See the Future: A Semantic Segmentation Network Predicting Ego-Vehicle Trajectory with a Single Monocular Camera

Yuxiang Sun, Weixun Zuo, Ming Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Ego-vehicle trajectory prediction is important for autonomous vehicles to detect collisions and accordingly avoid accidents. Recent approaches employ prior-known or on-line acquired road topology or geometries as motion constraints for their predictive models. However, the prior-known information (e.g., pre-built maps) might become unreliable due to, for example, temporal changes caused by road constructions. Whereas on-line perception may require high-cost sensors, such as large filed-of-view laser scanners, to get an overview structure of the local environment, making the prediction difficult to afford, especially for driving assistance systems. So in this letter, we provide a solution without using road topology or geometries for ego-vehicle trajectory prediction. We formulate this problem as a two-class semantic segmentation problem and develop a novel sequence-based deep neural network to predict the trajectory. The only sensor we need during runtime is a single front-view monocular camera. The inputs to our network are several consecutive images, and the output is the predicted trajectory mask that can be directly overlaid on the current front-view image. We create our datasets with different prediction horizons from KITTI. The experimental results confirm the effectiveness of our approach and the superiority over the baselines.

Original languageEnglish
Article number9004469
Pages (from-to)3066-3073
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume5
Issue number2
DOIs
Publication statusPublished - Apr 2020

Keywords

  • ADAS
  • autonomous vehicles
  • ego-vehicle
  • semantic segmentation
  • Trajectory prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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