# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 5 Collaborative Learning Technologies
### Ensemble Learning for IoV
They consider the privacy protection of vehicle data and propose a collaborative approach based on private classifiers that are installed on each vehicle. While each vehicle is a weak classifier, using the communications among neighboring vehicles, the results from multiple vehicles are merged with an ensemble learning method, resulting in a much better intrusion detection system. Simulation results show that the ensemble classifier can achieve 96.94% accuracy without violating the user's privacy. In Ref. [129], an ensemble learning approach is applied in the path prediction of obstacle vehicles for the purpose of enabling a more advanced driver assistance system. Three different types of sensor data--namely, light detection and ranging (LiDAR), camera, global positioning system (GPS)--are considered trained by the ensemble learning. A recurrent neural network (RNN) is combined with ensemble learning to capture temporal behaviors of vehicles and environments. The authors show that the ensemble learning can improve the robustness of prediction with a network model of small size, which ensures its validity in dynamic road scenarios. [PERSON] et al. [130] discuss the pedestrian detection problem in railway driver support systems where existing object detection technologies do not work well. They propose a three-stage system based on ensemble learning. In the first stage, various non-deep feature-classifier models are used to generate bounding boxes. Based on the results of the first stage, the second stage applies multiple deep CNN models to generate multiple individual adapted classification results. The final stage employs an ensemble learning method to combine the individual results to find a better detection result. The ensemble-learning-based system can significantly improve the detection accuracy as compared with well-used existing models. [PERSON] et al. [131] propose an ensemble-learning-based approach for short-term vehicle traffic prediction. They use an ensemble learning model with the long short-term memory (LSTM) technique. The approach consists of two stages. In the first stage, different LSTM models repressing different effects of different time lags on the prediction are trained separately. In the second stage, ensemble learning is used to determine the weight coefficient of each LSTM model, and combine different models to make the final decision. They apply six different highway traffic datasets from Seattle, USA, to show that the ensemble-learning-based approach could perform better than six existing traffic flow prediction models. [PERSON] et al. [132] propose WBELA, a weight-based ensemble learning algorithm, for intrusion detection in heterogeneous vehicular environments by identifying abnormal messages in the controller area network bus network. They use support vector machine (SVM), K-neighborhood (KNN), and decision tree (DT) methods as the basic classifiers. First, the CAN messages are processed by the basic classifiers for preclassification. Based on the preclassification results, an ensemble learning algorithm is used to optimize the weight of each classifier in order to reach the maximal accuracy and minimal errors. Simulation results are presented to show the advantage of the algorithm as compared with existing baselines in terms of precision and false positive rate. For the purpose of enabling context-aware computing of smartphone applications, [PERSON] [133] discusses the problem of transportation mode detection of smartphone users, and proposes a method based on an ensemble learning algorithm. The method uses three different basic classifiers, specifically, random forests (RF), gradient boosting (GB), and decision trees (DT). Each classifiers votes independently, and the ensemble learning algorithm makes a better prediction by taking the majority vote of these three classifiers.