The recent developments in the field of autonomous driving and electric vehicle technology have shown significant advancements in decision-making algorithms, fuel efficiency optimization, and battery state estimation. Researchers are increasingly focusing on integrating psychological models, such as the Conservation of Resources theory, into decision-making processes for automated driving, aiming to enhance both safety and interpretability. Additionally, there is a growing emphasis on addressing real-world constraints like observational perturbations in battery state of charge measurements, which has led to innovative solutions in constrained optimal fuel consumption for hybrid electric vehicles. The field is also witnessing a shift towards comprehensive models that evaluate the total cost of ownership for decarbonizing road freight transport, highlighting the potential for zero-emission vehicles to become economically viable with technological advancements. Furthermore, advancements in reinforcement learning for autonomous driving are being complemented with entropy-based controls to improve robustness and generate more human-like driving behaviors. Lastly, significant strides have been made in state of charge estimation for LiFePO4 batteries, with adaptive multi-model Kalman filters proving to be highly accurate and robust under various operating conditions.