Modeling the information-based control of steering through multiple waypoints

Modeling the visual control of steering has been an active area of research for decades, but the majority of work up to this point has focused on steering to a single target or along a winding road. There is much less work on the strategies used to steer through multiple waypoints, which is relevant in locomotor tasks such as slalom skiing. A critical open issue for modeling the multiple-waypoint task is how to capture the influence of information from waypoints that lie beyond the most immediate one. Recently, we found that humans do use such information, often altering their approach to the nearest waypoint, affording a smoother trajectory through the subsequent waypoint. The aim of the present study was to develop and test competing models that capture human steering behavior observed in multiple-waypoint tasks. We consider four models: (1) the behavioral dynamics model (Fajen & Warren, 2003) with a single goal (most immediate waypoint), (2) the behavioral dynamics model with two goals (two upcoming waypoints), (3) a pure-pursuit controller with a single goal (fixated waypoint) (Tuhkanen et al., 2023), and (4) a new model that relies on information about the constant-radius path that passes through the two upcoming waypoints. We simulated all four models and compared the model-generated trajectories to those produced by human subjects in a task that involves steering through multiple waypoints. Only Model 4 captures the shape of the human trajectories, initially veering away from the nearest waypoint before turning back, setting up a smoother trajectory through both waypoints. The other three models either do not anticipate (Model 1) or were influenced by the future waypoint but not in the way that was consistent with human behavior (Models 2 and 3). The findings demonstrate that human-like anticipation of multiple waypoints can be captured within an information-based framework.