# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 3 Non-Cooperative Relative Positioning
### Vision-Based Solutions  
Machine vision techniques are able to detect and localize objects by processing the images drawn from an imaging device like a camera. Although vision can provide highly valuable information about the environment, image processing techniques are complicated, computationally expensive and still under research. For automotive vision sensors, processing of road scenes can provide accurate information other sensors fail to obtain. Already today, cameras are being introduced in high-class vehicles for detecting lane marks and offer lane keeping assistance or lane departure warning systems. Furthermore, automatic traffic sign recognition systems are already able to inform the driver about the current speed limit and other types of hazards along the road. Further on, cameras are recently incorporated for object detection. Especially the detection of pedestrians, which would otherwise fail with radar sensors or laser scanners, can be accomplished with vision-based solutions. Generally, for a camera system, it is relatively easy to measure distances in the lateral, cross-track, direction. Depending on the resolution of the camera sensor, a certain distance-dependent lateral accuracy can be obtained. Vision processing is less effective for calculating the longitudinal, along-track, distance of an object. Monocular systems use one sole camera and exploit the geometry of the scene, along with the knowledge about the size of the objects, in order to estimate the longitudinal distance. On the contrary, in stereo-vision-based approaches, two cameras are able to directly estimate the 3D coordinates of an object. A survey on object detection algorithms for stereo cameras can be found in [33], while [34] gives a sound review on pattern analysis for vehicle detection. A comprehensive overview and literature survey on monocular and stereo-vision vehicle detection techniques including tracking and behavioral modeling is presented by [PERSON] et al. [35]. In their work, vehicle detection using vision is briefly compared with complementary technologies, as for instance radar and LIDAR. The use of the occupants' smartphone camera for vehicle distance estimation has been proposed in [36; 37; 38]. However, the inexpensive camera technology and the limited processing capabilities might, depending on the smartphone model, impose limitations on the performance regarding relative position accuracy, reliable detection of vehicles and the number of tracked objects. Camera systems have sensing ranges from 4 m-80 m and a field of view of \(\pm\)20\({}^{\circ}\) in azimuth [39]. However, stereo-vision and depth capability rapidly drop with distance and are limited to a range of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor** & **Resolution** & **Range** & **Azimuth Angle** & **Accuracy** & **Cycle** \\ \hline \hline PMD CamBoard & 200 \(\times\) 200 & 7 m & 40\({}^{\circ}\) & -’,’ - & 60 fps \\ PMD CamCube & 200 \(\times\) 200 & - & - & -’,’ - & - \\ SwissRanger SR4000 & 176 \(\times\) 144 & 10 m & 40\({}^{\circ}\) & 1 cm, -’, - & 50 fps \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time-of-flight cameras. up to 40 m [40]. In [41], the authors propose a 360\({}^{\circ}\) vision-based system with three cameras around the vehicle. While radar and laser scanners have rather constant ranging errors over distance, the range accuracy of camera systems typically decreases quadratically with distance [42]. At 3 m, a range error of 5 cm is stated in [43], while the error at 40 m is around 3 m. [PERSON] et al. achieve an accuracy of around 1% at ranges from 10 m to 95 m with a stereo camera system [44]. [PERSON] et al. apply deep learning techniques with a monocular camera to achieve a 6 m range error up to a 60 m distance [45]. The error increases to 20 m at 120 m.  
[PERSON] et al. have concluded in [46] that a monocular vision sensor offers sufficient accuracy in range and range rate to be used in an ACC application.