Corpus ID: 52916832

Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy

@article{Fridman2018HumanCenteredAV,
  title={Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy},
  author={Alex Fridman},
  journal={ArXiv},
  year={2018},
  volume={abs/1810.01835}
}
Building e�ffective, enjoyable, and safe autonomous vehicles is a lot harder than has historically been considered. �The reason is that, simply put, an autonomous vehicle must interact with human beings. �This interaction is not a robotics problem nor a machine learning problem nor a psychology problem nor an economics problem nor a policy problem. It is all of these problems put into one. It challenges our assumptions about the limitations of human beings at their worst and the capabilities of… Expand
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References

SHOWING 1-10 OF 29 REFERENCES
MIT Autonomous Vehicle Technology Study: Large-Scale Deep Learning Based Analysis of Driver Behavior and Interaction with Automation
Today, and possibly for a long time to come, the full driving task is too complex an activity to be fully formalized as a sensing-acting robotics system that can be explicitly solved throughExpand
Arguing Machines: Perception-Control System Redundancy and Edge Case Discovery in Real-World Autonomous Driving
TLDR
This work proposes and evaluates a method for discovering edge cases by monitoring the disagreement between two monocular-vision-based automated steering systems that are equipped in the first generation of Autopilot-capable vehicles. Expand
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles
TLDR
The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting and to gain insight into the strengths and limitations of the reviewed approaches. Expand
Collaborative control: a robot-centric model for vehicle teleoperation
Keywords: mobile robots ; teleoperation ; human-robot interaction ; [VRAI] Note: Carnegie Mellon University Reference LSRO2-REPORT-2001-002 Record created on 2005-02-04, modified on 2017-05-10
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions
TLDR
An “arguing machines” framework that pairs the primary AI system with a secondary one that is independently trained to perform the same task is proposed and it is shown that disagreement between the two systems is sufficient to improve the accuracy of the overall system given human supervision over disagreements. Expand
The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, George Air Force Base, Victorville, California, USA
This volume, edited by Martin Buehler, Karl Iagnemma and Sanjiv Singh, presents a unique and comprehensive collection of the scientific results obtained by finalist teams that participated in theExpand
Humans and Automation: System Design and Research Issues
From the Publisher: Human factors, also known as human engineering or human factors engineering, is the application of behavioral and biological sciences to the design of machines and human-machineExpand
Robust vehicle localization in urban environments using probabilistic maps
TLDR
This work proposes an extension to this approach to vehicle localization that yields substantial improvements over previous work in vehicle localization, including higher precision, the ability to learn and improve maps over time, and increased robustness to environment changes and dynamic obstacles. Expand
To Walk or Not to Walk: Crowdsourced Assessment of External Vehicle-to-Pedestrian Displays
TLDR
An automated online methodology for obtaining communication intent perceptions for 30 external vehicle-to-pedestrian display concepts was implemented and tested using Amazon Mechanic Turk and demonstrated that the methodology is scalable so that a large number of design elements or minor variations can be assessed through a series of runs even on much larger samples in a matter of hours. Expand
End to End Learning for Self-Driving Cars
TLDR
A convolutional neural network is trained to map raw pixels from a single front-facing camera directly to steering commands and it is argued that this will eventually lead to better performance and smaller systems. Expand
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