Zhang Bihui, Li Zhuqi, Li Bingjie, Zhan Jingbo, Deng Songtao, Fang Yi
School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
School of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Biomimetics (Basel). 2024 Nov 19;9(11):711. doi: 10.3390/biomimetics9110711.
Despite the implementation of numerous interventions to enhance urban traffic safety, the estimation of the risk of traffic crashes resulting in life-threatening and economic costs remains a significant challenge. In light of the above, an online inference method for traffic crash risk based on the self-developed TAR-DETR and WOA-SA-SVM methods is proposed. The method's robust data inference capabilities can be applied to autonomous mobile robots and vehicle systems, enabling real-time road condition prediction, continuous risk monitoring, and timely roadside assistance. First, a self-developed dataset for urban traffic object detection, named TAR-1, is created by extracting traffic information from major roads around Hainan University in China and incorporating Russian car crash news. Secondly, we develop an innovative Context-Guided Reconstruction Feature Network-based Urban Traffic Objects Detection Model (TAR-DETR). The model demonstrates a detection accuracy of 76.8% for urban traffic objects, which exceeds the performance of other state-of-the-art object detection models. The TAR-DETR model is employed in TAR-1 to extract urban traffic risk features, and the resulting feature dataset was designated as TAR-2. TAR-2 comprises six risk features and three categories. A new inference algorithm based on WOA-SA-SVM is proposed to optimize the parameters (C, g) of the SVM, thereby enhancing the accuracy and robustness of urban traffic crash risk inference. The algorithm is developed by combining the Whale Optimization Algorithm (WOA) and Simulated Annealing (SA), resulting in a Hybrid Bionic Intelligent Optimization Algorithm. The TAR-2 dataset is inputted into a Support Vector Machine (SVM) optimized using a hybrid algorithm and used to infer the risk of urban traffic crashes. The proposed WOA-SA-SVM method achieves an average accuracy of 80% in urban traffic crash risk inference.
尽管实施了众多干预措施以提高城市交通安全，但对导致生命威胁和经济成本的交通事故风险进行评估仍然是一项重大挑战。鉴于此，提出了一种基于自主开发的TAR-DETR和WOA-SA-SVM方法的交通事故风险在线推理方法。该方法强大的数据推理能力可应用于自主移动机器人和车辆系统，实现实时路况预测、持续风险监测和及时的路边援助。首先，通过提取中国海南大学周边主要道路的交通信息并纳入俄罗斯车祸新闻，创建了一个自主开发的城市交通目标检测数据集，名为TAR-1。其次，我们开发了一种基于上下文引导重建特征网络的创新型城市交通目标检测模型（TAR-DETR）。该模型对城市交通目标的检测准确率达到76.8%，超过了其他先进目标检测模型的性能。将TAR-DETR模型应用于TAR-1中以提取城市交通风险特征，所得特征数据集被指定为TAR-2。TAR-2包含六个风险特征和三个类别。提出了一种基于WOA-SA-SVM的新推理算法来优化支持向量机的参数（C，g），从而提高城市交通事故风险推理的准确性和鲁棒性。该算法通过结合鲸鱼优化算法（WOA）和模拟退火算法（SA）开发而成，形成了一种混合仿生智能优化算法。将TAR-2数据集输入到使用混合算法优化的支持向量机中，用于推断城市交通事故风险。所提出的WOA-SA-SVM方法在城市交通事故风险推理中实现了80%的平均准确率。
