Multiagent Information Fusion for Connected Driving: A Review

Abstract

This paper reviews the state-of-the-art multi-sensor fusion approaches applicable in the next-generation intelligent transportation systems where connected vehicles are cooperatively driven for maximum safety and efficiency. The review finds out that complementary sensor fusion in a time-varying distributed network is required, and for such applications, the state-of-the-art is sensor fusion in the random finite set filtering framework. The fundamental bases of random finite set filters are reviewed with more elaboration on a particular filter called the Labeled Multi-Bernoulli filter. An information-theoretic approach for data fusion based on minimizing information divergence between statistical densities is presented, along with how different divergence functions can be used for sensor fusion. Different approaches are evaluated for their tracking performance and computational cost in a realistic simulation scenario. Their advantages, and disadvantages in the context of real-time implementation in a connected driving scenario are discussed.

Keywords

• Random finite sets
• intelligent transport systems
• multi-object tracking
• information fusion