Electrified propulsion systems are a promising way of reducing traffic-related pollution. Because of the characteristics of the exhaust systems of engine-assisted vehicles, it is possible that pedestrians in close proximity to vehicles may encounter situations with high enough concentrations of emissions to cause specific health effects. To decrease the impact of vehicle emissions and pollutants on surrounding pedestrians, this chapter presents a cyber-physical optimisationOptimisation technique for the pedestrian-aware supervisory controlPedestrian-aware supervisory control strategy of hybrid propulsion systemsHybrid propulsion systems. The technique is a combination of the Bees AlgorithmBees algorithm and a fuzzy adaptive cost mapFuzzy adaptive cost map to optimise the rule-based power-split parameters. It is capable of self-adjusting the intertarget weights of exhaust emissions and fuel with real-time pedestrian density information during the optimisationsOptimisation. To examine the robustnessRobustness of the hybrid propulsion systemHybrid propulsion systems optimised by the introduced technique, the effects of communication quality and bootstrap sampling techniques are studied. The results show that applying the developed fuzzy adaptive cost mapFuzzy adaptive cost map can reduce emissions to nearby pedestrians by 14.42%.