# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 5 Collaborative Learning Technologies
### Clustering with Unsupervised Learning  
While k-means algorithm or its extensions are widely used, there are also some other clustering approaches. Here, we first discuss the use of k-means algorithms for IoV, and then show the applications of other clustering techniques. [PERSON] et al. [135] discuss a license plate (LP) recognition problem where multiple types of LPs, namely Arabic-Egyptian license plates and English license plates, are considered. A k-means clustering algorithm is applied to segment the LP characters. Experimental results show that their approach can achieve higher recognition accuracy than existing alternatives. [PERSON] et al. [136] present a deep-learning-based license plate recognition model that uses a k-means algorithm in character segmentation, and uses CNN in character recognition. Experimental results on three different datasets show the advantage of the model compared with existing baselines. [PERSON] et al. [137] study the challenges in recognizing the decision-making style of a driver. They introduce an approach that integrates an unsupervised learning approach, specifically, a k-means algorithm, with a supervised learning approach, a k-nearest neighbor (KNN) algorithm. They demonstrate that their approach can improve the traditional KNN algorithm with 72.67% faster recognition time and can shorten the recognition time by more than 72.67%, with higher accuracy. [PERSON] et al. [138] discuss the challenges of simultaneous localization and mapping in mobile environments, and employ a k-means algorithm to conduct image segmentation for the purpose of better processing dynamic regions. They use experiments to demonstrate the validity of the approach in terms of trajectory accuracy and computing speed. [PERSON] et al. [139] discuss the clustering problem of a channel impulse response for 60 GHz mmWave multipath channels in vehicular environments. Considering that the performance of the k-means algorithm is dependent on the value of k, they combine the k-means algorithm with a time-amplitude clustering algorithm to group multipath components based on their time delays and amplitudes. [PERSON] et al. [140] discuss a similar multipath clustering problem in vehicle-to-infrastructure (V2I) channels. They employ a variant of the k-means algorithm, namely, K-Power-Means, to conduct the clustering of multipath components. An algorithm is also used to find the best number for clusters before conducting the clustering. [PERSON] et al. [141] study the multi-hop broadcast issue in VNs, which is considered to be useful for disseminating safety-related messages. They introduce a vehicle-clustering-approach-based k-means algorithm. The selected cluster heads are used as forwarder nodes in multi-hop broadcasting. The geographical position of vehicles and inter-vehicle distance are considered in the clustering algorithm. Since the signal quality and vehicle mobility are not discussed in the clustering algorithm, this clustering algorithm requires an enhancement for handling complex vehicular environments. [PERSON] et al. [142] discuss the data transfer problem in for delay-tolerant vehicular applications, such as environmental map construction, traffic monitoring, weather sensing, and so forth. They define some geographical regions as black spots where the transmission rate should be reduced due to insufficient communication capability. Based on vehicular big data, a k-means algorithm is used to conduct clustering of black spots. [PERSON] et al. [143] consider the use of UAVs to improve the communication performance between small cell base stations. The focus on the optimal placement of UAVs for the purpose of serving more small cell base stations. A k-means algorithm is used to solve the problem by considering multiple metrics such as the achievable data rate, available bandwidth, and the capability of UAVs. Numerical results are used to show the superiority of the algorithm over the conventional approach in terms of the achievable data rate and spectral efficiency. [PERSON] et al.