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Open AccessJournal ArticleDOI

Life in the Fast Lane: The Evolution of an Adaptive Vehicle Control System

Todd Jochem, +1 more
- 15 Mar 1996 - 
- Vol. 17, Iss: 2, pp 11-50
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TLDR
This article describes the evolution of this system from a research project in machine learning to a robust driving system capable of executing tactical driving maneuvers such as lane changing and intersection navigation.
Abstract
Giving robots the ability to operate in the real world has been, and continues to be, one of the most difficult tasks in AI research. Since 1987, researchers at Carnegie Mellon University have been investigating one such task. Their research has been focused on using adaptive, vision-based systems to increase the driving performance of the Navlab line of on-road mobile robots. This research has led to the development of a neural network system that can learn to drive on many road types simply by watching a human teacher. This article describes the evolution of this system from a research project in machine learning to a robust driving system capable of executing tactical driving maneuvers such as lane changing and intersection navigation.

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Citations
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Journal ArticleDOI

The spatial semantic hierarchy

TL;DR: The assumptions and guarantees behind the generality of the SSH across environments and sensorimotor systems are described and evidence is presented from several partial implementations of the ssh on simulated and physical robots.
Journal ArticleDOI

Machine learning, social learning and the governance of self-driving cars:

TL;DR: Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, it is argued that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge.
Proceedings ArticleDOI

Making Bertha See

TL;DR: Details of the employed vision algorithms for object recognition and tracking, free-space analysis, traffic light recognition, lane recognition, as well as self-localization are presented.
Book ChapterDOI

Interest Point Detector and Feature Descriptor Survey

Scott Krig
TL;DR: The interest point is the keypoints in each image, and often provides the scale, rotational, and illumination invariance attributes for the descriptor; the descriptor adds more detail and more invariant attributes.
Journal ArticleDOI

Tradeoffs Between Directed and Autonomous Driving on the Mars Exploration Rovers

TL;DR: The strategies adopted for selecting between human-planned Directed drives versus rover-adaptive Autonomous Navigation, Visual Odometry and Path Selection drives are described.
References
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Book ChapterDOI

Learning internal representations by error propagation

TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book

Learning internal representations by error propagation

TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Book

A robust layered control system for a mobile robot

TL;DR: A new architecture for controlling mobile robots is described, building a robust and flexible robot control system that has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms.
Journal ArticleDOI

A robust layered control system for a mobile robot

TL;DR: In this paper, a new architecture for controlling mobile robots is described, which is made up of asynchronous modules that communicate over low-bandwidth channels, each module is an instance of a fairly simple computational machine.
Book

Neural Network Perception for Mobile Robot Guidance

TL;DR: This book describes a connectionist system called ALVINN (Autonomous Land Vehicle In a Neural Network) that overcomes difficulties and can learn to control an autonomous van in under 5 minutes by watching a person drive.
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