## 5 Convergence of ML and Blockchain in IoV
### Prospects of ML and BC Convergence for IoV System  
#### 5.1.1 Traffic Incidence-Related Applications  
ML and BC have been integrated for traffic incident detection [105]. In this study, the authors proposed a framework to manage traffic incidence and provide evidence for road incidence situations. The combination of cooperative event correlation and trust model (Long Short-term Memory (LSTM) and Bayesian model) was employed to predict the next event label at vehicle based on past sequence, while a three-layer hierarchy blockchain framework was incorporated to enhance the security of the traffic incident reporting application. Compared to existing approaches for traffic incident management, the study reported that average network overhead was lowered by 66.24%. The authors also highlighted the significance of the ML (LSTM) approach by comparing the performance of the approach with and without the machine learning in terms of the system overhead.  
In a similar study for effective traffic flow predictions, [PERSON] et al. [140] proposed a consortium blockchain-based federated learning framework. The authors leveraged a dBFT-based consortium blockchain to develop a decentralized FL framework. While the blockchain provided a secure defend against poisoning attacks, the FL approach ensured the protection of the privacy of the vehicle data. The results shows that effective robustness to data and poisoning attacks. Furthermore, the study illustrated the impact of blockchain integration by comparing model performance that integrates blockchain and without blockchain. As seen in Figure 8, blockchain incorporation into the proposed scheme ensured that attack success rate (ASR) stayed minimum, compared to the scheme without blockchain, thereby showing the efficacy of blockchain integration with machine learning methods in achieving the optimization of IoV applications. A similar thread has also been highlighted for autonomous driving applications, where blockchain proved robust against a malicious attack with increasing node participation [141]. This is illustrated in Figure 9. It can be noticed that as the number of nodes increased, the success rate remained constant with the proposed blockchain approach, while the collective learning without blockchain showed a decline in the success ratio as the nodes increased.  
Figure 7: Summary of key contribution of blockchain and machine learning in IoV. Adapted from [PERSON] et al. [139].  
For other traffic scenario applications, the DL approach has been combined with blockchain to enhance cooperative positioning for vehicles [142]. The authors reported a robust and secured framework with improved distance prediction performance. FL was deployed in convergence with blockchain for the effective privacy preservation of traffic data [143]. The proposed model showed improvement in terms of model accuracy, computation overhead, and effective consensus mechanism in securely preserving the privacy preservation of traffic data. The proposed model showed improvement in terms of model accuracy, computation overhead, and effective consensus mechanism in securely preserving the privacy preservation of traffic data.  
Figure 8: Comparison of the impact of blockchain on the different proportions of malicious vehicles on the attack success rate (ASR). Adapted from [PERSON] et al. [140].  
Figure 9: Comparison of two approaches with blockchain integration and without blockchain integration for autonomous driving lane change application. Ratio of successful driving with malicious nodes. Adapted from [PERSON] et al. [141].  
privacy of traffic data. Similar findings were observed for the applications to ensure a decentralized traffic information collection system [144], for crowdsensing traffic data collection in 5G IoV systems [145], for efficient crowdsensing traffic data management [146], for effective real-time traffic prediction [147], and to achieve a secure incentive-enabled traffic information-sharing scheme [148].