## 4 Experimental Results and Analysis  
### Data Collection  
Data collection is of paramount importance in the context of studying vehicle emissions and road scene analysis. By collecting comprehensive and accurate data, we can gain valuable insights into the environmental impact of vehicles and their relationship with road conditions. The integration of multiple data sources, such as emissions, vehicle speed, and GPS coordinates (altitude, longitude, latitude) allows for a holistic understanding of the factors influencing emissions and their spatial distribution within a road network. In our data collection efforts, we conducted numerous experiments in the city of Rouen, France, with ESIGELEC (Engineering High School located in Rouen) as the primary source location. To ensure the dataset's robustness and real-world applicability, we selected a Renault diesel car as the vehicle for our experiments, providing a specific vehicle type for analysis, and we selected multiple destinations, including Bosguouet, Brionne, Neubourg, and Yvetot, representing diverse road networks and driving conditions. For each destination, we collected data for both the outbound and return journeys, and to comprehensively assess emissions variations based on different driving scenarios, we focused on two distinct route types: one adhering to Google Maps' fastest route recommendation and the other aligned with an eco-friendly route suggestion. This approach enabled us to investigate the emissions implications stemming from different route choices, shedding light on the trade-off between speed and environmental impact. By traversing multiple routes and destinations, we accounted for the heterogeneity of road types, traffic congestion levels, and driving behaviors prevalent in the region. This approach allowed us to capture a wide range of driving scenarios, including urban areas, highways, and rural roads, contributing to a more comprehensive and representative dataset. The diversity of routes ensured that the dataset included different traffic patterns, environmental factors, and road infrastructure characteristics, making it suitable for studying eco-mobility and transport solutions in real-world settings. The Portable Emission Measurement System played a crucial role in our data collection process. It was installed at the rear of the vehicle as shown in Figure 2. With a frequency of 1 Hz, the PEMS system captured emissions, vehicle speed, and GPS coordinates at a high temporal resolution. This high-frequency data collection allowed us to analyze emission patterns and variations during different driving scenarios, providing detailed insights into the environmental impact of vehicles in real time. The integration of vehicle speed and GPS coordinates into our dataset further enriched the collected data. The speed data provided valuable information on driving behavior, including acceleration, deceleration, and average speeds. These data allowed for a comprehensive analysis of how speed influences emissions and traffic dynamics. The GPS coordinates provided precise location information, enabling the mapping of emissions to specific road segments and facilitating the identification of emission hotspots and areas with significant environmental concerns. Table 1 presents an overview of the PEMS data in the VAD, highlighting some examples of vehicle gas emissions and other relevant variables. In addition to collecting vehicle emissions and data, we have also captured images of the road scene. These images provide valuable visual information about the road infrastructure, traffic conditions, and the environment. For image collection, we used the Intel RealSense camera, which was mounted on the front of the vehicle to capture a forward-facing view as shown in Figure 3.  
The Intel RealSense camera is a powerful RGB (Red Green Blue) camera that captures high-resolution images with rich color information.