Artificial Intelligence (AI) is fundamentally reshaping the landscape of automotive engineering, particularly in passenger vehicles where comfort, safety, and customization are paramount. This paper presents a comprehensive review of current and emerging AI-driven applications in passenger cars, including intelligent vehicle insurance, real-time traffic optimization, personalized driving experiences, emotion recognition, and advanced EV battery management.
The integration of AI technologies—ranging from machine learning and computer vision to natural language processing and deep neural networks—enables predictive maintenance, adaptive driver-assist systems, and next-generation human-machine interfaces. AI’s role in vehicle insurance is also explored through behaviour-based risk scoring and claims automation. Furthermore, emotion-aware cabin systems are discussed, which aim to detect driver fatigue, stress, or distraction to improve road safety and mental wellness. The paper also highlights the transformative potential of AI in optimizing electric vehicle (EV) battery health through predictive energy management, ultimately contributing to extended battery life and improved range prediction. However, alongside these advances lie significant challenges such as data privacy, high development costs, regulatory compliance, AI system reliability in unpredictable conditions, and user acceptance. This paper addresses these concerns and presents a roadmap for safe and scalable AI integration, including recommendations for testing frameworks, legal considerations, and user trust-building strategies. With a focus on engineering analytics and human-centric design, this paper aims to serve as a strategic reference for OEMs, suppliers, and researchers striving to bring intelligent, reliable, and emotionally aware AI systems into the passenger car domain.
Keywords: Artificial Intelligence, Predictive Analytics, Vehicle Customization, Autonomous Systems