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The trajectory planning problem (TPP) has become increasingly crucial in theresearch of next-generation transportation systems, but it presents challengesdue to the non-linearity of its constraints. One specific case within TPP,namely the Eco-trajectory Planning Problem (EPP), poses even greatercomputational difficulties due to its nonlinear, high-order, and non-convexobjective function. This paper proposes a translatable explicit predictivemodel control (teMPC) framework to address the eco-trajectory planning problemin scenarios without lane-changing behavior. The teMPC framework consists of anoffline module and an online module. In the offline module, an optimaleco-trajectory batch is constructed by solving a sequence of simplifiedoptimization problems to minimize fuel consumption, considering various initialand terminal system states. Each candidate trajectory in the batch yields thelowest fuel consumption subject to a specific travel time from the vehicleentry to the departure from the intersection. In the online module, two dynamictrajectory planning algorithms are provided. Both algorithms greatly improvethe computational efficiency of planning and only suffer from a limited extentof optimality losses through a batch-based selection process becauseoptimization and calculation are pre-computed in the offline module. The latteralgorithm can also handle possible emergencies and prediction errors.Theoretical analysis and numerical tests are presented and discussed toevaluate the computational quality and efficiency of the teMPC framework undera mixed-traffic flow environment that incorporates human-driving vehicles (HDV)and connected and automated vehicles (CAV) with different market penetrationrates (MPR)

Translatable Explicit Model Predictive Control Framework for Connected and Automated Vehicles Eco-trajectory Planning