AI Overview - Definition, types (narrow, general), and paradigms (rule-based systems, ML, DL), IoV Introduction - Concept, evolution, IoV vs. IoT, V2X communication (V2V, V2I, V2N).IoV Ecosystem and AI Role - Components (vehicles, cloud, edge); AI in autonomous driving, traffic management.Applications and Case Studies -Smart transportation, predictive maintenance, safety systems.

ML Fundamentals - Supervised, unsupervised learning; regression, classification basics.ML for IoV - Decision trees, random forests for vehicle data analysis; data preprocessing (cleaning, normalization), Applications - Traffic prediction, driver behavior analysis, anomaly detection.

DL Basics - Neural networks, CNNs for image processing, basics of deep reinforcement learning, Computer Vision for IoV - Object detection, lane detection, traffic sign recognition.IoV Challenges and Applications - Real-time processing, autonomous driving (path planning, obstacle avoidance).

Communication Protocols - DSRC, C-V2X, 5G, edge computing basics, Data Management - Real-time data streaming, cloud-edge integration for IoV, AI and Security - AI for network optimization, intrusion detection, privacy in V2X.

Advanced AI Techniques - GANs, transfer learning for IoV applications, IoV Applications - Smart city integration, cooperative driving, platooning, Ethical Considerations - Bias in AI, safety, liability, environmental impacts.