Integrated Intelligent Control Systems for Eco and Safe Driving in Autonomous Vehicles

Autonomous vehicles (AVs) have a significant impact on the expansion of greenhouse gas emissions as well as driving safety. Consequently, ensuring safety while improving the energy efficiency of AVs has gained increasing importance. In this study, we offer an optimal intelligent system (OIS) by applying a multi-objective evolutionary optimization algorithm to an integrated control system, including an Adaptive Cruise Control (ACC) and an Intelligent Energy Management System (IEMS) that augments safety and lessens the energy consumption for Conventional AVs. In this - system, a predictive model is developed by defining the desired acceleration of the ego vehicle. The vehicle then follows a longitudinal path to track the lead vehicle on the same highway lane, ensuring a safe following distance while minimizing tracking errors. Subsequently, an Intelligent Energy Management System (IEMS) is introduced to optimize the torque output of the internal combustion engine, aimed at reducing the energy consumption of the ego vehicle. Additionally, a sensitivity analysis of the ego vehicle is conducted to account for disturbances and signal loss scenarios. In this way, a band-limited white noise is considered for road power demand (RPD) and measuring signal of lead vehicle velocity, simultaneously. Moreover, two different scenarios are designed regarding signal-losing circumstances and interruptions in receiving the signal of lead vehicle velocity. The optimal solutions reveal a strong independence between safety and fuel consumption, showing that their performances significantly affect each other. The optimal solutions reveal a strong interdependence between safety and fuel consumption, showing that their performances significantly affect each other. The results demonstrate that the optimal approach can significantly reduce fuel consumption while maintaining safety and effective collision avoidance performances.