Connected Cars, Connected Customers: The Role of AI and ML in Automotive Engagement
Abstract
The automotive industry has witnessed a transformative shift with the integration of Artificial Intelligence (AI) and Machine Learning (ML), significantly shaping the connected automotive ecosystem. This paper examines the role of AI and ML in advancing connected car technologies, focusing on their applications in autonomous driving, predictive maintenance, and personalized customer engagement. By analyzing developments from 2003 to 2022, the research highlights the opportunities and challenges posed by these technologies. Real-world case studies from industry leaders showcase successful implementations, emphasizing how AI and ML enhance customer experiences through predictive analytics and real-time decision-making. Despite challenges such as data privacy concerns, technological complexities, and high deployment costs, AI and ML continue to offer unprecedented possibilities for improving operational efficiency and fostering meaningful customer relationships. The study provides practical recommendations for leveraging these technologies, including enhancing data security, adopting cost-effective cloud-based solutions, and fostering collaborations between automotive and technology sectors. This forward-looking perspective underscores the potential of AI and ML to revolutionize customer engagement and operational strategies in the automotive industry. Furthermore, it highlights their role in advancing sustainability, improving safety standards, and creating new revenue streams, which collectively pave the way for a smarter, more connected, and efficient future.
References
Goodall, N. J. (2014). Machine Ethics and Automated Vehicles. Ethics and Information Technology, 16(1), 1-10.
Wang, X., Yu, H., & Wang, W. (2016). Predictive Maintenance System Based on Machine Learning. Journal of Industrial Information Integration, 1, 1-9.
Kapoor, R., Prasad, V., & Sharma, A. (2018). Enhancing Customer Experience in Automotive Industry Using AI. International Journal of Technology Management, 45(3), 245-260.
Tesla Motors. (2020). Autopilot Safety Report. Retrieved from www.tesla.com.
BMW Group. (2021). Innovation in Predictive Maintenance. Retrieved from www.bmwgroup.com.
Binns, R., Veale, M., & Van Kleek, M. (2018). Algorithmic Accountability: A Practical Framework for Algorithmic Transparency and Fairness. Journal of Law, Information and Technology, 24(2), 143-165.
Smith, J., & Gupta, R. (2020). The Impact of Edge Computing on Connected Car Systems. Journal of Emerging Automotive Technologies, 18(3), 56-70.
Waymo. (2021). Fully Autonomous Driving: A Technological Milestone. Retrieved from www.waymo.com.
Audi AG. (2022). Advanced Infotainment Systems Powered by AI. Retrieved from www.audi.com.
Alphabet Inc. (2021). Waymo’s Journey to Driverless Taxis. Corporate Blog Post. Retrieved from www.blog.google.
Barlow, J., & Eastwood, T. (2015). Big Data and Predictive Analytics in Automotive Maintenance. Industrial Big Data Journal, 12(4), 78-91.
AWS. (2021). Cloud Solutions for Automotive AI. Retrieved from aws.amazon.com.
Microsoft Azure. (2021). Leveraging AI to Revolutionize the Automotive Industry. Retrieved from azure.microsoft.com.
OpenAI. (2019). Generative Models for Automotive Design and Optimization. Artificial Intelligence Research Journal, 27(7), 92-109.
Naga Ramesh Palakurti. (2022). Empowering Rules Engines: AI and ML Enhancements in BRMS for Agile Business Strategies.ijsdai, 2(1),1-20.