Efficient Dynamic Programming Solution to a Platoon Coordination Merge Problem With Stochastic Travel Times
For researchers and practitioners in truck platooning, this work provides a more efficient method for dynamic platoon formation under uncertainty, though it is an incremental improvement over existing dynamic programming approaches.
This paper addresses the problem of coordinating two trucks to merge into a platoon on a highway under stochastic travel times, formulating it as a stochastic dynamic programming problem. The proposed solution reduces computational complexity by deriving bounds on the state space, and simulations show applicability to realistic instances.
The problem of maximizing the probability of two trucks being coordinated to merge into a platoon on a highway is considered. Truck platooning is a promising technology that allows heavy vehicles to save fuel by driving with small automatically controlled inter-vehicle distances. In order to leverage the full potential of platooning, platoons can be formed dynamically en route by small adjustments to their speeds. However, in heavily used parts of the road network, travel times are subject to random disturbances originating from traffic, weather and other sources. We formulate this problem as a stochastic dynamic programming problem over a finite horizon, for which solutions can be computed using a backwards recursion. By exploiting the characteristics of the problem, we derive bounds on the set of states that have to be explored at every stage, which in turn reduces the complexity of computing the solution. Simulations suggest that the approach is applicable to realistic problem instances.