Prospects of automated driving in different road freight vocations: Why non-driving matters more than driving

Abstract

Abstract Most truck modeling studies make generalized assumptions regarding driving times, distances, and non-driving tasks, except for loading and unloading, and therefore fail to capture the diversity of freight transport vocations, especially in the context of automated trucking analyses. To fill this gap, this study followed a three-stage process that involved reviewing existing literature, a typological analysis of driving and non-driving tasks in trucking vocations, and a quantitative assessment of the vocation specific benefits and challenges of automation. The last, however, was rather illustrative because of restricted available task-share data. The analysis grouped the tasks by their location: at the terminal/depot, on route, and at the customer site. From an operational perspective, tasks at the customer site are a key limiting factor for the use of automated vehicles. Non-driving tasks further determine the operational usefulness and benefits of automated vehicles. From a technical perspective, the traffic environment significantly influences the ease of implementing automated driving. For example, controlled traffic environments, such as yard transportation, are easiest to automate, while complex environments, such as urban areas, are the hardest. Overall, task shares significantly influence the utility of automated driving for individual freight vocations, with non-driving task shares ranging from 22 to 80%. Moreover, vocations that offer the greatest benefits may be more conducive to the development of automated driving technologies than those with the fewest technical and operational barriers to entry.