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<p>Adverse weather conditions, such as fog and other low-visibility scenarios, increase driver reaction stress and pose significant threats to driving safety. However, limited research has been conducted on reducing the risk of collisions under these conditions. To address this challenge, this study introduces a novel low-visibility forwards collision warning (LV-FCW) algorithm specifically designed for low-visibility situations. The proposed algorithm considers the impact of visibility on driving safety and adjusts the warning distance in real time on the basis of visibility conditions, allowing for earlier warning messages to the driver. Additionally, the algorithm is compared with various typical algorithms through numerical simulations, and its warning performance under different visibility levels and front vehicle behaviours is analysed. To further demonstrate its effectiveness, low-visibility driving simulation experiments are conducted to compare the LV-FCW algorithm with typical and no-warning algorithms. The results indicate that the LV-FCW algorithm significantly reduces the risk of collision, with the no-warning and typical algorithms presenting collision risks that are 10.88 and 3.4 times higher, respectively. These findings highlight the superior performance of the LV-FCW algorithm under foggy and other low-visibility conditions, significantly mitigating collision risk and providing insights for optimizing FCW algorithms in such environments. Furthermore, integrating this algorithm with vehicle-to-vehicle communication technology can further increase driving safety under adverse conditions.</p>

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Optimization of forward collision warning algorithm and driver-in-the-loop validation for intelligent vehicles under low-visibility conditions

  • 期刊论文

  • 发表日期：

    2025年2月13日

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  • Zhiqun Yuan,
  • Yuxuan Chang,
  • Yong Han,
  • Shuiwen Shen

摘要

Adverse weather conditions, such as fog and other low-visibility scenarios, increase driver reaction stress and pose significant threats to driving safety. However, limited research has been conducted on reducing the risk of collisions under these conditions. To address this challenge, this study introduces a novel low-visibility forwards collision warning (LV-FCW) algorithm specifically designed for low-visibility situations. The proposed algorithm considers the impact of visibility on driving safety and adjusts the warning distance in real time on the basis of visibility conditions, allowing for earlier warning messages to the driver. Additionally, the algorithm is compared with various typical algorithms through numerical simulations, and its warning performance under different visibility levels and front vehicle behaviours is analysed. To further demonstrate its effectiveness, low-visibility driving simulation experiments are conducted to compare the LV-FCW algorithm with typical and no-warning algorithms. The results indicate that the LV-FCW algorithm significantly reduces the risk of collision, with the no-warning and typical algorithms presenting collision risks that are 10.88 and 3.4 times higher, respectively. These findings highlight the superior performance of the LV-FCW algorithm under foggy and other low-visibility conditions, significantly mitigating collision risk and providing insights for optimizing FCW algorithms in such environments. Furthermore, integrating this algorithm with vehicle-to-vehicle communication technology can further increase driving safety under adverse conditions.