Enhancing Lidar-Based Object Detection in Adverse Weather Using Offset Sequences in Time

International Conference on Electrical, Computer and Energy Technologies (ICECET), pp. 1–6

Abstract

Automated vehicles require an accurate perception of their surroundings for safe and efficient driving. Lidar-based object detection is a widely used method for environment perception, but its performance is significantly affected by adverse weather conditions such as rain and fog. In this work, we investi-gate various strategies for enhancing the robustness of lidar-based object detection by processing sequential data samples generated by lidar sensors. Our approaches leverage temporal information to improve a lidar object detection model, without the need for additional filtering or preprocessing steps. We compare 10 different neural network architectures that process point cloud sequences including a novel augmentation strategy introducing a temporal offset between frames of a sequence during training and evaluate the effectiveness of all strategies on lidar point clouds under adverse weather conditions through experiments. Our research provides a comprehensive study of effective methods for mitigating the effects of adverse weather on the reliability of lidar-based object detection using sequential data that are evaluated using public datasets such as nuScenes, Dense, and the Canadian Adverse Driving Conditions Dataset. Our findings demonstrate that our novel method, involving temporal offset augmentation through randomized frame skipping in sequences, enhances object detection accuracy compared to both the baseline model (Pillar-based Object Detection) and no augmentation.

Authors 7

1. Raphael van Kempen Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering, RWTH Aachen University,Aachen,Germany

   Institute for Automotive Engineering, RWTH Aachen University, Aachen, Germany

2. Tim Rehbronn Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering, RWTH Aachen University,Aachen,Germany

   Institute for Automotive Engineering, RWTH Aachen University, Aachen, Germany

3. Abin Jose Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University,Aachen,Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

4. Johannes Stegmaier Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision, RWTH Aachen University,Aachen,Germany

   Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

5. Bastian Lampe Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering, RWTH Aachen University,Aachen,Germany

   Institute for Automotive Engineering, RWTH Aachen University, Aachen, Germany

6. Timo Woopen Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering, RWTH Aachen University,Aachen,Germany

   Institute for Automotive Engineering, RWTH Aachen University, Aachen, Germany

7. Lutz Eckstein Aachen Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Automotive Engineering, RWTH Aachen University,Aachen,Germany

   Institute for Automotive Engineering, RWTH Aachen University, Aachen, Germany