考虑预测风险的自动驾驶车辆运动规划方法

doi:10.19562/j.chinasae.qcgc.2023.08.007

Abstract

Abstract:

In this paper， a motion planning method for autonomous vehicles considering prediction risk is proposed， which is based on the model predictive control algorithm while incorporating interactive prediction of the future trajectories of surrounding vehicles and the risks. Firstly， the interaction between the vehicles is modeled as a graph structure， which is then used to construct an interaction-aware motion prediction module. Then multiple prediction models with isomorphic and different parameters are trained and ensemble technology is used to obtain the uncertainty risk of the prediction network for the prediction results. The model predictive control algorithm is then applied to deal with the risk based on the obtained prediction algorithm uncertainty risk. By comprehensively considering the safety constraints， and vehicle physical property constraints in the optimization problem constraints， a motion planning method for autonomous driving considering the risk of prediction uncertainty is designed. Finally， the motion prediction capability of the established prediction model， the effectiveness of motion planning approach based on model predictive control， and the capability of motion planning to deal with prediction risks are verified based on real driving data set and SUMO simulation platform. The simulation results show that the motion planning method proposed in this paper is capable of sensing the uncertain risks posed by the prediction algorithm while acting to mitigate those risks when confronted with scenarios of high risk of prediction， such as the emergency acceleration and deceleration of nearby vehicles， which can increase the safety of road driving.

Key words: motion planning, model predictive control, autonomous driving, graph convolution neural network, prediction risk

Ming Wang,Xiaolin Tang,Kai Yang,Guofa Li,Xiaosong Hu. A Motion Planning Method for Autonomous Vehicles Considering Prediction Risk[J].Automotive Engineering, 2023, 45(8): 1362-1372.

Figures/Tables 21

输入：

M

个GNN预测网络，训练数据集

D = {X, Y}

1：使用任意权重参数 $θ 1$ ， $θ 2$ ，…， $θ M$ 初始化 $M$ 个预测网络 $N N s m$ ；

2： for 对于每一个预测网络 $N N s m$ ：

3：随机打乱数据集 $D$ ；

4：随机更改训练随机数种子；

5： for 训练过程中的某一次迭代 $i t h$ do：

6： $N N s m$ 根据 $D$ 中训练数据前向传播获得损失值；

7：损失值反向传播获得 $N N s m$ 梯度 $L m$ ；

8：根据梯度 $L m$ 更新 $N N s m$ 权重参数 $θ m$ ；

9： end

10：获得训练好的GNN预测网络 $N N s m$ ；

11： end

12：获得M个同构异参的GNN预测网络；

13：最终预测结果： $s t = f ? t = M - 1 ∑ m = 1 M f t θ m$

14：不确定性风险：∑_t $= (M - 1) - 1 ∑ m = 1 M (f t θ m - f ? t i) 2$

输出：某时刻预测结果

s t

及其不确定性风险∑_t

参数	数值
前轴轴距 $l f$	1.81 m
后轴轴距 $l r$	1.33 m
自车包络圆个数 $n c$	3
包络圆半径 $r n c$	1.0 m
最大车速 $v m a x$	33 m/s
最大加速度 $a m a x$	2.8 m/s²
最大减速度 $a m i n$	-8 m/s²
最大转向角 $δ$	$± 0.22 ? π$
道路左、右边界 $d l e f t r o a d / d r i g h t r o a d$	$± 1.875 ?? m$

参数	数值
预测时域 $N P$	3 s
控制时域 $N c$	3 s
控制频率 $p$	10 Hz
轮廓误差权重 $q c$	500
滞后误差权重 $q l$	150
加速度权重 $q a$	6
转向角权重 $q δ$	150
速度权重 $q v$	2.5
松弛变量权重 $q ε$	2 800
参考线投影速度权重 $q ε$	0.01

References 20

1	YANG K， HUANG Y， QIN Y， et al. Potential and challenges to improve vehicle energy efficiency via V2X： literature review［J］. International Journal of Vehicle Performance， 2021， 7（3-4）： 244-265.
2	TANG X， YANG K， WANG H， et al. Driving environment uncertainty-aware motion planning for autonomous vehicles［J］. Chinese Journal of Mechanical Engineering， 2022， 35（1）： 120.
3	KRAJEWSKI R， BOCK J， KLOEKER L， et al. The highd dataset： a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems［C］. 2018 21st International Conference on Intelligent Transportation Systems （ITSC）. IEEE， 2018： 2118-2125.
4	ZHAN W， SUN L， WANG D， et al. Interaction dataset： an international， adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps［J］. arXiv preprint arXiv：， 2019.
5	TANG X， YANG K， WANG H， et al. Prediction-uncertainty-aware decision-making for autonomous vehicles［J］. IEEE Transactions on Intelligent Vehicles， 2022， 7（4）： 849-862.
6	DEO N， TRIVEDI M M. Convolutional social pooling for vehicle trajectory prediction［C］. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops， 2018： 1468-1476.
7	MO X， XING Y， LV C. Graph and recurrent neural network-based vehicle trajectory prediction for highway driving［C］. 2021 IEEE International Intelligent Transportation Systems Conference （ITSC）. IEEE， 2021： 1934-1939.
8	MO X， HUANG Z， XING Y， et al. Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network［J］. IEEE Transactions on Intelligent Transportation Systems， 2022， 23（7）： 9554-9567.
9	杨澜，绳贺，赵祥模，等. 信号交叉口自动驾驶左转车辆类人化全局运动轨迹规划［J］. 中国公路学报， 2022， 35（9）：14.
	YANG L，SHENG H，ZHAO X M，et al. Humanoid motion planning strategy for left-turn maneuvers of automated vehicles at signalized intersection［J］. China Journal of Highway and Transport， 2022， 35（9）：14.
10	BRITO B， AGARWAL A， ALONSO-MORA J. Learning interaction-aware guidance for trajectory optimization in dense traffic scenarios［J］. IEEE Transactions on Intelligent Transportation Systems， 2022， 23（10）： 18808-18821.
11	李文礼，韩迪，任勇鹏，等.基于交互车辆轨迹预测的自动驾驶车辆轨迹规划［J］.计算机应用研究，2023，40（2）：519-525，538.
	LI W L，HAN D，REN Y P， et al. Trajectory planning of autonomous vehicle based on interactive vehicle trajectory prediction［J］. Application Research of Computers， 2023，40（2）：519-525，538.
12	WANG H， LU B， LI J， et al. Risk assessment and mitigation in local path planning for autonomous vehicles with LSTM based predictive model［J］. IEEE Transactions on Automation Science and Engineering， 2021， 19（4）： 2738-2749.
13	LIU K， LI N， TSENG H E， et al. Interaction-aware trajectory prediction and planning for autonomous vehicles in forced merge scenarios［J］. IEEE Transactions on Intelligent Transportation Systems， 2022.
14	VELIČKOVIĆ P， CUCURULL G， CASANOVA A， et al. Graph attention networks［J］. arXiv preprint arXiv：， 2017.
15	LAKSHMINARAYANAN B， PRITZEL A， BLUNDELL C. Simple and scalable predictive uncertainty estimation using deep ensembles［J］. Advances in Neural Information Processing Systems， 2017， 30.
16	KONG J， PFEIFFER M， SCHILDBACH G， et al. Kinematic and dynamic vehicle models for autonomous driving control design［C］. 2015 IEEE Intelligent Vehicles Symposium （IV）. IEEE， 2015： 1094-1099.
17	SCHWARTING W， ALONSO-MORA J， PAULI L， et al. Parallel autonomy in automated vehicles： safe motion generation with minimal intervention［C］. 2017 IEEE International Conference on Robotics and Automation （ICRA）. IEEE， 2017： 1928-1935.
18	BAE S， SAXENA D， NAKHAEI A， et al. Cooperation-aware lane change maneuver in dense traffic based on model predictive control with recurrent neural network［C］. 2020 American Control Conference （ACC）. IEEE， 2020： 1209-1216.
19	ZANELLI A， DOMAHIDI A， JEREZ J， et al. FORCES NLP： an efficient implementation of interior-point methods for multistage nonlinear nonconvex programs［J］. International Journal of Control， 2020， 93（1）： 13-29.
20	SHALEV-SHWARTZ S， SHAMMAH S， SHASHUA A. On a formal model of safe and scalable self-driving cars［J］. arXiv preprint arXiv：， 2017.

[1]	HAO Han, WANG Si-南, LI Xiao, LIU Zong-Wei, ZHAO Fu-Quan. [J]. , 2017, 39(1): 1 -8 .
[2]	XU Cheng-Shan, JIANG Fa-Chao, SONG Sen-Nan, TIAN Guang-Yu. [J]. , 2017, 39(1): 9 -14 .
[3]	JIANG Ting, DAI Bing. [J]. , 2016, 38(12): 1459 -1466 .
[4]	CHEN Wu-Wei, DENG Shu-Chao, HUANG He, XIE You-Hao. [J]. , 2016, 38(12): 1488 -1493 .
[5]	CUI An, LI Bin, WANG Xue-Liang, ZHANG Shi-Zhan. [J]. , 2016, 38(12): 1521 -1525 .
[6]	YUAN Zhi-Qun, GU Zheng-Qi, YANG Ming-Zhi, PENG Qian, LIU Xian-Gui. [J]. , 2017, 39(1): 28 -34 .
[7]	ZUO Wen-Jie, CHEN Ji-Shun, LI Yi-Wen, LIU Nian. [J]. , 2017, 39(2): 145 -149 .
[8]	ZHANG Guan-Jun, ZHAO Xin-Feng, CAO Li-Bo. [J]. , 2017, 39(2): 150 -158 .
[9]	CAO Li-Bo, HU Yuan, YAN Ling-Bo, PENG Yu, SHI Xiang-南. [J]. , 2017, 39(2): 174 -180 .
[10]	ZHANG Yong, GU Zheng-Qi, LIU Shui-Chang, MI Cheng-Ji. [J]. , 2017, 39(3): 275 -280 .

集成数量M	ADE@3 s/m	FDE@3 s/m
1（基础模型）	0.278	0.713
2	0.277	0.710
4	0.264	0.694
6	0.263	0.692

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A Motion Planning Method for Autonomous Vehicles Considering Prediction Risk

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