collected from freeway
I5-N in California are conducted to evaluate the performance of DistPre. The
results demonstrate that DistPre provides time-efficient LSTM customization and
accurate fine-grained traffic-speed prediction for large-scale transportation
networks.Comment: 14 pages, 7 figures, 2 tables, Euro-par 2020 conferenc
Multi-resolution Tensor Learning for Large-Scale Spatial Data
High-dimensional tensor models are notoriously computationally expensive to
train. We present a meta-learning algorithm, MMT, that can significantly speed
up the process for spatial tensor models. MMT leverages the property that
spatial data can be viewed at multiple resolutions, which are related by
coarsening and finegraining from one resolution to another. Using this
property, MMT learns a tensor model by starting from a coarse resolution and
iteratively increasing the model complexity. In order to not "over-train" on
coarse resolution models, we investigate an information-theoretic fine-graining
criterion to decide when to transition into higher-resolution models. We
provide both theoretical and empirical evidence for the advantages of this
approach. When applied to two real-world large-scale spatial datasets for
basketball player and animal behavior modeling, our approach demonstrate 3 key
benefits: 1) it efficiently captures higher-order interactions (i.e., tensor
latent factors), 2) it is orders of magnitude faster than fixed resolution
learning and scales to very fine-grained spatial resolutions, and 3) it
reliably yields accurate and interpretable models
Learning to Drive Anywhere
Human drivers can seamlessly adapt their driving decisions across
geographical locations with diverse conditions and rules of the road, e.g.,
left vs. right-hand traffic. In contrast, existing models for autonomous
driving have been thus far only deployed within restricted operational domains,
i.e., without accounting for varying driving behaviors across locations or
model scalability. In this work, we propose AnyD, a single geographically-aware
conditional imitation learning (CIL) model that can efficiently learn from
heterogeneous and globally distributed data with dynamic environmental,
traffic, and social characteristics. Our key insight is to introduce a
high-capacity geo-location-based channel attention mechanism that effectively
adapts to local nuances while also flexibly modeling similarities among regions
in a data-driven manner. By optimizing a contrastive imitation objective, our
proposed approach can efficiently scale across inherently imbalanced data
distributions and location-dependent events. We demonstrate the benefits of our
AnyD agent across multiple datasets, cities, and scalable deployment paradigms,
i.e., centralized, semi-supervised, and distributed agent training.
Specifically, AnyD outperforms CIL baselines by over 14% in open-loop
evaluation and 30% in closed-loop testing on CARLA.Comment: Conference on Robot Learning (CoRL) 202
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