Model-Based Predictive Traffic Control for Intelligent Vehicles: Dynamic Speed Limits and Dynamic Lane Allocation

We consider traffic management and control approaches for automated highway systems with a combination of intelligent vehicles and roadside controllers. The vehicles are organized in platoons with short intraplatoon distances, and larger distances between platoons. Moreover, all vehicles are assumed to be automated, i.e., throttle, braking, and steering commands are determined by an automated on-board controller. Within a platoon the vehicles coordinate their actions so as to maintain a small but safe intervehicle distance, using adaptive cruise control methods. Platoon leaders receive speed set-points and lane change commands from the roadside controller. We propose a model-based predictive control (MPC) approach to determine appropriate speed limits and lane allocations for the platoons. In general, this results in mixed-integer optimization problems. We discuss some methods to solve these problems suboptimally on-line. The proposed approach is then applied to a simple simulation example in which the aim is to minimize the total time all vehicles spend in the network by optimally assigning dynamic speed limits and lane changes.