Autonomous Vehicles and Robust Decision-Making in Dynamic Environments
DOI:
https://doi.org/10.63995/NSTN6884Keywords:
Autonomous Vehicles; Decision-Making; Dynamic Environments; Machine Learning; Sensor Fusion; V2X CommunicationAbstract
Autonomous vehicles (AVs) are at the forefront of transforming transportation, requiring robust decision-making capabilities to navigate dynamic environments effectively. These vehicles rely on advanced sensors, machine learning algorithms, and real-time data processing to make split-second decisions that ensure safety and efficiency. Key challenges include accurately perceiving the environment, predicting the behavior of other road users, and responding to unpredictable conditions such as changing weather, road obstacles, and traffic patterns. Robust decision-making in AVs integrates various technologies, including computer vision, sensor fusion, and deep learning, to create comprehensive situational awareness. Decision-making frameworks, such as reinforcement learning and probabilistic modeling, enable AVs to evaluate multiple scenarios and select optimal actions under uncertainty. Additionally, the development of vehicle-to-everything (V2X) communication enhances situational awareness by allowing AVs to exchange information with infrastructure and other vehicles. Ensuring the reliability and safety of AVs in dynamic environments requires rigorous testing, validation, and continuous improvement of decision-making algorithms. Ethical considerations, regulatory frameworks, and public acceptance also play crucial roles in the widespread adoption of autonomous vehicles. This abstract highlights the technological and methodological advancements in robust decision-making for AVs, emphasizing their potential to revolutionize transportation by enhancing safety, reducing congestion, and improving mobility in dynamic and complex environments.
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