  Current research on Deep Reinforcement Learning (DRL) for automated on-ramp
merging neglects vehicle powertrain and dynamics. This work considers automated
on-ramp merging for a power-split Plug-In Hybrid Electric Vehicle (PHEV), the
2015 Toyota Prius Plug-In, using DRL. The on-ramp merging control and the PHEV
energy management are co-optimized such that the DRL policy directly outputs
the power split between the engine and the electric motor. The testing results
show that DRL can be successfully used for co-optimization, leading to
collision-free on-ramp merging. When compared with sequential approaches
wherein the upper-level on-ramp merging control and the lower-level PHEV energy
management are performed independently and in sequence, we found that
co-optimization results in economic but jerky on-ramp merging while sequential
approaches may result in collisions due to neglecting powertrain power limit
constraints in designing the upper-level on-ramp merging controller.

Systems and Control;Systems and Control