# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 5 Sensor Fusion for Relative Vehicle Positioning  
As we have seen, every relative positioning technology for vehicles has its advantages and disadvantages regarding its performance in terms of accuracy, reliability, availability, range or field of view. Consequently, an intelligent strategy is to combine the information from different sensors and localization technologies in order to obtain a more robust estimation of the target vehicle's position. This is usually accomplished by applying sensor fusion techniques, where the information is combined in a way that the resulting estimation uncertainty is lower than the uncertainty of each of the information sources individually. Ideally, the limitations of one type of sensor are overcome with the strengths of a complementary sensor. For instance, a common approach is the fusion of radar sensors and cameras. The limited sensing range of camera systems and the absence of range rate measurements can be enhanced with the use of a radar sensor, which, on the other hand, profits from the good lateral resolution and feature richness of camera systems. This approach has been followed by [PERSON], [PERSON] and [PERSON] in [128; 129; 130] using monocular cameras. The combination is found to increase reliability in vehicle detection and overall robustness of the relative positioning system. [PERSON] et al. achieve a 92% detection rate with no false alarms with a two-stage fusion of mm-wave radar and monocular camera in [131]. A stereo-camera system is combined with two radar sensors by [PERSON] in [132]. As explained in [133; 134], a radar sensor can profit from the precise lateral accuracy of a laser scanner, while contributing with an accurate relative velocity estimate of the target vehicle and a higher robustness against climatological adversities. A multi-sensor platform consisting of vision and LIDAR has been presented in [135]. The authors acknowledge that although an increased detection rate and correct target classification are accomplished, bad weather conditions affect both sensors equally. All non-cooperative approaches, i.e., radar, laser scanner and camera systems, are fused by [PERSON] et al. for collision mitigation by autonomous braking in [136]. [PERSON] et al. evaluated a two-layer sensor-fusion platform consisting of 14 asynchronous on-board sensors, including radar, LIDAR and vision cameras [137]. They claim a 93.7% detection rate with 5.7 false positives per minute. [PERSON] et al. present a sensor fusion approach for detecting obstacles and other vehicles based on radar, laser scanners and cameras [138]. Among the challenges faced when combining different perception sensors, there is the need to synchronize adequately the various measurements and to correct for the displacement due to the mounting location on the vehicle. An intelligent approach is to take advantage of the complementary nature of on-board ranging sensors and cooperative approaches based on GNSS and V2V communication. Cooperative positioning can help on-board sensors in different ways. On the one hand, it makes it possible to identify target vehicles and to track them over time, even when disappearing behind other vehicles or obstacles. It is inherent to a cooperative approach based on CAM or BSM over V2V that each vehicle has a unique identifier. Further on, a cooperative approach can extend the perception range of the on-board sensors behind curves or crests in rural areas. Even in urban environments, CAMs from vehicles behind buildings at intersections can be successfully received. Additionally, a cooperative approach will make it needless to equip a vehicle with multiple sensors to get an all-around view of the surrounding situation. On the other hand, since on-board ranging sensors have, in general, better accuracy than GNSS-based cooperative solutions, they can stabilize the relative position estimation during GNSS outages, as for instance in tunnels or against multipath propagation in urban canyons.