Object Matching for Inter-Vehicle Communication Systems—An IMM-Based Track Association Approach With Sequential Multiple Hypothesis Test

Autonomous driving poses unique challenges for vehicle environment perception due to the complex driving environment where the autonomous vehicle interacts with surrounding traffic participants. Due to the limited capability of any sensor perception system, it is highly desirable that an autonomous driving vehicle could use not only information from onboard sensors (say, radar/camera/lidar) but also from remote (network) information via inter-vehicle communication systems. The collaborative information from cooperative/non-cooperative remote vehicles (along with the onboard sensor data) could substantially improve the vehicle decision making process and push autonomous driving to be safer and more reliable. Inter-vehicle communication technologies are at the stage of development for market introduction, after years of research and standardization. In this paper, we setup a dedicated short range communication (DSRC) system to provide a low-latency inter-vehicle wireless communication channel. The task is to build a record linkage between the onboard sensor data and the corresponding DSRC-transmitted remote vehicle information when both sets belong to the same object, for the purpose of enhancing host vehicle environment perceiving capability and reliability. This is a typical data association problem. The challenges mainly lie in the inherent uncertain nature of the observation data and the practical issues that information often suffers from delays and drops. We propose a track-based association approach using an interacting multiple model estimator with a sequential multiple hypothesis test (denoted as IMM-SMHT) as an ubiquitous solution to handle different situations in complicated driving scenarios. To fully exploit the potential of such a system, only position information (from the DSRC channel and onboard radar system) is used for the object matching purpose-we try to use the least amount of information to achieve a high association accuracy; additional information can be used but not currently considered. We aim to provide a real world solution, and therefore, a prototype vehicle system is built with practical consideration on market availability, cost, and sensor limitations. We design meaningful use cases for creating functionality modules from a systematic point of view. The inter-vehicle information fusion system based on the track fusion approach using the IMM-SMHT is tested in real traffic on the U.S. roads and shows promising object matching performance of significant practical feasibility.