场景复杂度评估在轨迹预测和驾驶决策中的应用

doi:10.19562/j.chinasae.qcgc.2024.09.003

Abstract

Abstract:

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 graph-based 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 decision-making 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.

Key words: scenario complexity, autonomous driving decision making, trajectory prediction, graph model, vehicle-in-the-loop experiment

Daofei Li,Hao Pan. Application of Scenario Complexity Evaluation in Trajectory Prediction and Automated Driving Decision-Making[J].Automotive Engineering, 2024, 46(9): 1556-1563.

Figures/Tables 12

簇数 $k$	2	3	4	5
$C H$	2 670.4	2 851.5	2 709.5	2 767.5

纵向预测误差	类别0-类别1	类别0-类别2	类别1-类别2
Z值	-12.907	-2.693	-11.569
P值	$< 0.001$	0.007	$< 0.001$
横向预测误差	类别0-类别1	类别0-类别2	类别1-类别2
Z值	-9.313	-7.949	-0.781
P值	$< 0.001$	$< 0.001$	0.106

算法	求解耗时/ $m s$
算法	$P 1$	$P 10$	$P 50$	$P 90$	$P 99$
PAs	11.66	14.14	32.87	165.22	209.29
CAs	12.60	14.56	56.56	218.64	1055.5

References 18

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算法	并道成功率	并道失败率	并道剐蹭率
PAs	98.37	1.45	0.18
CAs	95.18	2.36	2.45

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Application of Scenario Complexity Evaluation in Trajectory Prediction and Automated Driving Decision-Making

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