An Extension Proposal for the Collective Perception Service to Avoid Transformation Errors and Include Object Predictions

The collective perception service, which is in progress of standardization by the European Telecommunication Standards Institute, allows to share perception information among connected vehicles and road side units and thus can increase both safety and traffic efficiency. However, based on our practical experience from our research on infrastructure support of automated vehicles on a pilot installation in real traffic, in this work, we outline some drawbacks of the existing draft when applied to real-world environments. We observe that the strict cartesian representation does not fit well with typical models used to predict the motion of vehicles in automated driving applications. In theses cases, transformations and approximations are required, which increases the uncertainty about the perceived objects. In this work, we demonstrate the effect of such transformation errors using examples and propose an extension of the standard to prevent unnecessary transformations and approximations. Additionally, we show that the collective perception service can further be enhanced by allowing the optional transmission of motion predictions of perceived objects. That is, receivers benefit from saving computation time for object predictions and from the reception of high-quality motion predictions from road side units that are more accurate due to their knowledge of local peculiarities.