AI drive: quantum-computational DRL framework for EHV navigational efficiency and security augmentation

The Q-learning approach, within deep reinforcement learning (DRL) methodology, is beneficial in multifarious tasks, one of them including enhancement of navigational tasks, shown by the DQN approach; merging Q-learning with deep neural networks is not without its challenges, particularly in situations where there is a chance of overestimating the correct value. In order to address the problems described above in the realm of electric and hybrid vehicles (EHVs), a quantum-computational adaptation that is influenced by Double Q-learning as a potential solution is proposed i.e., QED2Q-N. The enhanced version increases the efficacy of navigation by addressing the overestimates, which were included in previous editions, conceived with the intention of approximately modeling functions on a broad scale. The expansion of electric and hybrid vehicles (EHVs) toward growing autonomy, along with improvements in intelligent transportation systems (ITS), pose significant cybersecurity threats in the disciplines of vehicle automation and cooperative ITS. These underlying dangers may be found in the sectors of vehicle automation and cooperative ITS with an emphasis on the ways in which quantum-enhanced systems may assist in strengthening defenses, the use of quantum computing and deep reinforcement learning (DRL) merged together, works to bring improvement in EHV navigation accuracy and the enhancement of overall system security. The study underlines the relevance of adopting a quantum-computing technique in order to properly solve the dynamic issues associated with EHV technology.