Modeling Naturalistic Driving Environment with High-Resolution Trajectory Data
dc.contributor.author | Feng, Shuo | en_US |
dc.contributor.author | Yan, Xintao | en_US |
dc.contributor.author | Liu, Henry | en_US |
dc.date.accessioned | 2024-02-29T18:22:24Z | |
dc.date.issued | 2024-02-29 | |
dc.identifier | UMTRI-2024-2 | en_US |
dc.identifier.citation | Feng, S., Yan, X., & Liu, H.X. (2023). Modeling Naturalistic Driving Environment with High-Resolution Trajectory Data. Final Report. UMTRI-2024-2. | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/192512 | |
dc.description | Final Report | en_US |
dc.description | Learning Naturalistic Driving Environment Code | en_US |
dc.description | Learning Naturalistic Driving Environment Data | en_US |
dc.description.abstract | For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve statistical realism in simulation is a long-standing problem. In this project, we develop NeuralNDE, a deep learning-based framework to learn multi-agent interaction behavior from high-resolution vehicle trajectory data, and propose a conflict critic model and a safety mapping network to refine the generation process of safety-critical events, following real-world occurring frequencies and patterns. The results show thatNeuralNDE can achieve both accurate safety-critical driving statistics (e.g., crash rate/type/severity and near-miss statistics, etc.)and normal driving statistics (e.g., vehicle speed/distance/yielding behavior distributions, etc.), as demonstrated in the simulation of urban driving environments. | en_US |
dc.description.sponsorship | U.S. Department of Transportation Office of the Assistant Secretary for Research and Technology (O-STR) | en_US |
dc.format.extent | 42 | en_US |
dc.language | English | en_US |
dc.publisher | Center for Connected and Automated Transportation (CCAT) | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Modeling Naturalistic Driving Environment with High-Resolution Trajectory Data | en_US |
dc.type | Technical Report | |
dc.subject.hlbsecondlevel | Transportation | |
dc.subject.hlbtoplevel | Engineering | |
dc.contributor.affiliationum | University of Michigan Transportation Research Institute | |
dc.contributor.affiliationumcampus | Ann Arbor | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/192512/1/Modeling Naturalistic Driving Environment with High-Resolution Trajectory Data Final Report [Accessible].pdf | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/192512/2/Learning Naturalistic Driving Environment Code.zip | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/192512/3/Learning Naturalistic Driving Environment Data.zip | |
dc.identifier.doi | https://dx.doi.org/10.7302/22417 | |
dc.description.mapping | d520799f-6680-473f-9234-2ec9840d05bb | en_US |
dc.identifier.orcid | 0000-0002-2117-4427 | en_US |
dc.identifier.orcid | 0000-0002-0569-5628 | en_US |
dc.identifier.orcid | 0000-0002-3685-9920 | en_US |
dc.description.filedescription | Description of Modeling Naturalistic Driving Environment with High-Resolution Trajectory Data Final Report [Accessible].pdf : Final Report | |
dc.description.filedescription | Description of Learning Naturalistic Driving Environment Code.zip : Learning Naturalistic Driving Environment Code | |
dc.description.filedescription | Description of Learning Naturalistic Driving Environment Data.zip : Learning Naturalistic Driving Environment Data | |
dc.identifier.name-orcid | Feng, Shuo; 0000-0002-2117-4427 | en_US |
dc.identifier.name-orcid | Yan, Xintao; 0000-0002-0569-5628 | en_US |
dc.identifier.name-orcid | Liu, Henry; 0000-0002-3685-9920 | en_US |
dc.working.doi | 10.7302/22417 | en_US |
dc.owningcollname | Transportation Research Institute (UMTRI) |
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