Maximizing Road Capacity Using Cars that Influence People

摘要: The emerging technology enabling autonomy in vehicles has led to a variety of new problems transportation networks, such as planning and perception for autonomous vehicles. Other works consider social objectives decreasing fuel consumption travel time by platooning. However, these strategies are limited the actions surrounding human drivers. In this paper, we proactively achieving influencing behavior through planned interactions. Our key insight is that can use design local interactions influence achieve goals. To end, characterize increase road capacity afforded platooning, well vehicle configuration maximizes capacity. We present novel algorithm uses low-level control framework leverage optimally rearrange showcase our using simulated shared between human-driven vehicles, which illustrate reordering action.