Off-road Detection Using Vision Techniques for Hazardous Material Transportation Vehicles

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@article{1703631,
      author = {Shuashua Mao, Lifeng Zhao; Changqing Tian; Xiang Liu, Long Lin; Helix Xie; Ning Zhou, Yiping Zeng; Guangpu Zhang; Chenhui Pan, Kang Yao; Xiaoxin Pi; T. R. Chen},
      title = {Off-road Detection Using Vision Techniques for Hazardous Material Transportation Vehicles},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {1},
      pages = {243-246},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703631.pdf},
      abstract = {Ensuring the safety of hazardous material (Hazmat) transportation is critical. One of the most widely occurred traffic accidents is the run-off-road (ROR) incident. It is needed for exploring effective means of avoiding or even warning of ROR accidents for hazmat transportation. In particular, it is important to note that in many cases, vehicles are not driven on regular roads, but in "off-road" situations such as mountainous areas, where off-road detection is a hazard detection for abnormal driving. In this paper, we point out that it is a viable path for monitoring and detecting ROR incidents using an onboard camera. We reviewed existing literature in terms of the algorithm and the data that can be used. We also illustrate the shortcomings and provide insights into the future development of algorithms for detecting ROR incidents. Our research tailored a path to an effective ROR monitoring and detecting scheme that enhances the safety of hazardous material transportation.},
      keywords = {Algorithm design, Hazardous material transportation, Safety sciences.},
      month = {July},
  }