Abstract: Existing vehicle-infrastructure cooperative perception task scheduling methods face two major challenges: insufficient characterization of computational resource hard constraints, and the difficulty of meeting scheduling decision efficiency in dynamic environments. Furthermore, during the task scheduling process, bidirectional data transmission exists between Connected Autonomous Vehicles (CAVs) and Roadside Units (RSUs), where RSUs send perception data to CAVs, and CAVs offload tasks to RSUs and receive the results. In this process, the RSU-side computational delay in the task completion delay model is inversely proportional to the allocated computational resources, which introduces fractional nonlinear terms into the delay model and further increases the mathematical complexity of the problem. To address the above issues, this paper considers the scenario where both RSUs and CAVs are subject to computational resource upper bound constraints, and constructs a vehicle-infrastructure cooperative perception task scheduling model aimed at minimizing the total system delay. At the communication level, the model adopts Orthogonal Frequency Division Multiple Access (OFDMA) as the multiple access scheme, where the RSU allocates orthogonal subcarriers to each vehicle to ensure equal communication bandwidth between CAVs and the RSU. Meanwhile, considering the Doppler shift and Inter-Carrier Interference (ICI) caused by high-speed vehicle mobility, a quantitative relationship between vehicle speed and data transmission rate is established. At the computational resource allocation level, the model explicitly characterizes four types of constraints: the RSU total computational resource upper bound, the per-task computational resource upper bound, the RSU maximum number of parallel tasks, and the CAV onboard computational resource upper bound. From the RSU perspective, the core decision is to select which CAVs' tasks to admit and to allocate computational resources to them, thereby coupling task offloading decisions with computational resource allocation. On this basis, this paper takes minimizing the sum of task completion delays of all CAVs in the system as the objective function, takes the binary task offloading decision variable, the continuous RSU computational resource allocation variable, and the continuous CAV local computational resource allocation variable as the core optimization variables, and introduces the task completion delay as an auxiliary variable, to construct a mixed-integer nonlinear programming model with fractional nonlinear constraints. To solve the difficulty caused by nonlinear constraints, this paper proposes a customized solution algorithm based on the outer approximation method. The algorithm exploits the convex separable property of the delay function, performs a first-order Taylor expansion on the delay expression of RSU task processing to generate outer approximation cuts, and transforms the original nonlinear model into a mixed-integer linear programming problem. To address the shortcomings of the traditional outer approximation method, namely its sensitivity to initial points and insufficient convergence stability, this paper proposes an initial point selection strategy: taking the maximum computational resource allowable to a single task as the initial point, to improve the solution quality and efficiency of the model. To evaluate model performance, experiments compare the proposed model with two baseline schemes: Baseline 1: all CAVs process tasks locally without using RSU resources; Baseline 2: CAVs prioritize offloading tasks to the RSU, with tasks exceeding the RSU's capacity processed locally by CAVs, and the RSU allocates equal computational resources to each offloaded task. Experimental results demonstrate that, compared with Baseline 2, the proposed model reduces the total task completion delay by 15.6% and achieves a maximum reduction of 32.4% in the task completion delay for a single CAV. This paper also compares the proposed algorithm with the Genetic Algorithm and the Adaptive Large Neighborhood Search Algorithm. Experimental results demonstrate that, in terms of solution efficiency, the proposed initial point selection strategy enables the algorithm to converge in 2.55 rounds on average, with the solution time controlled between 0.91 and 2.86 seconds. Compared with the Genetic Algorithm, the proposed algorithm achieves an average saving of 56.2% in solution time. These results indicate that the proposed vehicle-infrastructure cooperative perception task scheduling model and method under computational resource constraints can effectively reduce the total system delay and improve the efficiency of scheduling decisions.

摘要： 现有车路协同感知任务调度方法面临两大核心问题：计算资源硬约束刻画不足，以及调度决策效率难以满足动态环境需求。此外，在车路协同感知任务调度过程中，智能网联汽车（Connected Autonomous Vehicle, CAV）与路侧单元（Roadside Unit, RSU）之间存在双向数据传输（RSU向CAV下发感知数据、CAV向RSU卸载任务并接收结果）。在这一过程中，任务完成时延模型中RSU端计算时延与分配的计算资源呈反比关系，从而在时延模型中引入分式非线性项，进一步提升了问题的数学复杂度。针对上述问题，本文考虑RSU和CAV两端均存在计算资源上限约束的情形，构建车路协同感知任务调度模型，旨在最小化系统总时延。在通信层面，模型采用正交频分多址（Orthogonal Frequency Division Multiple Access, OFDMA）接入方式，通过RSU为各车辆分配正交子载波，保证CAV与RSU之间具备均等的通信带宽；同时考虑车辆高速移动产生的多普勒频移与载波间干扰（Inter-Carrier Interference, ICI），建立车速与数据发送速率之间的定量关系。在计算资源分配层面，模型显式刻画RSU总计算资源上限约束、单任务计算资源上限约束、RSU最大并行任务数约束以及CAV车载计算资源上限约束。从RSU视角，核心决策是选择接纳来自哪些CAV的任务并为其分配计算资源，使任务卸载决策与计算资源分配相互耦合。在此基础上，本文以最小化系统内所有CAV的任务完成时延总和为目标函数，以任务卸载决策变量、RSU计算资源分配连续变量、CAV本地计算资源分配连续变量为核心优化变量，并引入任务完成时延作为辅助变量，构建了包含分式非线性约束的混合整数非线性规划模型。为解决非线性约束导致的求解困难，本文基于外逼近法提出一套定制求解算法。该算法利用时延函数的凸可分特性，对RSU处理任务的延迟表达式进行一阶泰勒展开，生成外逼近切割，将原非线性模型转化为混合整数线性规划问题。针对传统外逼近法对初始点敏感、收敛稳定性不足的缺陷，本文提出初始点选取策略：将单个任务可分配的最大计算资源作为初始点，以提升模型求解质量和求解效率。为评估模型性能，实验将本文模型与两种基准方案进行对比：（1）所有CAV完全本地处理任务，不使用RSU资源；（2）CAV优先将任务卸载至RSU，超出RSU承载能力的任务由CAV本地处理，RSU为每个被卸载的任务分配均等计算资源。实验结果表明：相较于CAV优先将任务卸载至RSU的方案，本文模型将任务完成时延总和降低15.6%；其中单辆CAV的任务完成时延最大降幅达32.4%。本文还将所提算法与遗传算法和自适应大邻域搜索算法进行对比。实验结果表明：在求解效率方面，本文提出的初始点选取策略可使算法平均2.55轮收敛、求解用时控制在0.91~2.86秒；本文算法相较于遗传算法在求解用时上平均节省56.2%。以上结果表明，本文提出的计算资源约束下的车路协同感知任务调度模型与方法，可有效降低系统总时延，提升调度决策效率。