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The CAT Vehicle Testbed: A Simulator with Hardware in the Loop for Autonomous Vehicle Applications
This paper presents the CAT Vehicle (Cognitive and Autonomous Test Vehic...
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Automatic Rule Learning for Autonomous Driving Using Semantic Memory
This paper presents a novel approach for automatic rule learning applica...
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Bridging the Gap between Open Source Software and Vehicle Hardware for Autonomous Driving
Although many research vehicle platforms for autonomous driving have bee...
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Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing
Microscopic traffic simulation provides a controllable, repeatable, and ...
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On Offline Evaluation of Vision-based Driving Models
Autonomous driving models should ideally be evaluated by deploying them ...
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Looking at Hands in Autonomous Vehicles: A ConvNet Approach using Part Affinity Fields
In the context of autonomous driving, where humans may need to take over...
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Towards Corner Case Detection for Autonomous Driving
The progress in autonomous driving is also due to the increased availabi...
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AutoRally An open platform for aggressive autonomous driving
This article presents AutoRally, a 1:5 scale robotics testbed for autonomous vehicle research. AutoRally is designed for robustness, ease of use, and reproducibility, so that a team of two people with limited knowledge of mechanical engineering, electrical engineering, and computer science can construct and then operate the testbed to collect real world autonomous driving data in whatever domain they wish to study. Complete documentation to construct and operate the platform is available online along with tutorials, example controllers, and a driving dataset collected at the Georgia Tech Autonomous Racing Facility. Offline estimation algorithms are used to determine parameters for physics-based dynamics models using an adaptive limited memory joint state unscented Kalman filter. Online vehicle state estimation using a factor graph optimization scheme and a convolutional neural network for semantic segmentation of drivable surface are presented. All algorithms are tested with real world data from the fleet of six AutoRally robots at the Georgia Tech Autonomous Racing Facility tracks, and serve as a demonstration of the robot's capabilities.
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