121 research outputs found
Towards Sim2Real Transfer of Autonomy Algorithms using AutoDRIVE Ecosystem
The engineering community currently encounters significant challenges in the
development of intelligent transportation algorithms that can be transferred
from simulation to reality with minimal effort. This can be achieved by
robustifying the algorithms using domain adaptation methods and/or by adopting
cutting-edge tools that help support this objective seamlessly. This work
presents AutoDRIVE, an openly accessible digital twin ecosystem designed to
facilitate synergistic development, simulation and deployment of cyber-physical
solutions pertaining to autonomous driving technology; and focuses on bridging
the autonomy-oriented simulation-to-reality (sim2real) gap using the proposed
ecosystem. In this paper, we extensively explore the modeling and simulation
aspects of the ecosystem and substantiate its efficacy by demonstrating the
successful transition of two candidate autonomy algorithms from simulation to
reality to help support our claims: (i) autonomous parking using probabilistic
robotics approach; (ii) behavioral cloning using deep imitation learning. The
outcomes of these case studies further strengthen the credibility of AutoDRIVE
as an invaluable tool for advancing the state-of-the-art in autonomous driving
technology.Comment: Accepted at AACC/IFAC Modeling, Estimation and Control Conference
(MECC) 202
Design of a Wheelchair with Legs for People with Motor Disabilities
A proof-of-concept prototype wheelchair with legs for people with motor disabilities is proposed, with the objective of demonstrating the feasibility of a completely new approach to mobility. Our prototype system consists of a chair equipped with wheels and legs, and is capable of traversing uneven terrain and circumventing obstacles. The important design considerations, the system design and analysis, and an experimental prototype of a chair are discussed. The results from the analysis and experimentation show the feasibility of the proposed concept and its advantages
CLC: Cluster Assignment via Contrastive Representation Learning
Clustering remains an important and challenging task of grouping samples into
clusters without manual annotations. Recent works have achieved excellent
results on small datasets by performing clustering on feature representations
learned from self-supervised learning. However, for datasets with a large
number of clusters, such as ImageNet, current methods still can not achieve
high clustering performance. In this paper, we propose Contrastive
Learning-based Clustering (CLC), which uses contrastive learning to directly
learn cluster assignment. We decompose the representation into two parts: one
encodes the categorical information under an equipartition constraint, and the
other captures the instance-wise factors. We propose a contrastive loss using
both parts of the representation. We theoretically analyze the proposed
contrastive loss and reveal that CLC sets different weights for the negative
samples while learning cluster assignments. Further gradient analysis shows
that the larger weights tend to focus more on the hard negative samples.
Therefore, the proposed loss has high expressiveness that enables us to
efficiently learn cluster assignments. Experimental evaluation shows that CLC
achieves overall state-of-the-art or highly competitive clustering performance
on multiple benchmark datasets. In particular, we achieve 53.4% accuracy on the
full ImageNet dataset and outperform existing methods by large margins (+
10.2%).Comment: 10 pages, 7 tables, 4 figure
AutoDRIVE: A Comprehensive, Flexible and Integrated Cyber-Physical Ecosystem for Enhancing Autonomous Driving Research and Education
Prototyping and validating hardware-software components, sub-systems and
systems within the intelligent transportation system-of-systems framework
requires a modular yet flexible and open-access ecosystem. This work presents
our attempt towards developing such a comprehensive research and education
ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and
deploying cyber-physical solutions pertaining to autonomous driving as well as
smart city management. AutoDRIVE features both software as well as
hardware-in-the-loop testing interfaces with openly accessible scaled vehicle
and infrastructure components. The ecosystem is compatible with a variety of
development frameworks, and supports both single and multi-agent paradigms
through local as well as distributed computing. Most critically, AutoDRIVE is
intended to be modularly expandable to explore emergent technologies, and this
work highlights various complementary features and capabilities of the proposed
ecosystem by demonstrating four such deployment use-cases: (i) autonomous
parking using probabilistic robotics approach for mapping, localization, path
planning and control; (ii) behavioral cloning using computer vision and deep
imitation learning; (iii) intersection traversal using vehicle-to-vehicle
communication and deep reinforcement learning; and (iv) smart city management
using vehicle-to-infrastructure communication and internet-of-things
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