Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion

Peide Cai, Sukai Wang, Yuxiang Sun, Ming Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

All-day and all-weather navigation is a critical capability for autonomous driving, which requires proper reaction to varied environmental conditions and complex agent behaviors. Recently, with the rise of deep learning, end-to-end control for autonomous vehicles has been well studied. However, most works are solely based on visual information, which can be degraded by challenging illumination conditions such as dim light or total darkness. In addition, they usually generate and apply deterministic control commands without considering the uncertainties in the future. In this letter, based on imitation learning, we propose a probabilistic driving model with multi-perception capability utilizing the information from the camera, lidar and radar. We further evaluate its driving performance online on our new driving benchmark, which includes various environmental conditions (e.g., urban and rural areas, traffic densities, weather and times of the day) and dynamic obstacles (e.g., vehicles, pedestrians, motorcyclists and bicyclists). The results suggest that our proposed model outperforms baselines and achieves excellent generalization performance in unseen environments with heavy traffic and extreme weather.

Original languageEnglish
Article number9091334
Pages (from-to)4218-4224
Number of pages7
JournalIEEE Robotics and Automation Letters
Volume5
Issue number3
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes

Keywords

  • Automation technologies for smart cities
  • autonomous vehicle navigation
  • motion planning and control
  • multi-modal perception
  • sensorimotor learning

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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