## 4 Machine Learning (ML)-Based Internet of Vehicles
### Applications of Machine Learning-Based IoV System  
The use of ML in IoV can be deployed into the IoV based on the different architectural layers. For instance, in the perception layer, ML-based application can be beneficial in the detection of moving objects (vehicles, pedestrians, cyclists, traffic signs, road markings, etc.) through input data from visual or sensors with high speed and accuracy [20]. Furthermore, ML can be applied to improve vehicle position accuracy by analyzing input data from GPS sensors and other location-based data. Such accurate and improved vehicle positioning could potentially reduce errors and enhance location information for IoV applications. Collision avoidance is another aspect of interest for ML-based applications in IoV. ML model can be built from input data from radar and cameras, and the model could accurately detect and classify stationary or moving vehicles or objects, predict their speed or trajectory, and alert other participants in the IoV system to avoid collision and ensure safety in the system. AV applications, in particular, have ranged from just mediated perceptions like traffic sign recognition, the detection of the road, and lane change to real-time integrated decision making from scene understanding [34]. Routing is another area where ML finds application in the IoV system. The ML model can be deployed to analyze traffic patterns, road and weather conditions, and user preferences, and make predictions for optimal route planning [111], evaluate traffic dynamics, and make intelligent decisions. Some of the outputs of a ML-based IoV could be in accident forecasting or prediction [112; 113; 114; 115]. ML-based IoV applied to routing can be effective in lowering travel time by minimizing traffic congestion [115; 116] and guarantee overall efficiency in IoV routing [117; 118]. In the network layer, one way the ML-based IoV is utilized is in network resource management. ML can enhance the selection of content cache at the edge network by learning from input data such as traffic patterns, user behavior, and content popularity [20; 119; 120]. Such intelligent decisions could yield low latency and reduce bandwidth consumption in the IoV system. ML can be integrated into the IoV physical, network, or infrastructure layer for effective attack, fault, and anomaly detection [20]. Data from sensors and network sources can be monitored continuously and analyzed for the possibility of a fault, attack, or anomaly in the IoV system. This could ensure overall system security and reliability as security threats or potential attacks, or system malfunction could be detected early and mitigated in real-time. An anomaly in an IoV system could be detected or estimated in the driver/vehicle behavior [121; 122]. Advanced ML approaches such as FL have been explored for privacy protection in a data fusion scenario for traffic state estimation application [123]. Vertical FL was leveraged to ensure that multiple collaborating parties could jointly train a model without compromising or exchanging their private data. However, authors worked on the assumptions that parties involved are honest and provided authentic and accurate data. Table 6 provides a summary of ML-based IoV applications and their commonly used models. Figure 6: A deep learning configuration for smart transport system application.