Autonomous Vehicles | ScienceGate

Abstract The freight transport sector is a low profit and high competition business and therefore has less ability to invest in research and development in the field of autonomous vehicles (AV) than the private car industry. There are already different levels of automation technologies in the transport industry, but most of these are serving niche demands and answers have yet to be found about whether it would be worthwhile to industrialise these technologies. New innovations from different fields are constantly changing the freight traffic industry but these are less disruptive than on other markets. The aim of this article is to show the current state of development of freight traffic with regards to AVs and analyse which future directions of development might be viable. The level of automation is very different in the case of different transport modes and most probably the technology will favour road transport over other, less environmentally harmful traffic modes.

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A Study of Interpretation of Private liability for Accidents led by Level 3 Autonomous Vehicles

BUSINESS LAW REVIEW ◽

10.24886/blr.2019.3.33.1.179 ◽

2019 ◽

Vol 33 (1) ◽

pp. 179-207

Author(s):

Se-Min Park

Keyword(s):

Autonomous Vehicles ◽

Level 3

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Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

10.26434/chemrxiv.11819268.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Christian Devereux ◽

Justin Smith ◽

Kate Davis ◽

Kipton Barros ◽

Roman Zubatyuk ◽

...

Keyword(s):

Drug Development ◽

Autonomous Vehicles ◽

Potential Energy Surfaces ◽

Organic Molecules ◽

Chemical Elements ◽

Molecular Potential ◽

Wide Range ◽

Energy Surfaces ◽

New Applications ◽

Physical Phenomena

<p>Machine learning (ML) methods have become powerful, predictive tools in a wide range of applications, such as facial recognition and autonomous vehicles. In the sciences, computational chemists and physicists have been using ML for the prediction of physical phenomena, such as atomistic potential energy surfaces and reaction pathways. Transferable ML potentials, such as ANI-1x, have been developed with the goal of accurately simulating organic molecules containing the chemical elements H, C, N, and O. Here we provide an extension of the ANI-1x model. The new model, dubbed ANI-2x, is trained to three additional chemical elements: S, F, and Cl. Additionally, ANI-2x underwent torsional refinement training to better predict molecular torsion profiles. These new features open a wide range of new applications within organic chemistry and drug development. These seven elements (H, C, N, O, F, Cl, S) make up ~90% of drug like molecules. To show that these additions do not sacrifice accuracy, we have tested this model across a range of organic molecules and applications, including the COMP6 benchmark, dihedral rotations, conformer scoring, and non-bonded interactions. ANI-2x is shown to accurately predict molecular energies compared to DFT with a ~10<sup>6</sup> factor speedup and a negligible slowdown compared to ANI-1x. The resulting model is a valuable tool for drug development that can potentially replace both quantum calculations and classical force fields for myriad applications.</p>

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