Intelligent Safety Control Integrated with Energy Management Systems for Autonomous Vehicles: Adaptive Cruise and Lane Change Strategies

The global transition toward cleaner energy and intelligent infrastructure has propelled the rise of autonomous vehicles (AVs), which aim to enhance safety, improve energy efficiency, and reduce human driving error. The successful deployment of AVs hinges not only on technological advancements and regulatory support but also on ensuring robust safety and intelligent energy management systems (EMS). This thesis addresses these two critical challenges—safety and energy efficiency—through the development of integrated intelligent control systems. While the literature on AVs is extensive, existing studies often address safety and energy management in isolation. Conventional ACC systems do not account for energy optimisation, lane-change research typically overlooks fuel efficiency, and EMS strategies rarely incorporate safety-critical decision-making. These gaps highlight the need for integrated frameworks that simultaneously optimise safety and energy under real-world conditions. The first safety concern addressed in this thesis is the intelligent control of AVs in relation to the vehicle ahead. To address this, an Adaptive Cruise Control (ACC) system is developed, ensuring safe speeds and distances between the AV and the lead vehicle. This system also enhances fuel economy and reduces emissions by maintaining optimal acceleration and deceleration rates. A switched Model Predictive Control (MPC) system, along with a Neuro-Fuzzy (NF) controller, determines the desired speed and safe following distance, and the performance of the switched MPC is mathematically proven to be stable. The energy management system (EMS) is integrated to intelligently control energy consumption based on ACC commands. Results indicate that the ACC-MPC and ACC-NF systems significantly 2 reduce driving risks, and energy consumption is improved by 2.6% with the ACC-NF approach. The second safety concern is related to intelligent lane-change manoeuvres. To address this, an intelligent algorithm is employed within the AV lane-change system to identify the Most Important Objects (MIO) and determine the most feasible and safe trajectory. The Adaptive Model Predictive Control (AMPC) framework is introduced to manage the nonlinearity and time-variance of the vehicle's state space during lane changes, ensuring smooth, safe transitions. Additionally, for optimising internal combustion engine (ICE) performance, a data-driven modelling approach is used to predict engine performance without requiring detailed knowledge of engine internals. Nonlinear Model Predictive Control (NMPC) is applied to handle the highly nonlinear dynamics of the ICE, optimising torque and speed to improve fuel efficiency. The key contribution of this thesis lies in the simultaneous optimisation of engine control and lane-change trajectories, ensuring both fuel efficiency and passenger comfort. Results demonstrate that the AMPC system effectively reduces driving risk and improves energy efficiency by 2.6% using NMPC methodology. In conclusion, the integration of enhanced adaptive cruise control, lane-change controllers, and energy efficiency systems for AVs leads to a significant improvement in safety, a re-duction in driving risks, and enhanced energy efficiency. The findings demonstrate that these intelligent systems make autonomous vehicles not only safer and more reliable but also more energy-efficient, marking a step toward the practical deployment of AVs with improved overall vehicle performance.<p></p>