汽车工程 ›› 2024, Vol. 46 ›› Issue (6): 965-974.doi: 10.19562/j.chinasae.qcgc.2024.06.003
收稿日期:
2023-12-05
修回日期:
2024-02-25
出版日期:
2024-06-25
发布日期:
2024-06-19
通讯作者:
黄晶
E-mail:huangjing926@hnu.edu.cn
基金资助:
Jing Huang(),Xiangzhen Liu,Xiaoyang Deng,Ran Chen
Received:
2023-12-05
Revised:
2024-02-25
Online:
2024-06-25
Published:
2024-06-19
Contact:
Jing Huang
E-mail:huangjing926@hnu.edu.cn
摘要:
混合交通流下由于驾驶员意图的不确定性行驶轨迹将呈现多模态属性,为了提高安全性并实现个性化驾驶,本文提出一种基于环境车辆多模态轨迹预测的智能车轨迹规划算法。首先,结合图卷积神经网络(GCN)和长短期记忆网络(LSTM)并加入注意力机制建立轨迹预测模型,预测不同行驶意图下的未来轨迹概率分布。然后,针对环境车辆的多意图概率下预测轨迹集合,根据自动驾驶风格偏好,设定一定的概率阈值挑选出确信轨迹,将其投影到规划路径上生成S-T图,并通过动态规划和二次规划进行基于碰撞风险规避的速度规划。最后,基于模型预测控制(MPC)对本文模型在典型换道场景和NGSIM真实道路场景下进行仿真测试并与现有模型进行对比验证。结果表明:本文提出的模型在安全性、舒适性和行车效率等方面均优于对比模型,能够在准确预测环境车辆未来轨迹的前提下实现最优轨迹规划,保证自动驾驶汽车安全、高效的行驶。
黄晶,刘祥臻,邓潇阳,陈然. 基于多模态轨迹预测的智能车轨迹规划研究[J]. 汽车工程, 2024, 46(6): 965-974.
Jing Huang,Xiangzhen Liu,Xiaoyang Deng,Ran Chen. Research on Intelligent Vehicle Trajectory Planning Based on Multimodal Trajectory Prediction[J]. Automotive Engineering, 2024, 46(6): 965-974.
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