36,161 research outputs found
Some remarks on wheeled autonomous vehicles and the evolution of their control design
Recent investigations on the longitudinal and lateral control of wheeled
autonomous vehicles are reported. Flatness-based techniques are first
introduced via a simplified model. It depends on some physical parameters, like
cornering stiffness coefficients of the tires, friction coefficient of the
road, ..., which are notoriously difficult to identify. Then a model-free
control strategy, which exploits the flat outputs, is proposed. Those outputs
also depend on physical parameters which are poorly known, i.e., the vehicle
mass and inertia and the position of the center of gravity. A totally
model-free control law is therefore adopted. It employs natural output
variables, namely the longitudinal velocity and the lateral deviation of the
vehicle. This last method, which is easily understandable and implementable,
ensures a robust trajectory tracking problem in both longitudinal and lateral
dynamics. Several convincing computer simulations are displayed.Comment: 9th IFAC Symposium on Intelligent Autonomous Vehicles (Leipzig,
Germany, 29.06.2016 - 01.07.2016
Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera
Understanding ego-motion and surrounding vehicle state is essential to enable
automated driving and advanced driving assistance technologies. Typical
approaches to solve this problem use fusion of multiple sensors such as LiDAR,
camera, and radar to recognize surrounding vehicle state, including position,
velocity, and orientation. Such sensing modalities are overly complex and
costly for production of personal use vehicles. In this paper, we propose a
novel machine learning method to estimate ego-motion and surrounding vehicle
state using a single monocular camera. Our approach is based on a combination
of three deep neural networks to estimate the 3D vehicle bounding box, depth,
and optical flow from a sequence of images. The main contribution of this paper
is a new framework and algorithm that integrates these three networks in order
to estimate the ego-motion and surrounding vehicle state. To realize more
accurate 3D position estimation, we address ground plane correction in
real-time. The efficacy of the proposed method is demonstrated through
experimental evaluations that compare our results to ground truth data
available from other sensors including Can-Bus and LiDAR
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Therapeutic Effect of Targeting Branched-Chain Amino Acid Catabolic Flux in Pressure-Overload Induced Heart Failure.
Background Branched-chain amino acid (BCAA) catabolic defect is an emerging metabolic hallmark in failing hearts in human and animal models. The therapeutic impact of targeting BCAA catabolic flux under pathological conditions remains understudied. Methods and Results BT2 (3,6-dichlorobenzo[b]thiophene-2-carboxylic acid), a small-molecule inhibitor of branched-chain ketoacid dehydrogenase kinase, was used to enhance BCAA catabolism. After 2Â weeks of transaortic constriction, mice with significant cardiac dysfunctions were treated with vehicle or BT2. Serial echocardiograms showed continuing pathological deterioration in left ventricle of the vehicle-treated mice, whereas the BT2-treated mice showed significantly preserved cardiac function and structure. Moreover, BT2 treatment improved systolic contractility and diastolic mechanics. These therapeutic benefits appeared to be independent of impacts on left ventricle hypertrophy but associated with increased gene expression involved in fatty acid utilization. The BT2 administration showed no signs of apparent toxicity. Conclusions Our data provide the first proof-of-concept evidence for the therapeutic efficacy of restoring BCAA catabolic flux in hearts with preexisting dysfunctions. The BCAA catabolic pathway represents a novel and potentially efficacious target for treatment of heart failure
A state-of-the-art review on torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains
© 2019, Levrotto and Bella. All rights reserved. Electric vehicles are the future of private passenger transportation. However, there are still several technological barriers that hinder the large scale adoption of electric vehicles. In particular, their limited autonomy motivates studies on methods for improving the energy efficiency of electric vehicles so as to make them more attractive to the market. This paper provides a concise review on the current state-of-the-art of torque distribution strategies aimed at enhancing energy efficiency for fully electric vehicles with independently actuated drivetrains (FEVIADs). Starting from the operating principles, which include the "control allocation" problem, the peculiarities of each proposed solution are illustrated. All the existing techniques are categorized based on a selection of parameters deemed relevant to provide a comprehensive overview and understanding of the topic. Finally, future concerns and research perspectives for FEVIAD are discussed
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