A Spatial-State-Based Omni-Directional Collision Warning System for Intelligent Vehicles

Wenjing Zhao, Siyuan Gong, Dezong Zhao, Fenglin Liu, N. N. Sze, Mohammed Quddus, Helai Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Collision warning systems (CWSs) have been recognized as effective tools in preventing vehicle collisions. Existing systems mainly provide safety warnings based on single-directional approaches, such as rear-end, lateral, and forward collision warnings. Such systems cannot provide omni-directorial enhancements on driver’s perception. Meanwhile, due to the unclear and overlapped activation areas of above single-directional CWSs, multiple kinds of warnings may be triggered mistakenly for a collision. The multi-triggering may confuse drivers about the position of dangerous targets. To this end, this paper develops a spatial-state-based omni-directional collision warning system (S-OCWS), aiming to help drivers identify the specific danger by providing the unique warning. First, the operational domains of rear-end, lateral, and forward collisions are theoretically distinguished. This distinction is attained by a geometric approach with a rigorous mathematical derivation, based on the spatial states and the relative motion states of itself and the target vehicle in real time. Then, a theoretical omni-directional collision warning model is established using time-to-collision (TTC) to clarify activation conditions for different collision warnings. Finally, the effectiveness of the S-OCWS is validated in field tests. Results indicate that the S-OCWS can help drivers quickly and properly respond to the warnings without compromising their control over lateral offsets. In particular, the probability of drivers giving proper responses to FCW doubles when the S-OCWS is on, compared to when the system is off. In addition, the S-OCWS shortens the responses time of nonprofessional drivers, and therefore enhances their safety in driving.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Intelligent Transportation Systems
DOIs
Publication statusAccepted/In press - 2024

Keywords

  • Alarm systems
  • Connected vehicles
  • driving performance
  • field tests
  • Intelligent vehicles
  • Mathematical models
  • omni-directional collision warning system
  • Real-time systems
  • Traffic control
  • Vectors
  • Vehicles

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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