[Retracted] Deep and Reinforcement Learning Technologies on Internet of Vehicle (IoV) Applications: Current Issues and Future Trends

Table 2

Summary of AI applications for cloud-based IoV frameworks.

Work	Year	Source	Framework approaches	Features	Advantages
Zhang et al. [50]	2021	Springer	Resource allocation scheme based on deep RL scheme	Minimum total computing cost for IoV in an edge environment	Effectively allocate the computing resources of IoV
Grigorescu et al. [51]	2020	Sensors	Self-driving autonomous vehicles to forecast future traffic demands in IoV	AI inference engines for autonomous driving applications	Mitigating privacy issues and effective path prediction
Claudio et al. [52]	2020	IEEE	ML-based framework for IoV edge network	Avoid the abuse of IoV edge computing activities	Improve the performance of control mechanisms in IoV
Ning et al. [53]	2019	IEEE	Deep RL for cloud computing-based offloading framework for IoV	Minimize the overall energy consumption	Reduce energy consumption by 60%
Razi et al. [54]	2018	IEEE	Data analytics framework for fog-based IoV architecture	Analysis based on DL and deep RL (DRL)	Context-aware services in an IoV environment
Chen et al. [55]	2018	Springer	Mobile cloud computing framework	Framework-based deep learning approach	Outperform objects detection rate