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: Please tell me how far object <2> is from us. Classify the answer into: (A) Close (0-20m) (B) Medium (20-50m) (C) Far (50-80m) (D) Very far (80m+).
  
  Reasoning protocol (metric 3D):
  1. Identify the target object and its type. If it is beyond ~50 m, flag that type and distance both get unreliable, and cross-check against scene scale.
  2. Estimate its distance using at least THREE independent cues, NOT vertical image position alone: (a) apparent size vs. the real-world size of that object class, (b) the ground-contact point on the road plane and convergence of lane markings toward the vanishing point, (c) occlusion / foreground ordering, (d) scale anchors such as lane width (~3.5 m), vehicle lengths, or sign heights.
  3. Check whether the road is flat, sloped, elevated, or curving; state how that would distort a naive 'lower = closer' reading and correct for it.
  4. Commit to a single distance estimate in meters, then map it to the bucket.
  
  Finish with a single line in the exact format: 'Answer: <LETTER>'.
choices: A. Close (0-20m) B. Medium (20-50m) C. Far (50-80m) D. Very far (80m+)