# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 6 Validation of the CCAM for Autonomous Driving
### A Use-Case Experiment  
In this experimental use-case, we discuss how a use-case can be implemented in order to investigate the impact of the external perception created by the on-road deployed infrastructure. This work is supported by the CCAM infrastructure, which is fully developed  
Figure 8: Frequency of technical documents in 3 GPP releases (14–17) supporting V2X communication. Figure 9: Frequency of system and network support for AD usecase groups through cellular technologies. (physically through SCAD Lab in UAEU, UAE), and will be deployed in the near future. For different use-cases of autonomous driving, we have conducted a set of experiments to study the impact of external information. These experiments were carried out over a dataset from Berlin. The dataset captures a famous Berlin roundabout and is relevant to the CCAM infrastructure. A noteworthy point here is that parts of the deployment that collects the data do not track the vehicles, and there are no sensors for road conditions. Additionally, the different cameras could not track vehicles throughout their path through the intersection. As a further point to be noted, activity analysis sensors capture the highest degree of detail. For the purpose of creating valid simulation models, this tracking is extremely useful. The vehicle count is performed by traffic analysis sensors on the configured segments of roads. A one-minute interval is used to aggregate counts and a classification by vehicle type is available for some segments. We started with the manual data analysis and carried out some sanity checks. This led us to inspect the data for visually validating the expected patterns. Further, a consistency check was carried out and an assessment of the system accuracy was conducted by:  
* Analyzing properties, data type, aggregation, and time resolution of the data,
* Statistic aggregation and comparison of reference values,
* Using spatial correlations to analyze time relationships and constraints in the data. Next, we performed vehicle counting. The number of vehicles is counted through extracting the vehicles from the image streams. Similarly, we performed vehicle classification. The types of vehicles are detected in the image streams. It is note worthy that the vehicle types change drastically at certain times in the data. As a result, we opted to focus on the vehicle count (aggregated) for now and furnish our future research work with vehicle type classification. After accumulating the recorded data in minute intervals for traffic analysis, we expected the following:  
* To perform matching between data sources, e.g., working days vs. holidays, normal hour of the day and peaks, daily averages, etc.,
* To detect temporary closures without difficulty,
* To have simple correlations between sensor stations,
* To perform accurate detection of traffic lights. With the help of interactive plots, we visualized the dataset by selecting time range, sensor locations, and smoothing options that we could freely select. Next, we selected a simple Neural Network (NN) regressor for traffic volume predictions. The NN regressor was trained for first 20 days and the total available data size is 38 days. We aim to have a simple and small network for producing acceptable predictions. Therefore, the experiments consider various input formatting, number of layers, and neurons. Consider Figure 10, which shows the predictions. It goes without saying that the nature of an application decide about the required accuracy. In what follows next, we provide details about the neural network and some features, as follows:  
* A total of 100 neurons;
* Solver (L-BFGS);
* Activation function (tanh);
* Minute of the day (0-1439);
* Day of week (Monday-Sunday (0-6));
* Is a holiday (true, false);
* Vehicle count of that minute (number)--the class label/output. Though the above mentioned features are important, there exist other promising features as well.