## 4 Discussion  
According to the approach derived in Section 2.2, the RET changes can be detected with CUSUM change detectors, which rely on the on-the-fly minimization effected by PHCDs. In order to detect all kinds of the RET transitions between the three RETs considered herein, the simultaneous use of six differently tuned PHCDs is necessary. In Section 3.2, we have demonstrated what happens to the functions \(g_{\cdots,\cdots,\cdots}^{\cdots,\cdots}\) and \(I_{\cdots,\cdots,\cdots}^{\cdots,\cdots}\) when the actual RET change is not what the detector is tuned to detect. In fact, in the examples given there, we have applied change detectors that were tuned to the inverse transitions. If one wanted to use the aforementioned PHCDs for the purpose of detecting not only the changes between different RETs, but also the actual RETs themselves, furthermore, wished to overcome the haphazard behavior of the \"off-the-tune\" PHCDs, there are promising possibilities; for instance, the respective functions \(h_{l}^{\cdots-\cdots}\) can be generated and considered within a sliding window, furthermore, several overlapping sliding windows can be used at the same time. In addition, these could be multi-scale windows. An artificial neural network (ANN) proposed for TS-based RET detection was presented in [26]. The ANN-based method made use of sliding multi-scale windows, and for these windows, TS histograms were calculated. The network proposed there could well be extended to input and make good use of the \"summaries\" of functions \(h_{l}^{\cdots-\cdots}\), rather than the TS histograms. These summaries could be of syntactic nature. A tool capable of exploring time series data for pattern and query search tasks, as well as for generating syntactic descriptions of the time series was proposed and demonstrated in [45]. The syntactic descriptions of the functions \(h_{l}^{\cdots-\cdots}\) should preferably be computed for sliding multi-scale windows. The TS-based RET change, the inferred actual RET within, and the complete inferred RET structure--i.e., a map layer, or sublayer--of an urban area could be utilized in various manners in automotive applications. First, the TS-based RET change, or the TS-based actual RET could initiate warnings to novice drivers, e.g., \"You are now driving in a downtown area.\" What actually is meant by this warning is as follows: \"The area might be uncrowded now, but in half an hour, or so it could turn very busy and could be loaded with intense car traffic. Therefore, find a parking place now, if want to stay in this area.\"  
It also hints at reducing speed to, say, 40 km/h. In a Res area, the respective warning could, for instance, instruct the novice driver to watch out for groups of children playing on the streets. In relation to the control of smart cars, the preferred speed could be set to some lower than 50 km/h speed in the Dt area, especially during and close to the usual peak hours. The maximum acceleration and deceleration values could be set to safer values. In relation to the ADAS/AD computations carried out on-board smart cars, particularly to the computations related to TS detection and recognition, a specific geometrical size range for TSs can be used. In narrow streets of historical districts, often smaller TSs are installed by the road authorities, and that size should be allowed in the TS verification phase of the computing. The detection of traffic lanes and the estimation of the distances to the TSs from the ego-car are examples for computations that implicitly make use of some spatial models of the road and its environment. In Res areas--at least in our country--multi-lane roads are infrequent, therefore simpler road structures/models should be matched against the camera images of the road scenery.