AI-Driven Advanced Driver Assistance Systems (ADAS)
Keywords:
ADAS, Advanced Driver Assistance Systems, AIAbstract
As concerns for the ecological and social well-being of people and societies are increasing, driving has made significant progress in terms of safety in recent times. This is mainly due to a new generation of driving aids provided by Advanced Driver Assistance Systems. These systems may not guarantee state-of-the-art autonomous driving, but they are increasingly present in any new car. Traditional ADAS was mainly developed under centralized control driven by the model-based approach. However, it is currently implemented based on data-driven approaches with a focus on the use of artificial intelligence solutions.
The goal of this essay is to analyze the role played by artificial intelligence engines in the development, optimization, and fine-tuning of ADAS functions. This essay believes that artificial intelligence is an important driver of new generations of ADAS as it can provide super-central solutions to ADAS functions. Consequently, this essay deals with the role of artificial intelligence-based optimization in developing and fine-tuning ADAS functions. Since autonomous driving deals with complexity originating from different sources, researchers are studying efficient solutions to be implemented in advanced driver systems. The role of AI is considered crucial for developing new generations of ADAS. Despite the fact that no autonomous cars are actually sold, several new centralized functions powered by artificial intelligence are present in modern cars. These functions cannot provide full-time automation or even part-time sustained automation yet, due to the sheer complexity of the driving task, but they nonetheless assist the driver in delivering enhanced safety while improving the "road experience".
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References
Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.
Singh, Jaswinder. "The Ethics of Data Ownership in Autonomous Driving: Navigating Legal, Privacy, and Decision-Making Challenges in a Fully Automated Transport System." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 324-366.
Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.
S. Kumari, “Digital Transformation Frameworks for Legacy Enterprises: Integrating AI and Cloud Computing to Revolutionize Business Models and Operational Efficiency ”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 186–204, Jan. 2021
Tamanampudi, Venkata Mohit. "NLP-Powered ChatOps: Automating DevOps Collaboration Using Natural Language Processing for Real-Time Incident Resolution." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 530-567.
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