Who Cited It

ALVINN, an autonomous land vehicle in a neural network

2018 · 1,429 citations · 4 from inside this corpus

Dean Pomerleau low

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

ALVINN, an autonomous land vehicle in a neural network (2018)ALVINN, an autonomous land ve…Deep Reinforcement Learning: A Brief Survey (2017)Deep Reinforcement Learning: …Apprenticeship learning via inverse reinforcement learning (2004)Apprenticeship learning via i…Self-improving reactive agents based on reinforcement learning, planning and teaching (1992)Self-improving reactive agent…End-to-End Training of Deep Visuomotor Policies (2015)End-to-End Training of Deep V…
4 of 4 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
this paper works it cites works citing it node size = global citations · hover for the full title

What cites it, inside the corpus

PaperYearCited
Deep Reinforcement Learning: A Brief Survey20174,434
Apprenticeship learning via inverse reinforcement learning20042,895
Self-improving reactive agents based on reinforcement learning, planning and teaching19921,656
End-to-End Training of Deep Visuomotor Policies20151,400

Links

DOI · Open access copy · OpenAlex record

Topics

Neural Networks and ApplicationsComputer Science
Autonomous Vehicle Technology and SafetyEngineering
Target Tracking and Data Fusion in Sensor NetworksComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports1 author record(s) attached.
  • supports7 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:59+00:00.

sha256 59007f057c7dae86…