A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset
By Benjamin Coifman, Lizhe Li. 2017
385 citations recorded by the source corpus.
Subfields: Automotive Engineering, Building and Construction, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/3d770027aed1c787e3890acc378f11e5d720868b
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
- Hang Zhao
- Benjamin Sapp
- Wei Zhan
- Charles Ruizhongtai Qi
- Masayoshi Tomizuka
- Christoph Stiller
- Jiquan Ngiam
- Vijay Vasudevan
In Building and Construction
In Control and Systems Engineering
- Charles Ruizhongtai Qi
- Masayoshi Tomizuka
- Jonathon Shlens
- Jiquan Ngiam
- Yin Zhou
- Yuning Chai
- Hang Zhao
- Benjamin Sapp
Citations in the corpus
- Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset (2021, background): “We also note there are a host of other motion forecasting datasets which, while popular, are orders of magnitude smaller, have O(10) unique locations, and/or are not focused on driving environment, for example the Stanford Drone Dataset [30], NGSIM [10], ETH [24], UCY [21], Town Center [2].”
- Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks (2018, background): “from considerable tracking noise [20], and even with aggressive filtering and correction techniques, significant problems still exist such as multiple collisions [21] that do not exist in the real-world sample.”
- INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps (2019, methodology): “Researchers proposed methods [21] to rectify the errors, but it can only improve the quality of a small part of the dataset.”