## 2 Related Work
[22] introduced an indMZ scheme that involves collaborative vehicles generating randomized versions of a pseudonym until the desired level of anonymity is achieved. Despite the privacy enhancement offered by fake beacons, a fundamental conflict between VANET functionality and privacy preservation persists. To enhance anonymity, the size of the mix zones is often extended beyond junctions to obscure critical information, such as steering angles, lane changes, and accelerations. Due to imperfect trajectory predictions, the need for increased fake beacons arises to reduce the success rate of tracking by attackers. However, this significantly amplifies the transmission overhead of the system and may potentially impact the Quality of Service (QoS) of the VANET [29; 44]. Deep learning has been extensively applied in the field of trajectory prediction and pseudonym changing for vehicles, primarily due to its ability to model complex patterns and dependencies in sequential data. However, the effectiveness of these applications can vary significantly based on the specific architecture used and the context of the application. Some studies [45; 46] explored the use of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for generating vehicle trajectories. While RNNs are adept at processing sequential information, their performance in trajectory prediction at intersections was found to be less accurate due to the lack of practical intersection-related information during training. This shortfall makes the dummy vehicles generated by RNNs more susceptible to being distinguished by attackers. LSTMs, on the other hand, are better suited for long-distance trajectory predictions. They tend to underperform in scenarios involving short distances, such as those encountered at intersections, where the trajectory changes are abrupt and localized. [PERSON] et al. [47] shifted focus to reinforcement learning, which is utilized to generate complete vehicle trajectories over large areas that closely mimic the real vehicular states. However, this approach does not specifically address trajectory generation at junctions. Since semantic attacks often exploit the continuity of vehicle states to track vehicles, gen erating realistic trajectories within junctions becomes crucial. The short duration vehicles spend at junctions necessitates a method that not only maintains accuracy but also speeds up trajectory generation to effectively confuse attackers. One of the inherent limitations of deep learning highlighted by these studies is its relative inefficiency in real-time applications. Deep learning models, especially those involving complex architectures like LSTMs and reinforcement learning networks, require substantial computation, which can impede their ability to generate immediate responses needed for intersection scenarios. These insights suggest a need for optimizing deep learning approaches that cater specifically to the unique dynamics at intersections, possibly by integrating more contextual data or employing models that balance complexity with computational efficiency. Further research could explore hybrid models or advanced training techniques that enhance the real-time capabilities of deep learning applications in vehicular systems. The challenge of minimizing the generation of fake trajectories while preserving vehicle privacy in low-vehicle-density scenarios remains to be addressed.