基于概率轨迹预测分布的不确定性感知变道运动规划算法

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Open Journal of Vehicular Technology Pub Date : 2024-07-16 DOI:10.1109/OJVT.2024.3428645
Zhiqiang Zhang;Lei Zhang;Mingqiang Wang;Cong Wang;Zhenpo Wang
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引用次数: 0

摘要

全面准确地了解交互式交通环境有助于自动驾驶车辆进行合理的运动规划。考虑到周围车辆驾驶行为预测的不确定性,本文提出了一种主机车辆整体风险评估方法,以实现高效的运动规划。本文构建了一个社会时序卷积长短期记忆网络,以捕捉主机和周围车辆之间的交互特征,并预测轨迹预测不确定性在预测范围内的统计分布。然后开发了一种基于二维高斯分布的动态风险评估和软更新方法,通过构建基于周围车辆预测轨迹多模态分布的占用图,在空间和时间上量化驾驶风险。通过最小化替代驾驶行为的多目标函数,确定主机车辆的最优运动。从 NGSIM 数据集中提取的典型驾驶场景验证了所提方案的有效性。结果表明,所提出的方法可以全面评估潜在风险,并在最大限度降低驾驶风险的同时有效实现运动规划。
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An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution
Comprehensive and accurate understanding of the interactive traffic environment facilitates reasonable motion planning for automated vehicles. This paper presents an overall risk assessment method for the host vehicle to achieve efficient motion planning considering uncertainties of the predicted driving behaviors of surrounding vehicles. A Social Temporal Convolutional Long Short-Term Memory network is constructed to capture the interactive characteristics among the host and surrounding vehicles and to predict the statistical distribution of the trajectory prediction uncertainty in the prediction horizon. Then a two-dimensional Gaussian distribution-based dynamic risk assessment with a soft update method is developed to spatially and temporally quantify the driving risk by constructing the occupancy map based on the multi-modal distribution of the predicted trajectories for the surrounding vehicles. The optimal motion of the host vehicle is determined by minimizing a multi-objective function of the alternative driving behaviors. The effectiveness of the proposed scheme is verified under typical driving scenarios extracted from the NGSIM dataset. The results show that the proposed method can comprehensively evaluate the potential risk and efficiently achieve motion planning while minimizing the driving risk.
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来源期刊
CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
期刊最新文献
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