Cai Xiangpeng, Lv Bowen, Yao Hanchen, Yang Ting, Dai Houde
College of Transportation and Navigation, Quanzhou Normal University, Donghai Street 398, Quanzhou 362046, Fujian, China.
College of Engineering and Design, Hunan Normal University, Lushan Road 36, Changsha 410081, Hunan, China.
Accid Anal Prev. 2025 Feb;210:107836. doi: 10.1016/j.aap.2024.107836. Epub 2024 Nov 20.
The implementation of advanced driver assistance systems (ADAS) has significantly impacted the prevention of traffic accidents, particularly through the forward collision warning (FCW) algorithm. Nevertheless, traffic conflicts on traffic routes remain a significant issue, since most FCW algorithms cannot accurately determine the distance between the host vehicle (HV) and remote vehicle (RV) on curved roads. Hence, this study proposes a vector-based FCW (V-FCW) algorithm to address the issue of false warnings on unconventional road sections. The V-FCW algorithm employs vector relationships to estimate the poses of HV and RV at the current and next moments, thereby effectively calculating the relative angles. Firstly, the HV and RV transmit their position vector, velocity vector, and heading angle in real time via the vehicle-to-vehicle (V2V) communication technique. Subsequently, the localization of lanes is conducted through the vehicle-to-infrastructure (V2I) communication technique, with the assistance of roadside unit (RSU)-based local maps. Finally, a V-FCW algorithm was implemented on the Simcenter Prescan simulation platform and a cellular vehicle-to-everything (C-V2X, i.e., the combination of V2V and V2I) communication platform. The simulation results demonstrate that the proposed V-FCW algorithm can accurately identify and warn dangerous vehicles on both straight and curved roads. Moreover, the experimental results obtained from the hardware-in-the-loop approach illustrate the efficacy of the proposed V-FCW algorithm in accurately forecasting four warning levels on both straight and curved roads. Consequently, this study yields a significant contribution to the field of vehicle-road cooperation in C-V2X-enable intelligent driving.
先进驾驶辅助系统（ADAS）的实施对交通事故预防产生了重大影响，特别是通过前碰撞预警（FCW）算法。然而，交通路线上的交通冲突仍然是一个重大问题，因为大多数FCW算法无法准确确定在弯曲道路上主车（HV）与远程车辆（RV）之间的距离。因此，本研究提出一种基于矢量的FCW（V-FCW）算法，以解决非常规路段的误报问题。V-FCW算法利用矢量关系来估计当前和下一时刻HV和RV的位姿，从而有效计算相对角度。首先，HV和RV通过车对车（V2V）通信技术实时传输其位置矢量、速度矢量和航向角。随后，借助基于路边单元（RSU）的本地地图，通过车对基础设施（V2I）通信技术进行车道定位。最后，在Simcenter Prescan仿真平台和蜂窝车联网（C-V2X，即V2V和V2I的组合）通信平台上实现了V-FCW算法。仿真结果表明，所提出的V-FCW算法能够在直道和弯道上准确识别并警告危险车辆。此外，从硬件在环方法获得的实验结果说明了所提出的V-FCW算法在准确预测直道和弯道上的四个警告级别方面的有效性。因此，本研究对C-V2X支持的智能驾驶中的车路协同领域做出了重大贡献。
