57 research outputs found
Monte-Carlo Tree Search for Behavior Planning in Autonomous Driving
The integration of autonomous vehicles into urban and highway environments
necessitates the development of robust and adaptable behavior planning systems.
This study presents an innovative approach to address this challenge by
utilizing a Monte-Carlo Tree Search (MCTS) based algorithm for autonomous
driving behavior planning. The core objective is to leverage the balance
between exploration and exploitation inherent in MCTS to facilitate intelligent
driving decisions in complex scenarios.
We introduce an MCTS-based algorithm tailored to the specific demands of
autonomous driving. This involves the integration of carefully crafted cost
functions, encompassing safety, comfort, and passability metrics, into the MCTS
framework. The effectiveness of our approach is demonstrated by enabling
autonomous vehicles to navigate intricate scenarios, such as intersections,
unprotected left turns, cut-ins, and ramps, even under traffic congestion, in
real-time.
Qualitative instances illustrate the integration of diverse driving
decisions, such as lane changes, acceleration, and deceleration, into the MCTS
framework. Moreover, quantitative results, derived from examining the impact of
iteration time and look-ahead steps on decision quality and real-time
applicability, substantiate the robustness of our approach. This robustness is
further underscored by the high success rate of the MCTS algorithm across
various scenarios.Comment: 6 pages, 3 figure
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario
In modern commercial search engines and recommendation systems, data from
multiple domains is available to jointly train the multi-domain model.
Traditional methods train multi-domain models in the multi-task setting, with
shared parameters to learn the similarity of multiple tasks, and task-specific
parameters to learn the divergence of features, labels, and sample
distributions of individual tasks. With the development of large language
models, LLM can extract global domain-invariant text features that serve both
search and recommendation tasks. We propose a novel framework called S\&R
Multi-Domain Foundation, which uses LLM to extract domain invariant features,
and Aspect Gating Fusion to merge the ID feature, domain invariant text
features and task-specific heterogeneous sparse features to obtain the
representations of query and item. Additionally, samples from multiple search
and recommendation scenarios are trained jointly with Domain Adaptive
Multi-Task module to obtain the multi-domain foundation model. We apply the
S\&R Multi-Domain foundation model to cold start scenarios in the
pretrain-finetune manner, which achieves better performance than other SOTA
transfer learning methods. The S\&R Multi-Domain Foundation model has been
successfully deployed in Alipay Mobile Application's online services, such as
content query recommendation and service card recommendation, etc.Comment: CIKM 2023,6 page
Generalized Simple Regenerating Codes: Trading Sub-packetization and Fault Tolerance
Maximum distance separable (MDS) codes have the optimal trade-off between
storage efficiency and fault tolerance, which are widely used in distributed
storage systems. As typical non-MDS codes, simple regenerating codes (SRCs) can
achieve both smaller repair bandwidth and smaller repair locality than
traditional MDS codes in repairing single-node erasure.
In this paper, we propose {\em generalized simple regenerating codes} (GSRCs)
that can support much more parameters than that of SRCs. We show that there is
a trade-off between sub-packetization and fault tolerance in our GSRCs, and
SRCs achieve a special point of the trade-off of GSRCs. We show that the fault
tolerance of our GSRCs increases when the sub-packetization increases linearly.
We also show that our GSRCs can locally repair any singe-symbol erasure and any
single-node erasure, and the repair bandwidth of our GSRCs is smaller than that
of the existing related codes
Scheduling and Airport Taxiway Path Planning Under Uncertainty
Congestion and uncertainty on the airport surface are major constraints to the available capacity of the air transport system. This project is to study the problem of planning and scheduling airport surface movement at large airports. Specifically, we focus on the departure time scheduling and taxiway path planning of multiple aircraft under uncertainty. We also developed a simulation tool that is capable of simulating aircraft movement along the taxiway and possible uncertainty during the movement
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