Data augmentation of automotive lidar point clouds under adverse weather situations

Jose Roberto Vargas Rivero, Thiemo Gerbich, Boris Buschardt, Jia Chen

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

In contrast to previous works on data augmentation using LIDAR (Light Detection and Ranging), which mostly consider point clouds under good weather conditions, this paper uses point clouds which are affected by spray. Spray water can be a cause of phantom braking and understanding how to handle the extra detections caused by it is an important step in the development of ADAS (Advanced Driver Assistance Systems)/AV (Autonomous Vehicles) functions. The extra detections caused by spray cannot be safely removed without considering cases in which real solid objects may be present in the same region in which the detections caused by spray take place. As collecting real examples would be extremely difficult, the use of synthetic data is proposed. Real scenes are reconstructed virtually with an added extra object in the spray region, in a way that the detections caused by this obstacle match the characteristics a real object in the same position would have regarding intensity, echo number and occlusion. The detections generated by the obstacle are then used to augment the real data, obtaining, after occlusion effects are added, a good approximation of the desired training data. This data is used to train a classifier achieving an average F-Score of 92. The performance of the classifier is analyzed in detail based on the characteristics of the synthetic object: size, position, reflection, duration. The proposed method can be easily expanded to different kinds of obstacles and classifier types.

Original languageEnglish
Article number4503
JournalSensors (Switzerland)
Volume21
Issue number13
DOIs
StatePublished - 1 Jul 2021

Keywords

  • ADAS
  • AV
  • Classification
  • Data augmentation
  • LIDAR
  • Point cloud
  • Sensor model
  • Spray water
  • Synthetic data

Fingerprint

Dive into the research topics of 'Data augmentation of automotive lidar point clouds under adverse weather situations'. Together they form a unique fingerprint.

Cite this