Cut-In Gap Acceptance Toward Autonomous vs. Human-Driven Vehicles: Evidence from the Waymo Open Motion Dataset

arXiv cs.RO / 5/5/2026

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Key Points

  • The study tests whether drivers accept shorter cut-in (lane-change) gaps when the target is an autonomous vehicle (AV) rather than another human-driven vehicle (HDV), using real-world data from the Waymo Open Motion Dataset (WOMD).
  • From the same traffic environments, the authors detect 706 HDV-to-AV and 3,172 HDV-to-HDV cut-in events using an eight-criterion lane-change detector.
  • Drivers accept a significantly shorter median longitudinal gap in front of the Waymo AV (7.58 m) than in front of HDV targets (9.57 m), and this difference remains after speed-matched resampling.
  • Cut-ins toward the AV also happen at higher speeds and more frequently fall below the 10-meter gap threshold, indicating a consistent behavioral asymmetry that is relevant for safety.
  • The findings imply AV-specific calibration is needed for both motion-planning safety envelopes and traffic simulation models to reflect how humans actually behave toward AVs.

Abstract

Autonomous vehicles (AVs) are widely known to follow conservative, rule-based motion policies that surrounding drivers can learn to anticipate. A direct consequence is that human drivers may accept shorter longitudinal gaps when cutting in front of an AV than when targeting another human-driven vehicle (HDV). We test this hypothesis using the Waymo Open Motion Dataset (WOMD), which provides 25,906 real-world highway scenarios at 10 hertz. An eight-criterion lane-change detector extracts 706 HDV-to-AV and 3,172 HDV-to-HDV cut-in events from the same traffic environment. The median accepted gap in front of the Waymo AV is 7.58 meters versus 9.57 meters for HDV targets, a 1.99 meter reduction that is statistically significant (p equals 5.76 times 10 to the negative eighth power, d equals negative 0.224) and persists under speed-matched resampling. Cut-in speeds toward the AV are 37 percent higher (51.7 versus 37.7 kilometers per hour, d equals 0.502), and 68.0 percent of AV-targeted cut-ins occur below the 10 meter gap boundary versus 51.8 percent of HDV-targeted events (chi-squared equals 60.5, p is less than 10 to the negative thirteenth power). These results reveal a systematic and safety-relevant asymmetry in human gap-acceptance behavior that warrants AV-specific calibration of both motion-planning safety envelopes and traffic simulation models.