基于多目标优化的智能车辆换道轨迹规划

赵树恩; 王金祥; 李玉玲

doi:10.19818/j.cnki.1671-1637.2021.02.020

基于多目标优化的智能车辆换道轨迹规划

doi: 10.19818/j.cnki.1671-1637.2021.02.020

重庆交通大学机电与车辆工程学院，重庆 400074

基金项目:

国家自然科学基金项目 52072054

重庆市自然科学基金项目 cstc2018jcyjAX0422

详细信息

作者简介:
赵树恩(1972-)，男，陕西洋县人，重庆交通大学教授，工学博士，从事智能汽车与自动驾驶技术研究

通讯作者:
王金祥(1993-)，男，山东临沂人，重庆交通大学工学硕士研究生

中图分类号: U491.2
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出版历程
- 收稿日期: 2020-10-22
- 刊出日期: 2021-04-01

Lane changing trajectory planning of intelligent vehicle based on multiple objective optimization

School of Mechanotronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Funds:

National Natural Science Foundation of China 52072054

Natural Science Foundation of Chongqing cstc2018jcyjAX0422

More Information

Author Bio:
ZHAO Shu-en(1972-), male, professor, PhD, zse0916@163.com

Corresponding author: WANG Jin-xiang(1993-), male, graduate student, 383148322@qq.com

摘要

摘要: 为提高智能车辆换道轨迹规划的拟人性和实时性，提出了安全、舒适、节能等多目标协同优化的换道轨迹规划算法，该轨迹规划方法的适应性取决于车辆换道时间、纵横向速度及加速度等关键变量的约束条件；基于车辆运动学和动力学理论，分析了动态未知环境下车辆换道安全区域，建立了六次多项式车辆理想换道轨迹模型，并运用遗传算法-BP神经网络理论对换道终止时刻及目标位置进行预测，得到了复杂场景下车辆换道轨迹簇；分析了基于可行解空间的车辆换道安全性、舒适性、经济性等性能评价函数，构建了多性能目标协同优化目标函数和约束条件，运用鲸鱼优化算法对换道轨迹簇进行优化，实现多性能目标协同的智能车辆换道轨迹最优规划；为进一步验证多目标优化轨迹规划算法的准确性，运用L3级智能车辆测试平台对结构化道路场景下多目标优化换道轨迹规划算法进行了试验验证。仿真和试验结果表明：提出的轨迹规划算法在满足各项约束的情况下可成功实现平稳、安全换道，并且与传统驾驶人换道相比，换道过程的安全性、舒适性及多目标综合性能分别提升了5.1%、3.3%和1.7%，有效提升了动态环境下智能车辆换道轨迹规划的拟人性。
- 智能车辆 /
- 车道变换 /
- 轨迹规划 /
- 多目标优化 /
- 鲸鱼优化算法
Abstract: To improve the anthropomorphism and real-time performance of lane changing trajectory planning for intelligent vehicles, a lane changing trajectory planning algorithm based on the multi-objective collaborative optimization of safety, comfort, and energy saving was proposed. The adaptation of proposed trajectory planning method depended on the constraints of key variables such as lane changing time, longitudinal and lateral velocities, and accelerations. Based on the theory of vehicle kinematics and dynamics, the safe area of vehicle lane changing in dynamic unknown environments was analyzed, and the ideal lane-changing trajectory model of a sixth-degree polynomial was established. A genetic algorithm-back propagation neural network was used to predict the end time and target position of lane changing, and lane changing trajectory clusters in complex scenes were obtained. The performance evaluation functions of safety, comfort, and economy of vehicle lane changing based on feasible solution space were analyzed, and the objective function and constraint conditions of multi-objective collaborative optimization were constructed. The whale optimization algorithm was used to optimize the lane changing trajectory clusters to achieve an optimal lane changing trajectory planning of intelligent vehicles with multi-performance objectives. To further verify the accuracy of the multi-objective optimization trajectory planning algorithm, an L3-level intelligent vehicle test platform was used to test the algorithm for intelligent vehicles in structured road scenes. Simulation and experimental results show that the proposed algorithm can successfully achieve smooth and safe lane changing under various constraints. Compared with traditional lane changing of driver, the safety, comfort, and multi-objective comprehensive performance of the method are improved by 5.1%, 3.3%, and 1.7%, respectively, which effectively improves the personification of intelligent vehicle lane-changing trajectory planning in dynamic environments. 2 tabs, 11 figs, 30 refs.
- intelligent vehicle /
- lane changing /
- path planning /
- multi-objective optimization /
- whale optimization algorithm

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图 1 换道潜在碰撞场景

Figure 1. Potential collision scenarios during lane changing

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图 2 BP神经网络拓扑结构

Figure 2. Topology structure of BP neural network

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图 3 GA-BP神经网络预测结果

Figure 3. Prediction results of GA-BP neural network

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图 4 安全换道示意

Figure 4. Schematics of safety lane changing

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图 5 换道轨迹簇

Figure 5. Trajectory cluster of lane changing

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图 6 算法结果对比

Figure 6. Comparison of algorithm results

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图 7 最优轨迹仿真结果

Figure 7. Optimal trajectory simulations results

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图 8 试验测试平台

Figure 8. Test platform for experiment

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图 9 最优换道轨迹与驾驶人换道轨迹

Figure 9. Comparison between optimal lane changing trajectory and driver's lane changing trajectory

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图 10 最优换道轨迹与驾驶人换道轨迹动态特性对比

Figure 10. Comparison of dynamic characteristics between optimal lane changing trajectory and driver's lane changing trajectory

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图 11 最优换道轨迹与驾驶人换道轨迹综合性能评价对比

Figure 11. Comparison of comprehensive performance evaluation between optimal lane changing trajectory and driver's lane changing trajectory

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表 1 GA-BP神经网络输出误差分析

Table 1. Output error analysis of GA-BP neural network

	指标	最大误差	平均绝对误差	均方根误差
BP神经网络	终止时刻/s	1.55	0.62	0.74
BP神经网络	目标位置/m	8.25	4.57	7.48
GA-BP神经网络	终止时刻/s	1.19	0.40	0.50
GA-BP神经网络	目标位置/m	3.25	2.57	4.49

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表 2 最优换道轨迹与驾驶人换道轨迹综合评价

Table 2. Comprehensive assessment of optimal lane changing trajectory and driver's lane changing trajectory

指标	L_e(t)/g	L_c(t)/(m·s^-2)	d₁/m	d₂/m	J
驾驶人换道	13.336	1.813	24.853	14.852	-0.178
最优轨迹换道	12.654	1.504	25.730	15.058	-0.236

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