Driver distraction gets a new test: a hierarchical model makes lane and speed information compete for sampling, and all eight measures land within one human standard deviation
Deciding whether to look at the lane or the speedometer becomes a problem of allocating limited visual resources under partial observability. Earlier models treated lane keeping and speed control separately, and none formalized sampling decisions alongside coupled lateral and longitudinal control. A hierarchical computational rationality model learns lane keeping, speed maintenance and supervisory attention allocation together; across 30 simulated runs, all eight measure means fell within one human standard deviation, though the model produced no lane deviations.