Motions Speak Louder Than Words: Designing Human-like AV Speed Profiles at Crosswalks with a Bayesian Optimisation–Driven Adaptive Experiment

Autonomous vehicles (AVs) must communicate yielding intent to pedestrians in mixed traffic to ensure safe interaction, and vehicle decelerating motion is a promising implicit communication channel at crosswalks. Designing AV deceleration motion remains difficult because it is continuous with interdependent kinematics (e.g., braking onset, deceleration rate), making fixed‑design experiments inefficient. Therefore, we parameterised deceleration behaviour using a minimum‑jerk model (MJM), which can generate smooth speed profiles from tuneable parameters (stopping distance and duration). We will then formulate profile design as an optimisation problem that maximises pedestrian‑rated safety and efficiency. We will conduct an adaptive experiment in an immersive pedestrian simulator in which Bayesian optimisation sequentially proposes profiles that are most likely to improve safety and efficiency ratings based on pedestrians’ evaluations of previous profiles. Preliminary results from fitting MJM to 62 naturalistic human profiles indicate that the MJM reproduces the overall human profile shape (median RMSE = 0.88 m/s) with only 2 parameters (i.e., stopping distance, duration), supporting its use as a profile generator for the experiment. This work can contribute a human-in-the-loop framework for designing AV motion as an implicit interface.