# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 5 Proposed CCAM Solution for Autonomous Driving
### Sensors and Devices
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor Type** & **Description** & **Parameters Measured** \\ \hline Car Parking & In parking lots, these sensors can be of various use, including finding a preferred parking space for drivers, identifying wrong parking, parking violations and expired tickets. Up to 300 spaces can be covered by one sensor and the range can be as long as 400 m. The raw video streams are only processed on-board in an effort to increase privacy. & Total parking spots, free spots, and occupied spots \\ \hline Traffic Analysis & In highways, the crowded traffic can be caught/recorded via these sensors and it can capture as wide as four lanes per camera. The frequency with which the data are recorded can be modified according to needs, e.g., per month, week, day, hour, or per minute. The user can view the evaluations through camera image on a display, via email reported regularly by a reporting engine or sent through CSV file. & Classification of vehicle types and automatic counting of vehicles \\ \hline Activity Analysis & These sensors can recognize/distinguish different automobile activities at certain geographical locations. Information regarding motion and most lingered places can be traced via analysis of sensory data. The information of activity patterns (pedestrian walking, cycling etc.), gathered via these sensors may assist in dynamic decision making for AD, e.g., scheduling traffic light controls or LDM trajectory planning. & Visualization of motion and dwelling time, objective measurement of hot spots, compilation of statistical evaluations with adjustable duration and intervals of evaluation. \\ \hline Environmental & The effects of AD on the environment can be analyzed via these sensors. Moreover, different parameters and their relationship can be studied through interpretations and evaluations of sensory data collected from environmental and traffic analysis sensors. & Air quality index, NO\({}_{2}\), NO, O\({}_{3}\), particulate matter (PM1, PM2.5, PM10) \\ \hline Road condition & These sensors are installed on masts (e.g., streetlamps) or bridges and can be used as optical technologies for the purpose of visual information of site and measure remote temperature. & Road status (dry, damp, wet, ice, snow, and chemically wet), road surface temperature, snow height, and water film height \\ \hline Traffic Light & These sensors are integrated with the existing traffic control system and use DSRC communication technology for short range messages delivery between traffic light, vehicles, pedestrians, and other road users. & Traffic light status, e.g., red, yellow, green \\ \hline Camera-based Sensors & The type of sensors visually scans its surroundings and capture images and videos that contains important and relevant information. The information is then used for perception creation, object detection, context or scene understanding, object tracking, etc. & Object detection, road status, and classification. \\ \hline LiDAR Sensors & These sensors are popular to perform 3D mapping of the surroundings. The 3D mapping is in point-cloud format and operations over these point-cloud depends over the number of layers in the sensor. This is to say, LiDAR comes with various layers detailing the objects it scans. & Field-of-view, point density, and depth level based on scanning layers \\ \hline RADAR Sensors & These sensors are usually used for detection objects that are moving or stable. Through radar, the object can be tracked by determining their distance, angle, and velocity. & Range and distance to the detectable object and object tracking. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Types and descriptions of the sensors. Figure 4: A look into some of the visualizations of activity and environmental sensors.