International Large-Scale Vehicle Corpora for Research on Driver Behavior on the Road

By Kazuya Takeda, John H. L. Hansen, Pinar Boyraz, L. Malta, Chiyomi Miyajima, H. Abut. 2011

77 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Ekim Yurtsever
2. Kazuya Takeda
3. Jacob Lambert
4. Alexander Carballo
5. Chiyomi Miyajima
6. Eijiro Takeuchi

In Computer Vision and Pattern Recognition

1. Ekim Yurtsever
2. Jacob Lambert
3. Alexander Carballo
4. Kazuya Takeda
5. John H. L. Hansen
6. Chiyomi Miyajima
7. Carlos Busso
8. Eijiro Takeuchi

In Transportation

1. Ekim Yurtsever
2. Kazuya Takeda
3. Chiyomi Miyajima
4. Alexander Carballo
5. Jacob Lambert

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “Cityscapes [270] X X Vehicle Berkley DeepDrive [271] X X Vehicle X X Mapillary [272] X X Vehicle X X Oxford RobotCar [49] X X Vehicle X X X KITTI [174] X X X X Vehicle H3D [273] X X X Vehicle ApolloScape [274] X X X X Vehicle nuScenes [175] X X X X Vehicle X X Udacity [275] X X X X Vehicle DDD17 [85”
• Driver Modeling for Detection and Assessment of Driver Distraction: Examples from the UTDrive Test Bed (2017, background): “, Strategic Highway Research Program 2 and New Energy and Industrial Technology Development [22], [23]), the development of automatic tools to organize, prune, and cluster drivercentric-based events for driver modeling is a growing research topic.”
• A Traffic Flow Simulation Framework for Learning Driver Heterogeneity from Naturalistic Driving Data using Autoencoders (2019, background): “The first one comprises detailed ego-behavior signals (19)-(21) such as steering and pedal operations, which are crucial for learning particular behavior patterns.”
• Integrating driving behavior and traffic context through signal symbolization (2016, methodology): “Corpus In this study, we used expressway-driving data from NUDrive corpus [16].”
• Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer (2019, background): “Details of the corpus can be found in [33].”
• Integrating Driving Behavior and Traffic Context Through Signal Symbolization for Data Reduction and Risky Lane Change Detection (2018, methodology): “We used NUDrive corpus [5] in our experiments.”