Stochastic dynamic programming control policies for fuel efficient in-traffic driving

This paper demonstrates a methodology, based on stochastic dynamic programming, for developing a control policy that prescribes vehicle speed to minimize on average a weighted sum of fuel consumption and travel time, while travelling along the same route or a set of routes in a given geographic area. Given the current road grade, traffic speed and vehicle speed, the control policy prescribes an offset in vehicle speed relative to current traffic speed, which when added to the predicted value of traffic speed, gives a vehicle speed set point for an adaptive cruise control system. It is shown that transition probability matrices necessary to generate the control policy can be constructed from gathered data. A virtual testing environment based on CarSim is used for simulations that can effectively handle vehicle following and adaptive cruise control scenarios. Comparative fuel savings are shown to depend on time of travel (off-peak hours or rush hour) and traffic assumptions.