## 2 System Overview
### Automotive Remote Sensors  
The types of sensors used for driving environment perception and object tracking in recent years include radars, cameras, and lidars. These sensors perform detection of objects in the vehicle surroundings and provide information on the object state (e.g., relative position and relative speed) and object classification. Some of the most common advanced driver assistance system (ADAS) applications enabled by the environment perception technology include forward collision warning (FCW), automatic emergency braking (AEB), adaptive cruise control (ACC), lane departure warning (LDW), and lane keeping assist  
Figure 1: Functional blocks of the proposed cooperative environment perception system. system (LKAS). For this study, we equipped our test vehicle with radar and camera sensors that have already been incorporated into production vehicles. Automotive radar, which is an active ranging sensor designed for detecting and tracking remote targets in the surrounding environment, is one of the most used ranging sensors for ADAS functions these days. Automotive radars are capable of providing the relative position and speed information about the objects located within the sensor FOV, and they operate even in adverse weather conditions such as rain, fog, and snow. Two frequency bands, 24 GHz and 77 GHz, have been predominantly exploited for automotive radars [30; 31; 32]. The 24-GHz band is mainly used for short-range radar systems with a detection range up to 30 m [30; 33], while the 77-GHz band is mainly used for long-range radar systems with a detection range up to 250 m [31]. A frequency modulated continuous wave (FMCW) radar is the most common type of automotive radars used for remote sensing of surroundings [31]. FMCW radars transmit a frequency-modulated signal in a continuous manner. The frequency of the signal changes linearly with time, and this enables accurate time measurement based on the frequency difference between the transmitted and received signals, which can be converted into range information. The specification of Delphi ESR 2.5, the multimode radar system utilized for the driving experiments in this study, is shown in Table 1.  
Computer vision techniques are increasingly utilized for automotive safety applications. Camera systems with computer vision methods perform detection and localization of the objects that have been captured in the camera images. It is also possible to extract valuable information on the driving environment, such as lane marking positions and road curvature, from the images obtained with the cameras on board the vehicle. A comprehensive review on computer vision techniques for vehicle detection, tracking, and behavior characterization is presented in [34]. The most notable camera system that has been widely incorporated into production vehicles is that of Mobileye [35]. The longitudinal distance to the surrounding vehicle can be determined based on the image position of the target and the detected target width or height [36; 37; 38]. Based on [36], the accuracy of the range estimated with the camera system decreases quadratically as the distance to the object increases, while the error percentage increases linearly, such that a 5% error in range is expected at 45 m and a 10% error in range is expected at 90 m. The characteristics of the Mobileye 630 camera system that was installed on our test vehicle are summarized in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Delphi ESR 2.5**} \\ \cline{2-3}  & **Long-Range** & **Mid-Range** \\ \hline Frequency band & 76.5 GHz & 76.5 GHz \\ Range & 175 m & 60 m \\ Range accuracy & 0.5 m & 0.25 m \\ Angular accuracy & 0.5 deg & 1.0 deg \\ Horizontal FOV & 20 deg & 90 deg \\ Data update & 50 ms & 50 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Automotive radar system specifications.