TY -的A2 Baskan Ozgur盟——Hamedi Hamidreza AU -鲱鱼,Rouzbeh PY - 2022 DA - 2022/02/17 TI -换道轨迹预测建模使用神经网络SP - 9704632六世- 2022 AB -关于自主驾驶,换道(LC)是至关重要的,尤其是在复杂的动态环境。这是一个具有挑战性的任务模型LC由于驾驶行为是复杂和不确定。本研究采用双层前馈反向传播神经网络,包括乙状结肠隐藏神经元和线性输出神经元评估内在LC的复杂性。此外,模型的评估和验证是由大规模的轨迹数据。从下一代获得实证LC数据模拟(NGSIM)项目培训和测试神经网络LC模型。调查结果显示,引入模型可以准确预测LC下车辆的轨迹误差小,令人满意的精度。目前的工作评估LC /端点开始通过分析周围的车辆和速度估计。这是观察到神经网络模型取得了几乎相同的预测观测LC轨迹以及按原始和目标车道上的车辆轨迹。此外,信用证的行为特征验证、神经network-produced LC差距分布进行了比较真实的数据,证明LC差距分布的特点不明显不同于现实生活中的LC的行为。最后,介绍了神经网络的LC模型相比,支持向量回归LC模型。 It was found that the trajectory predictions of both models were adequately consistent with the observational data and could capture both lateral and longitudinal vehicle movements. In turn, this demonstrates that the neural network and support vector regression models had satisfactory performance. Also, the proposed models were evaluated using new inputs such as speed, gap, and position of the subject vehicle. The analysis findings indicated that the performance of the proposed NN and SVR models was higher than the model with new inputs. SN - 1687-8086 UR - https://doi.org/10.1155/2022/9704632 DO - 10.1155/2022/9704632 JF - Advances in Civil Engineering PB - Hindawi KW - ER -