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		{
			"accessLevel": "public",
			"bureauCode": [
				"021:04"
			],
			"contactPoint": {
				"fn": "National Transportation Library Data Curator",
				"hasEmail": "mailto:ntldatacurator@dot.gov"
			},
			"dataQuality": true,
			"description": "Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability. In this project we developed a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against several benchmarks. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision-making process and correct model errors attributable to limited training data.",
			"distribution": [
				{
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					"accessURL": "https://doi.org/10.15787/VTT1/LWA2VP",
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					"description": "Manifest document containing file list.",
					"title": "MANIFEST.TXT"
				},
				{
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					"accessURL": "https://doi.org/10.15787/VTT1/LWA2VP",
					"format": "CSV",
					"mediaType": "text/csv",
					"description": "In the feature folder.",
					"title": "vehicle_tracks_000.csv"
				},
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					"mediaType": "text/csv",
					"description": "In the feature folder.",
					"title": "vehicle_tracks_003.csv"
				},
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					"accessURL": "https://doi.org/10.15787/VTT1/LWA2VP",
					"format": "CSV",
					"mediaType": "text/csv",
					"description": "In the feature folder.",
					"title": "vehicle_tracks_007.csv"
				},
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					"accessURL": "https://doi.org/10.15787/VTT1/LWA2VP",
					"format": "CSV",
					"mediaType": "text/csv",
					"description": "In the train labels folder.",
					"title": "vehicle_tracks_007.csv"
				},
				{
					"@type": "dcat:Distribution",
					"accessURL": "https://doi.org/10.15787/VTT1/LWA2VP",
					"format": "CSV",
					"mediaType": "text/csv",
					"description": "In the train labels folder.",
					"title": "vehicle_tracks_003.csv"
				},
				{
					"@type": "dcat:Distribution",
					"accessURL": "https://doi.org/10.15787/VTT1/LWA2VP",
					"format": "CSV",
					"mediaType": "text/csv",
					"description": "In the train labels folder.",
					"title": "vehicle_tracks_000.csv"
				},
				{
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					"accessURL": "https://doi.org/10.15787/VTT1/LWA2VP",
					"format": "PDF",
					"mediaType": "application/pdf",
					"description": "Contains file information about dataset and GitHub files",
					"title": "Safe-D_06-009_Description of track data.pdf"
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			"format": "ZIP",
			"identifier": "https://doi.org/10.15787/VTT1/LWA2VP",
			"issued": "2023-12-07",
			"keyword": [
				"Automated vehicle control",
				"Driver monitoring",
				"Behavior",
				"Driver support systems",
				"Predictive models",
				"Vehicle safety",
				"Perception",
				"Markov processes",
				"Statistical inference",
				"Car following"
			],
			"landingPage": "https://doi.org/10.15787/VTT1/LWA2VP",
			"language": [
				"en-US"
			],
			"license": "https://creativecommons.org/publicdomain/zero/1.0/legalcodee",
			"modified": "2023-12-07",
			"policyStatement": "This dataset was made public under the requirements enumerated in the U.S. Department of Transportation's 'Plan to Increase Public Access to the Results of Federally-Funded Scientific Research' Version 1.1 << https://doi.org/10.21949/1520559 >> and guidelines suggested by the DOT Public Access website << https://doi.org/10.21949/1503647  >>, in effect and current as of December 03, 2020.",
			"policyURL": "https://doi.org/10.21949/1520559 , https://doi.org/10.21949/1503647 ",
			"programCode": [
				"021:053"
			],
			"publisher": {
				"@type": "org:Organization",
				"name": "Safety through Disruption (Safe-D) University Transportation Center ",
				"subOrganizationOf": {
					"@type": "org:Organization",
					"name": "Office of the Assistant Secretary for Research and Technology",
					"subOrganizationOf": {
						"@type": "org:Organization",
						"name": "Department of Transportation",
						"subOrganizationOf": {
							"@type": "org:Organization",
							"name": "U.S. Government"
						}
					}
				}
			},
			"references": [
				"https://rosap.ntl.bts.gov/view/dot/73296", "https://github.com/ran-weii/interactive_inference"
			],
			"spatial": "United States",
			"title": "Enhancing Automated Vehicle Safety Through Testing with Realistic Driver Models [supporting dataset]",
			"webService": null
		}
	]
}