Addressing Data Annotation Challenges in Multiple Sensors: A Solution for Scania Collected Datasets

Data annotation in autonomous vehicles is a critical step in the development of Deep Neural Network (DNN) based models or the performance evaluation of the perception system. This often takes the form of adding 3D bounding boxes on time-sequential and registered series of point-sets captured from active sensors like Light Detection and Ranging (LiDAR) and Radio Detection and Ranging (RADAR). When annotating multiple active sensors, there is a need to motion compensate and translate the points to a consistent coordinate frame and timestamp respectively. However, highly dynamic objects pose a unique challenge, as they can appear at different timestamps in each sensor's data. Without knowing the speed of the objects, their position appears to be different in different sensor outputs. Thus, even after motion compensation, highly dynamic objects are not matched from multiple sensors in the same frame, and human annotators struggle to add unique bounding boxes that capture all objects. This article focuses on addressing this challenge, primarily within the context of Scania-collected datasets. The proposed solution takes a track of an annotated object as input and uses the Moving Horizon Estimation (MHE) to robustly estimate its speed. The estimated speed profile is utilized to correct the position of the annotated box and add boxes to object clusters missed by the original annotation.

I. Introduction

The pursuit of autonomous vehicles, particularly in heavy vehicle manufacturing, has gained momentum across var-ious industries. At its core, the essential element driving the progress is ground truth data, essential for evaluating the Autonomous Vehicles (AV) software stack. To obtain this invaluable data, several approaches have emerged. One method involves equipping the ego and multiple non-ego vehicles with Global Positioning System (GPS) sensors and orchestrating staged scenarios, for instance, overtaking, U-turns, roundabouts etc. While the GPS enables compre-hensive state-awareness of the environment and provides valuable insights, this approach can't be extended to real-world driving. As such, highly controlled scenarios also fall short in emulating the complexities of real-world driving, leaving gaps in the generalization ability of the system.