question: You are answering a 3D spatial-reasoning question from a SINGLE monocular driving image with numbered bounding boxes. Frontier models routinely get these right by luck while reasoning incorrectly: they lean on flat-image shortcuts ('lower in the frame = closer', 'bigger box = nearer', 'left in the image = to my left') that collapse on elevated roads, slopes, curves, and intersections. To answer reliably you must reconstruct the real 3D scene, not the pixel layout. Reason explicitly and quantitatively.
  
  Question: Specify the type of object <4>.
  
  Reasoning protocol (range-robust classification):
  1. Estimate the object's distance first. Past ~50 m, type confusion (suv / car / van / pickup) is the dominant error; flag it explicitly.
  2. Use shape cues that survive distance: roofline length and height, a cargo bed (pickup), a tall boxy cargo body (van), raised ride height and long roof (suv), a three-box low profile (sedan/car), and axle count for large vehicles.
  3. Calibrate apparent size against a lane width (~3.5 m) or a nearby known vehicle.
  4. Choose the type whose real 3D proportions match; do NOT default to 'car' when the object is small or distant. State the decisive cues before answering.
  
  Finish with a single line in the exact format: 'Answer: <LETTER>'.
choices: A. car B. pedestrian C. suv D. light_truck