Collaborative positioning and embedded multi-sensors fusion cooperation in advanced driver assistance system

In this paper, we propose two different applications in the area of V2V communications. First, we present a method for better car tracking using GPS information shared through the V2V communication and a vision system in order to support accurate positioning. To accomplish this, we propose to use particle filtering techniques, and when GPS data is unavailable, or of poor quality, we couple GPS data with vision data collected from the vehicles. Second, we present a new simulated framework for prototyping the whole process by combining embedded data, vision data and V2V simulations to progress toward an anti-collision application. This framework could provide a better understanding of road security by studying the impact of V2V communications, thereby improving the quality of perception systems and adding new features for “ADAS”. Our experimentations have been conducted in different scenarios on a fleet of vehicles moving and communicating in real-time conditions. The obtained results demonstrate the consistency of our method whenever the GPS is unavailable. Moreover, they prove the feasibility and the performance efficiency of such real-time multisensory fusion to provide an integrated framework for collision avoidance.

• A novel method using adapted particle filtering for better vehicle tracking utilizing GPS information and a vision system shared through V2V communication. • An integrated cooperative framework for prototyping different systems for collision avoidance. • Supports better information quality towards safer design of ADAS systems.