The connected vehicle is becoming a reality, and both centralised applications provided through telecommunication infrastructure, and cooperative applications achieved directly by the vehicles through vehicle-to-vehicle communications are gaining a lot of interest from the public. Though several solutions have been investigated to ensure identification and privacy protection of vehicles participating in these applications, only a few works address the recording of exchanged messages from an audit perspective, especially in the context of vehicle-to-vehicle communications only. This paper, proposes a review of the literature on vehicular communications, and explores particularly the solutions envisaged for audit in this context, such as the Blockchain technology. We also point out the most challenging issues that should be addressed in order to achieve an effective deployment of this latter in vehicular communications, especially in the vehicle-to-vehicle communication context, which should help in developing an effective Blockchain-based audit strategy.

Vehicular communications can be achieved through the infrastructure (Vehicle-to-infrastructure network, V2I), as well as directly through vehicle-to-vehicle communication (V2V) via ad hoc networks. In V2V communications, the routing protocols are designed in order to optimize the dissemination of messages. This paper presents an evaluation of routing protocols such as the Optimized Link State Routing (OLSR), Ad hoc On-demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Geographic Routing Protocol (GRP), while considering both vehicular safety application requirements and mobility models based on real-world traces of vehicular traffic. The results show that, though proactive routing protocols perform better in this context, the four routing protocols fail to fulfill the safety application requirements on the delay metric even for a reasonable number of vehicles.

The cooperative vehicle paradigm offers new opportunities for enhancing various vehicle functions through distributed applications. The localization function is one of the most important since its services are needed by numerous applications ranging from driver navigation to autonomous vehicle guiding. Though the GPS (Global Positioning System) provides honorable service for driver navigation, its precision is not accurate for allowing an autonomous vehicle to localize itself correctly on the road lanes. Recently an ego-localization technique where a group of vehicles exchange their position and the related correction through V2V communications has been proposed in order to enhance the precision of the 10- cation of each node in the group. In this paper, we evaluate the performance that this application can expect from V2V communication services supplied by the CBL (Chain-Branch-Leaf) clustering scheme. The simulation results show that CBL achieves delays and packet delivery ratio adapted to various rates of the ego-localization application traffic.

A method derived from the Sequential Monte Carlo approaches is proposed here to solve the vehicle detection and tracking problem using a scanning laser rangefinder. The originality of this approach lies in a joint detection and tracking of the objects that avoid the usual pre-detection stage. The proposed modeling is strongly nonlinear. To improve the efficiency of the solution, we use a Rao-Blackwell particle filter: the non-linearity of the state-space equations is taken into account by a particle filter and the linearity is optimally processed by a Kalman filter. The solution of the proposed modeling is based on a matched filter (to the object) which uses a predefined vehicle model. A central point here is to calculate the weights of the matched particle filter according to the vehicle model. The efficiency of the method is shown in terms of estimation accuracies and detection.