Enhancing Automobile Motion Planning Algorithms using Quantum Computing: A Systematic Review
Abstract
The emergence of autonomous vehicles has highlighted the need for advanced motion planning algorithms capable of navigating diverse weather conditions efficiently and safely. In this study, the potential of Quantum Machine Learning (QML) algorithms in enhancing automobile motion planning across various weather scenarios has been investigated. Generative models such as Quantum Generative Adversarial Networks (QGANs), Quantum Autoencoders, and Quantum Boltzmann Machines (QBMs) are being used to generate a variety of critical weather scenarios using the Berkley Deep Drive (BDD100K) dataset. The performance of these algorithms is examined with respect to metrics such as accuracy, robustness, and computational efficiency. Despite the theoretical advantages of QML, practical implementation faces multiple challenges including error rates, limited qubit coherence times, and the need for error-correction techniques.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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