Martine Wahl

Chain-Branch-Leaf: A clustering scheme for vehicular networks using only V2V communications

Abstract The development of vehicular networks leads to a plethora of new applications that are already becoming essential to the drivers and to cooperative vehicles. One important condition to the continuity of these new services is the existence of an ubiquitous network access, which cannot be completely guaranteed. In this context, developing reliable ad hoc vehicle-to-vehicle communications is a good alternative that also becomes mandatory. This work proposes a clustering scheme that combines the information on road configuration, vehicle mobility and link quality in order to build a structure similar to a vehicular network infrastructure, while relying only on the vehicles. This clustering scheme can be integrated into any reactive, proactive, or geographic ad hoc routing protocol in order to optimize the flooding of messages and simplify routing operations. The evaluations realized through simulation show that the proposed clustering scheme allows creating and maintaining a stable backbone during a significant time. Moreover, the simulation results show that the proposed clustering scheme reduces significantly the impact of broadcast traffic flooding in comparison with well-established techniques such as multipoint relaying, to mention few.

Use of contextual information by Bayesian Networks for multi-object tracking in scanning laser range data

This paper presents a new method to improve the perception of the environment of a vehicle. The aim here is to detect the vehicles using a scanning laser range finder and track the detected objects at each time. This contribution can be considered as an element of a global vehicle-to-vehicle (v2v) surveillance system where the on-board system receives warnings from the other local systems. It allows to extend the effective surveillance field and as a consequence to provide a faster reaction of the vehicle (collision avoidance or mitigation). To deal with this multi-target tracking problem, we focus on the Joint Probabilistic Data Association (JPDA) methods. Their particularity lies in their ability to take into account the probabilistic characteristics of the detector (detection and false alarm probabilities). Whereas in many works the detection probability is set up once, our contribution is to propose a method that dynamically estimates for each object the detection probability using the contextual information modeled by a Bayesian Network (BN-JPDAF).

Using Real-World Car Traffic Dataset in Vehicular Ad Hoc Network Performance Evaluation

Vehicular ad hoc networking is an emerging paradigm which is gaining much interest with the development of new topics such as the connected vehicle, the autonomous vehicle, and also new high-speed mobile communication technologies such as 802.11p and LTE-D. This paper presents a brief review of different mobility models used for evaluating performance of routing protocols and applications designed for vehicular ad hoc networks. Particularly, it describes how accurate mobility traces can be built from a real-world car traffic dataset that embeds the main characteristics affecting vehicle-to-vehicle communications. An effective use of the proposed mobility models is illustrated in various road traffic conditions involving communicating vehicles equipped with 802.11p. This study shows that such dataset actually contains additional information that cannot completely be obtained with other analytical or simulated mobility models, while impacting the results of performance evaluation in vehicular ad hoc networks. Keywords—MOCoPo dataset; mobility models; vehicular ad hoc networks; simulation; performance evaluation

Model-based detection and tracking of vehicle using a scanning laser rangefinder: A particle filtering approach

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.

Multiple target detection and tracking by interacting joint probabilistic data association filter and bayesian networks: Application to real data

This paper proposes an algorithm of multiple target detection and tracking on road, developed for the laserscanner data. It is based on Bayesian networks for calculating the detection probability of target used in a JPDA filter. We propose a method based on the integration of detection probability of target in the JPDA filter, in which the joint probabilities of associations are calculated for multiple target. It then solves the problem of founding one or more observation in more than one gate of target by constructing hypothesis. A Bayesian network is used to determine this probability of detection. It includes a target model and takes into account the contextual information such as the size of the target, the distance between targets and the center of the sensor characteristics (position, measurement uncertainty). The method is applied to real data provided by a scanning laser sensor with multiple targets. as well as the possibility that an object enters and leaves the scene.

IEEE 802.11a performance for infrastructure-to-train communications in an underground tunnel

The deployment of wireless technologies for public transport may imply several constraints due to specific environment, such as tunnels. In this paper, we evaluate the performance of IEEE 802.11a based networks deployed for infrastructure-to-train communications in underground tunnels. The signal attenuation specific to the tunnel propagation is taken into account. We propose an optimization of the handover detection mechanism to minimize the connectivity loss when the train moves between Access Points. We propose a performance evaluation based on OPNET simulator.

Bayesian Networks and Probabilistic Data Association Methods for Multi-Object Tracking: Application to Road Safety

This paper presents a Bayesian network-based approach to multisensor multitarget detection and tracking problem. The aim here is to propose an improvement of the probabilistic data association approach that takes into account contextual information about the scene. This information is modeled by a Bayesian network that allows a dynamic estimation of the detection probability of the PDA. Our approach is then applied to synthetic data from scanning radar that is mounted on a moving vehicle. The aim is to detect the surrounding objects and track them through the sequence.

Data pre-processing for obstacle detection in automotive applications

We have worked on the design of a VAN (value-added network) system used in the French PROMETHEUS PROCHIP obstacle detection car demonstrator. This centralized demonstrator architecture shown its limits in 1994. Then we decided to compress the sensor data before conveying them via the network. This paper reminds of our contribution in PROCHIP project and presents our radar database, including the experimental and simulated data, and the radar data pre-processing.

Performance evaluation of AODV, DSR, GRP and OLSR for VANET with real-world trajectories

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.

Performance Evaluation of 802.11p-Based Ad Hoc Vehicle-to-Vehicle Communications for Usual Applications Under Realistic Urban Mobility

In vehicular ad hoc networks, participating vehicles organize themselves in order to support lots of emerging applications. While network infrastructure can be dimensioned correctly in order to provide quality of service support to both vehicle-to-vehicle and vehicle-to-infrastructure communications, there are still many issues to achieve the same performance using only ad hoc vehicle-to-vehicle communications. This paper investigates the performance of such communications for complete applications including their specific packet size, packet acknowledgement mechanisms and quality of service requirements. The simulation experiments are performed using Riverbed (OPNET) Modeler on a network topology made of 50 nodes equipped with IEEE 802.11p technology and following realistic trajectories in the streets of Paris at authorized speeds. The results show that almost all application types are well supported, provided that the source and the destination have a direct link. Particularly, it is pointed out that introducing supplementary hops in a communication has more effects on end-to-end delay and loss rate rather than mobility of the nodes. The study also shows that ad hoc reactive routing protocols degrade performance by increasing the delays while proactive ones introduce the same counter performance by increasing the network load with routing traffic. Whatever the routing protocol adopted, the best performance is obtained only while small groups of nodes communicate using at most two-hop routes.