摘要
场景复杂度的评估对于提升自动驾驶车辆应对多变环境的能力以及增强算法的适用性至关重要。本文中设计了基于图模型的复杂度评估算法,充分考虑场景中的交互拓扑,将场景划分为3类不同复杂度。在匝道汇流场景下,验证了该算法的合理性与有效性。为说明复杂度评估算法的拓展性,将其应用于自动驾驶的轨迹预测与决策算法开发中。通过结合自然驾驶数据集和实车在环试验,对提出算法进行测试,结果表明:场景复杂度评估可预先估计预测的不确定性,显著提升自动驾驶决策算法的实时性与最优性。在数据回放测试中,复杂度评估模块可帮助降低并道失败率、并道剐蹭率分别为38%、92%,具有潜在的应用前景。
The evaluation of scenario complexity is crucial for improving adaptability and flexibility of autonomous vehicles in coping with complex environments and enhancing the applicability of the algorithms.A graphbased algorithm for evaluating scenario complexity is developed in this paper,which fully considers interactive topology and categorizes traffic scenarios into three complexity levels.The reasonability and effectiveness are validated in ramp merging scenarios.To demonstrate its scalability,the evaluation algorithm is applied in the development of the trajectory prediction and decision-making algorithms of automated driving.The proposed algorithms are then tested using natural driving datasets and vehicle-in-the-loop experiments.The results indicate that scenario complexity evaluation enables early estimation of prediction uncertainty,enhances the real-time and optimality of decisionmaking algorithms.In data replay tests,the complexity assessment module can reduce the failure rate and collision rate during lane merging by approximately 38%and 92%,respectively,indicating promising application prospects.
作者
李道飞
潘豪
Li Daofei;Pan Hao(Institute of Power Machinery and Vehicular Engineering,Zhejiang University,Hangzhou 310027)
出处
《汽车工程》
EI
CSCD
北大核心
2024年第9期1556-1563,共8页
Automotive Engineering
基金
国家自然科学基金面上项目(52372421)
浙江省科技厅领雁研发攻关计划项目(2023C01238,2022C01241)资助。
关键词
场景复杂度
自动驾驶决策
轨迹预测
图模型
实车在环试验
scenario complexity
autonomous driving decision making
trajectory prediction
graph model
vehicle-in-the-loop experiment
