Published 23-10-2024
Keywords
- Machine Learning,
- Autonomous Driving,
- Decision-Making
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
Autonomous driving is a collection of technologies that allow a vehicle to sense and perceive the physical and social environment, model and plan its driving behavior, and then make decisions based on a cost versus benefit analysis. The promise of autonomous driving is to create a safer system, with less congestion and a better user experience, that can operate at scale without significant manual intervention.
The definition of five levels of vehicle autonomy ranges from 0 to 5. Vehicles with level 0 autonomy have no automation and drivers execute all vehicle control tasks. With increased system capability, level 1 vehicles have some driver assist features while level 2 vehicles can autonomously control speed and steering but require driver attention. At levels 3, 4, and 5, vehicles operate with increasing autonomy and decreasing reliance on driver attention and intervention. Machine learning has been a part of the intelligent systems used in autonomous driving for decades. The rise in the use of machine learning is directly associated with the ability to capture, store, and process the vast amount of data generated by these highly automated vehicles.
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