# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 2 Sensor Characteristics and Information Fusion
### Information Fusion of Radar and V2V Communication  
The V2V communication, however, has several limitations when it applies to automated driving. GPS receiver has poor position estimation performance when it enters a tunnel or urban shadowing area due to low signal strength and multipath propagation error. These situations cause degradation of target tracking performance which bring about dead-reckoning mode, and consequently producing larger errors. Depending on the transmission power and receiver sensitivity, transmission messages can be received up to a distance of 1 km [16]. However, obstacles, such as buildings in urban environments, the surrounding topography in rural environments or big trucks on highways might block the wireless signal and thus make a communication between vehicles not possible. ITS-G5 (European standard for V2V communication) uses Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA) to access the wireless channel and avoid the occurrence of packet collisions. However, in dense traffic scenarios, packet collisions occur and can lead to a drop in performance of a cooperative positioning approach [17]. Therefore information from V2V communication is often considered not to be reliable enough to initiate automated maneuvers. On the other hand, radar (camera and LiDAR systems as well) has its own limitations. Many heuristics are applied to the object detection, which leads to a compromise between sensitivity and false positive rates. Static objects or objects in far distances are very hard to detect reliably. Despite the shortcomings in both technologies, we found they are complimentary to each other. Radar can provide a constant passive scanning of the surrounding, catch all detection candidates and provides good relative positioning measurements. If those candidates happen to be V2V communication device equipped, the information from their DSRC messages can help to eliminate almost all uncertainties and enrich those candidates with much more additional information. In this way, we do not need to worry about the drops in V2V communication message reception and low quality of positioning from GPS. In Figure 3, in the high level fusion architecture, all sensor information is processed at the sensor level, making possible applications which have high standard modularity and simplicity. Therefore the high level fusion is proposed and it has been successfully and well described in automotive applications, such as adaptive cruise control [18] and for other active safety feature [19]. The demonstrated high level fusion architecture for the automated driving algorithm is composed of two. One is a local filter and the other is master filter as seen in Figure 3.  
For the radar data process, at a local filter, extended Kalman filters (EKF)-based interacting multiple model (IMM) has been implemented to compute the remote vehicles' dynamic states with relative position and velocity. A state augmented estimation algorithm to compensate the communication latencies is used in the V2V communication local filter. To express dynamic motion of the remote vehicle, we derive a kinematic vehicle model. After then, to describe the remote vehicle's  
Figure 3: High-Level Information Fusion Architecture. CAN bus, standard measurement model is derived and the vehicle information is transmitted. The state vector of the local filter of V2V communication is derived in (1):  
\[\mathbf{x}_{r\sigma}^{v2v}=\left[ \begin{array}{cc}p_{rv,x}^{v2v}&p_{rv,y}^{v2v }&\theta_{rv}^{v2v}&v_{rv,x}^{v2v}&\gamma_{rv}^{v2v}&a_{rv,x}^{v2v}&\gamma_{rv}^ {v2v}\end{array} \right]^{T} \tag{1}\]  
Here subscript \(rv\) is remote vehicle, \(p\) is the relative position between two vehicles, \(\theta\) is the relative yaw angle between two, \(v\) is the longitudinal speed, \(\gamma\) is the yaw speed, \(a\) is the longitudinal speed, and \(\dot{\gamma}\) is the derivative term of yaw speed. In Figure 3, fusion of V2V communication track and radar track is described in the master filter.