The Impact of Pose Alignment Errors on a Classical Late Infrastructure-Vehicle Collaboration Framework using Experimental Data

Accurate perception and localization of dynamic objects is crucial for autonomous systems to navigate complex environments and avoid collisions. However, onboard sensing struggles with complex environments due to the challenges associated with occlusion, hindering progress in achieving advanced levels of vehicle autonomy in these environments. Cooperative driving automation has emerged as an enabling technology for the development of safer vehicles by using shared perception information, thus enhancing the situational awareness of the ego-vehicle. Alignment of shared perceptual information is a crucial task within this cooperative framework, and any pose errors can compromise the safety of the system. This paper investigates the real-world impact of pose alignment errors on vehicle-infrastructure collaborative driving by identifying, isolating, and analyzing individual error sources within a late collaboration framework. Our proposed method creates a shared global V2X environmental model by fusing LiDAR object representations from individual agents. We examine the sensitivity of this global map to common real-world error sources, including V2X communication delays, GPS positioning uncertainty, and sensor calibration errors. In real-world scenarios, these map alignment errors are non-zero, and their magnitudes depend on the specific use case. By isolating and studying each error type individually, we gain insight into their relative impacts and how they accumulate in real-world collaborative driving systems. We demonstrate the benefits of V2X collaboration and determine the most impactful errors on map misalignment through quantitative analysis, thus allowing the reader to conceptualize the implications and challenges of pose alignment errors in collaborative driving.