Towards Fully Autonomous Driving: Classical and Deep Learning Methods Novel Integration in a Hybrid Decision-Making Framework

Decision Planning (DP) is a critical module in achieving level 5 autonomy for Autonomous Vehicles (AVs). Existing state of-the-art DP methods each have distinct advantages and limitations. This paper presents a hybrid Decision-Making (DM) framework that integrates classical and deep learning methodologies to emulate human-like behavior in diverse driving scenarios. The proposed architecture includes three main components: (1) a dual-stream Convolutional Neural Network (CNN) that processes front- and rear-view camera inputs to classify driving scenarios as either simple (highway) or complex (urban), (2) a Hierarchical Finite State Machine (HFSM) for handling simple scenarios, and (3) a Deep Learning (DL) module for managing complex environments. The system was evaluated in the CARLA simulator using diverse traffic scenarios. The human driver baseline was derived from a survey of 50 drivers, who assessed the optimal decisions for simulated scenes. The hybrid model outperformed baselines, achieving a 30% improvement in decision optimality over human drivers and a 45% improvement over a standalone DL approach. It also maintained a safety rate of 92%, an efficiency of 66%, and an 85.5% lane idleness rate. These results demonstrate the potential of the proposed framework to enhance safety, generalization, and performance in fully autonomous driving systems.