面向自动协同驾驶的多车编队任务分配策略

李长乐; 张云锋; 张尧; 毛国强; 贾存兴

doi:10.11999/JEIT190557

面向自动协同驾驶的多车编队任务分配策略

doi: 10.11999/JEIT190557

李长乐^1, ,,
张云锋¹,
张尧¹,
毛国强^{1, 2},
贾存兴³

1.
西安电子科技大学综合业务网理论及关键技术国家重点实验室西安 710071
2.
悉尼科技大学电气与数据工程系悉尼 2007
3.
河北省高速公路京雄筹建处雄安 071799

基金项目: 国家自然科学基金(U1801266)，陕西省重点研发计划项目(2018ZDXM-GY-038, 2018ZDCXL-GY-04-02)，陕西高校青年创新团队，西安市科技计划项目(201809170CX11JC12)

详细信息

作者简介:
李长乐：男，1976年生，教授，博士生导师，研究方向为网联网控无人驾驶、智能网联汽车超视距感知、交通大数据分析及应用、大规模网络技术、高动态网络技术等

张云锋：男，1996年生，硕士生，研究方向为车辆编队、协同驾驶

张尧：男，1993年生，博士生，研究方向为车联网、边缘计算、无线传感器网络

毛国强：男，1974年生，教授，博士生导师，研究方向为智能交通技术、车联网、智慧公路与智能网联驾驶、下一代移动通信系统(5G)关键技术研发、物联网、无线定位技术等

贾存兴：男，高级工程师，研究方向为公路与水路运输、建筑科学与工程

通讯作者:
李长乐　clli@mail.xidian.edu.cn

中图分类号: TN929.5
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出版历程
- 收稿日期: 2019-07-25
- 修回日期: 2019-11-28
- 网络出版日期: 2019-11-29
- 刊出日期: 2020-01-21

Task Assignment Strategy for Platoons in Cooperative Driving

1.
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
2.
School of Electrical and Data Engineering, University of Technology Sydney, Sydney 2007, Australia
3.
Hebei Expressway Preparation and Construction Office for Beijing-Xiongan Expressway, Xiongan 071799, China

Funds: The National Natural Science Foundation of China (U1801266), The Key Research and Development Program of Shaanxi (2018ZDXM-GY-038, 2018ZDCXL-GY-04-02), The Youth Innovation Team of Shaanxi Universities, The Science and Technology Projects of Xi’an (201809170CX11JC12)

摘要

摘要: 自动驾驶的实现需要大量车载传感器的支持，然而，在有限车载计算资源条件下，由传感器所产生的庞大数据量使得自动驾驶任务的实时性难以满足，成为阻碍自动驾驶技术进一步发展的重要阻力。通过将驾驶任务进行协作处理，因而充分利用多个协作车辆的计算资源，自动协同驾驶成为解决该问题的新途径。而如何形成多车编队并实现编队中驾驶任务分配则是实现自动协同驾驶的关键。该文首先采用排队理论G/G/1模型建立一种普适性车辆编队网络拓扑分析模型，充分考虑编队内车辆间的任务协作能力和单个车辆的任务负荷，得出任务的处理时延和车辆系统中的平均任务数；其次，采用支持向量机(SVM)方法，基于车辆的负荷程度及处理能力将车辆的“空闲”、“繁忙”两状态进行分类，进而建立针对车辆协作任务分配的候选车辆集。最后，基于上述分析，该文提出面向多车编队协同驾驶的任务均衡策略——基于分类的贪婪均衡策略(C-GBS)，以充分平衡编队内所有车辆的任务负荷并利用不同车辆的任务处理能力。仿真结果表明，该策略能够减小重负荷网络中的任务处理时延，有效提升自动驾驶车辆的任务处理效率。
- 自动协同驾驶 /
- 多车编队 /
- 任务分配 /
- 排队论 /
- 支持向量机
Abstract: Autonomous vehicles are equipped with multiple on-board sensors to achieve self-driving functions. However, a tremendous amount of data is generated by autonomous vehicles, which significantly challenges the real-time task processing. Through multiple-vehicle cooperation, which makes the best of vehicle onboard computing resources, autonomous and cooperative driving becomes a promising candidate to solve the aforementioned problem. In this case, it is vital for autonomous and cooperative driving to form a driving platoon and allocate driving tasks efficiently. In this paper, a more general analytical model is developed based on G/G/1 queueing theory to model the topology of platoons. Next, Support Vector Machine (SVM) method is adopted to classify the “idle” and “busy” categories of the vehicles in the platoon based on their computing load and task processing capacity. Finally, based on the analysis above, an efficient task balancing strategy of platoons in autonomous and cooperative driving called Classification based Greed Balancing Strategy (C-GBS) is proposed, in order to balance the task burden among vehicles and cooperate more efficiently. Extensive simulations demonstrate that the proposed technique can reduce the processing delay of driving tasks in platoons with high computing load, which will improve the processing efficiency in autonomous vehicles.
- Autonomous and cooperative driving /
- Platoon /
- Task assignment /
- Queuing theory /
- Support Vector Machine (SVM)

HTML全文

图 1 自动协同驾驶的多车编队场景示意图

下载: 全尺寸图片幻灯片

图 2 车辆编队网络拓扑建模

下载: 全尺寸图片幻灯片

图 3 不同分配策略的任务总时延

下载: 全尺寸图片幻灯片

图 4 不同分配策略的处理时延比值关系图

下载: 全尺寸图片幻灯片

图 5 不同分配策略的系统资源利用率

下载: 全尺寸图片幻灯片

表 1 C-GBS算法

　输入：车辆集

$V$ ，任务集T

　输出：结果集S

　(1) 基于对车辆状态的分类，初始化候选车辆集

${V_1}$ 和结果集S；

　(2) 遍历任务集T，选取T 中时延门限

${T_i}$ 最小的任务

${t_i}$ ，对其进行
　　分配；

　(3) 选择候选车辆集

${V_1}$ 中的第1辆车

${v_{k,1}}$ ，根据

${v_{k,1}}$ 的处理速率和
　　任务

${t_i}$ 的size_i估计

${v_{k,1}}$ 处理任务

${t_i}$ 所需的时间

${\tau _{i,1}}$ ，并令
　　

${\tau _i} = {\tau _{i,1}},\kappa = 1$ ；

　(4) 遍历候选车辆集

${V_1}$ ，依次计算

${V_1}$ 中每辆车v_k处理任务

${t_i}$ 所需
　　时间

${\tau _{i,k}}$ ，若

${\tau _{i,k}}<{\tau _{i}}$ ，则令

${\tau _i} = {\tau _{i,k}},\kappa = k$ ；

　(5) 遍历

${V_1}$ 完成后，将任务

${t_i}$ 分配给

${V_1}$ 中的第

$\kappa$ 辆车处理；

　(6) 更新车辆v_k的状态，更新候选车辆集

${V_1}$ ，更新任务集T并更
　　新结果集S记录每项任务的处理情况；

　(7) 返回第(2)步，继续执行，直到任务全部完成。

下载: 导出CSV

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