Intention-Misjudgment-Oriented Background-Traffic Generation for Autonomous Vehicle Testing

Surrounding vehicles may misjudge an autonomous vehicle’s intended motion and yield or merge at the wrong time. These conflicts are difficult to test systematically when surrounding vehicles’ expectations cannot be varied separately from the autonomous vehicle’s planned motion. This paper proposes an intention-misjudgment-oriented background-traffic generator for autonomous-vehicle testing. A speed-intention perturbation mechanism separates the nominal speed plan of the vehicle under test (VUT) from the estimate supplied to surrounding vehicles, enabling controlled underestimation and overestimation. A Transformer-based interaction encoder relates the perturbed estimate to traffic context and 50-step guiding-lane references to generate context-dependent responses. Multimodal decoding represents alternative responses, while learned affine state updates couple their evolution to inter-vehicle interactions and VUT control inputs. In highway-merging simulations, the best-matching generated trajectories have lower average and endpoint position errors than those of the Mixtures of Affine Time-varying Systems (MATS) baseline trajectory-prediction model at all three comparison speeds. Across 2353 matched initial scenarios, underestimating the VUT’s future speed by 25% yields an observed collision rate of 11.3%, compared with 6.7% under accurate estimation. The generated cases illustrate conflicts associated with delayed lane changes and altered passing order. These results characterize controlled, simulator-based stress-test outcomes in the IDM–MOBIL highway-merging setting; they do not constitute empirical validation of human-driver responses to intention misjudgment.