MPC-Based Adaptive Cruise Control with Sensor Fusion for Smart Cities

Accommodating the needs for having integrated and holistic approaches to address the challenges of governing and planning smart cities is explored in this paper. The problem of developing a smart and sustainable solution for intelligent transportation systems is addressed. Nowadays, autonomous vehicle technologies attract great interest, where advanced driving assistance systems (ADASs) target the safety and comfort of drivers, while ensuring fuel efficiency as well. This paper explores some ADASs features that are based on actual driving conditions to design adaptive Cruise Control (ACC) with sensor fusion that is based on Model Predictive Control (MPC). In order to get the appropriate velocity output response and to achieve a smooth acceleration response, the characteristics of this controller will be correctly tuned. Both lane keeping assist (LKA) and Lane Detection (LD) are the main features to be investigated, using different driving scenarios. The control methodology is based on introducing a hybrid lateral and longitudinal control system to guarantee having a better tracking response, safe driving, and lower fuel consumption, which is highly needed for sustainable smart cities. A closed-loop Simulink model will be used to test and validate the proposed system. The obtained results indicate that the system is indeed cost-effective and is a promising sustainable solution for smart transportation systems.