Datasets:
File size: 2,991 Bytes
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"field_id": "F022",
"field": "autonomous driving",
"directions": [
{
"direction_id": "G0",
"name": "Perception of the surrounding driving scene",
"scope": "Identify the surrounding driving scene from onboard observations or observations shared across vehicles and roadside infrastructure, including cooperative perception, 3D object detection, present spatial semantics and occupancy representations, and drivable-area and road-scene segmentation."
},
{
"direction_id": "G1",
"name": "Recovery of road geometry and structure",
"scope": "Recover lane and road structure from driving observations, including local lane shapes, map elements, vectorized road maps, and connectivity among lanes and traffic elements."
},
{
"direction_id": "G2",
"name": "Vehicle localization against observed and mapped geometry",
"scope": "Determine vehicle position and pose through place recognition, sensor-to-map matching, or geometric mapping developed jointly with localization."
},
{
"direction_id": "G3",
"name": "Tracking and forecasting traffic dynamics",
"scope": "Use observations over time to maintain traffic participants’ identities and motion states and to predict their future behavior or changes in occupied scene space."
},
{
"direction_id": "G4",
"name": "Learning and selecting driving actions",
"scope": "Learn or select driving behavior in road space shared with other traffic participants, including single-vehicle policies, ego-vehicle planning that accounts for predictions and uncertainty, and coordination of multiple vehicles’ passage."
},
{
"direction_id": "G5",
"name": "Generation and predictive modeling of driving worlds",
"scope": "Construct synthetic environments, traffic, and sensor observations for driving research, or learn future driving-scene states and possible action-conditioned outcomes."
},
{
"direction_id": "G6",
"name": "Driving-system testing and safety validation",
"scope": "Use challenging scenarios, requirements, and error models in virtual testing to expose driving- or perception-system failures and assess performance and safety."
},
{
"direction_id": "G7",
"name": "Perception under distribution shift and anomalous conditions",
"scope": "Study perception reliability beyond ordinary training conditions, including transfer across specified environmental or data-domain changes and recognition of anomalous inputs, detection outliers, and rare road hazards."
},
{
"direction_id": "G8",
"name": "Language-grounded driving reasoning and interaction",
"scope": "Connect language with driving scenes, actions, and intentions, both to produce driving decisions from language instructions and to evaluate scene understanding and reasoning expressed through question answering or language explanations."
}
]
}
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