In the realm of autonomous vehicles, learning is achieved through reinforcement learning. To illustrate, let's consider the task of training a self-driving car to apply the brakes when it approaches a wall. Initially, the car continues to drive until it collides with the wall multiple times. These experiences are then stored in its memory, enabling the car to learn from them. Gradually, through these collisions, the car becomes aware that it must halt when it nears a wall. Traditionally, each car undergoes individual training until it learns to stop automatically and avoid collisions. However, a paradigm shift is proposed in this paper. Instead of isolated learning, each car would be connected to a shared public ledger. This ledger would serve as a platform for cars to exchange their experiences, enabling all vehicles connected to the ledger to learn from a single car's experiences. This collective learning approach eliminates the arduous task of individually training each car. Facilitating this revolutionary concept is the utilization of Blockchain Technology. By leveraging blockchain, cars can collectively enhance their understanding of when to stop, drawing from the valuable experiences shared on the shared ledger. This creates a collaborative ecosystem where knowledge is shared, accelerating the learning process and paving the way for safer and more efficient autonomous vehicles. The integration of Artificial Intelligence and Blockchain Technology in the context of autonomous vehicles holds immense promise. Through the implementation of an artificially intelligent blockchain system, the learning process of self-driving cars can be streamlined, fostering a future where shared knowledge propels advancements in technology and enhances overall safety on the roads. Experience the power of Artificial Intelligence integrated Blockchain, and unlock the full potential of autonomous vehicles.


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    Titel :

    Review of Artificial Intelligence-Integrated Blockchain for Training Autonomous Vehicles


    Beteiligte:
    Kumar, Amit (Autor:in) / Sharma, Neha (Autor:in)


    Erscheinungsdatum :

    2023-11-03


    Format / Umfang :

    1191684 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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