Route and speed optimization for autonomous trucks

Autonomous vehicles, and in particular autonomous trucks (ATs), are an emerging technology that is becoming a reality in the transportation sector. This paper addresses the problem of optimizing the routes and the speeds of ATs making deliveries under uncertain traffic conditions. The aim is to reduce the cost of emissions, fuel consumption and travel times. The traffic conditions are represented by a discrete set of scenarios, using which the problem is modeled in the form of two-stage stochastic programming formulations using two different recourse strategies. The strategies differ in the amount of information available during the decision making process. Computational results show the added value of stochastic modeling over a deterministic approach and the quantified benefits of optimizing speed.