UAV-assisted jam-absorption driving strategy for on-ramp weaving sections
Article Text (Baidu Translation)
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摘要: 高速公路入口匝道交织区易产生具有自激性和可传播性的走停波，影响通行效率与能耗。面向未来无人机与智能网联车协同的应用场景，提出并验证了一套用于入口匝道交织区的无人机协同吸波驾驶策略，在智能网联车辆低渗透率的条件下进行了多方案对比评估。对照设定的4种方案，即仅观测无人机作为基准方案、传统车路协同吸波、自适应动态控制吸波、无人机与智能网联车协同吸波，构建了一体化流程，依次完成识别、预测与控制；利用多架无人机连续观测交织区，识别速度显著下降带及其传播方向，确定了走停波的空间位置与移动趋势；计算走停波在上游经过关键位置的时间范围，形成到达时间窗，并据此确定触发控制时间及目标速度；在到达时间窗内从车流中选择满足通信与安全约束的智能网联车辆，施加温和的稳速控制，使其进入交织区前小幅降速，通过后逐步恢复；全过程设定安全距离、加减速上限与速度回升门限，保证了可行与安全。分析结果表明：在开源微观交通仿真平台构建的入口匝道交织区场景中，相较于仅观测无人机的基准方案，无人机与智能网联车协同吸波使平均通行时间由65.78 s降至63.71 s，下降3.1%；波级指标显示拥堵严重度降低，速度分布整体上移；在渗透率为2%的条件下，触发覆盖率与选车成功率保持稳定；在相同需求与扰动强度下，其抑波效益优于渗透率为5%的传统车路协同方案和渗透率为2%的自适应动态控制策略方案。无人机提供的高视角观测与智能网联车辆的稳速干预可在低渗透率与轻路侧条件下实现可实施的吸波治理，适用于高速公路入口匝道交织区，并具备与可变限速和匝道计量协同应用的潜力。Abstract: Self-exciting and propagating stop-and-go waves are easily generated in expressway entrance ramp weaving sections, affecting traffic efficiency and energy consumption. Therefore, for a future application scenario of coordination between unmanned aerial vehicles (UAVs) and connected autonomous vehicle (CAV), a UAV-assisted jam-absorption driving strategy for entrance ramp weaving sections was proposed and validated. A multi-scenario comparative evaluation was conducted under the condition of a low CAV penetration rate. Based on four preset schemes, namely, UAV observation only as the baseline; traditional vehicle-road coordination jam-absorption; adaptive dynamic control jam-absorption; and UAV and CAV coordinated jam-absorption, an integrated process was constructed, and identification, prediction, and control were completed sequentially. Multiple UAVs were used to continuously observe the weaving sections to identify the significant speed-drop region and its propagation direction, determining the spatial location and movement trend of the stop-and-go waves. The time range for the stop-and-go wave to pass a key upstream location was calculated to form an arrival time window, and the control trigger time and the target speed were determined accordingly. Within the arrival time window, CAVs satisfying communication and safety constraints were selected from the traffic flow. A gentle speed stabilization control was applied on them to allow these vehicles to decelerate slightly before entering the weaving section. Their speed gradually recovered after passing through. Throughout the entire process, safety distance, acceleration and deceleration limits, and a speed recovery threshold were set to ensure feasibility and safety. The analysis results show that, in an entrance ramp weaving section scenario constructed on an open-source microscopic traffic simulation platform, compared to the UAV observation-only scenario, the average travel time is reduced from 65.78 s to 63.71 s, a decrease of 3.1%, by the UAV and CAV coordinated jam-absorption. Wave-level indicators show that congestion severity is reduced, and the speed distribution is shifted upward overall. At a 2% penetration rate, the trigger coverage rate and the vehicle selection success rate remain stable. Under the same demand and disturbance intensity, its wave-suppression benefit is superior to the traditional vehicle-road coordination scheme with a 5% penetration rate, and also to the adaptive dynamic control strategy scheme with a 2% penetration rate. Implementable jam-absorption governance can be achieved through high-angle observation provided by UAVs and speed stabilization intervention by CAVs under conditions of low penetration rate and light roadside infrastructure. The approach is applicable to expressway entrance ramp weaving sections and possesses the potential for coordinated application with variable speed limits and ramp metering.
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图 2 路网与瓶颈示意以及SUMO仿真关键场景
Figure 2. Road network and bottleneck diagrams, as well as key scenes from SUMO simulations
图 4 四种方案波级指标对比(拥堵严重度与速度中位数)
Figure 4. Comparison of four scheme wave level indicators (congestion severity and median speed)
图 5 不同UAV部署策略场景及相应的敏感性分析结果
Figure 5. Different UAV deployment strategy scenarios and the sensitivity analysis results
表 1 不同类型车辆动力学参数
Table 1. Dynamic parameters of different vehicle types
车辆类型 最大速度/(m·s-1) 加速度/(m·s-2) 最小车头时距/s 最小车间距/m 驾驶波动系数 车长/m 最大值 期望值 小汽车 33.33 1.40 2.00 1.00 2.00 0.60 4.50 慢速小汽车 25.00 1.00 2.00 1.20 2.00 0.40 4.50 货车 25.00 0.80 1.30 1.70 3.00 0.60 12.00 慢速货车 22.22 0.60 1.30 1.90 3.00 0.40 12.00 
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表 2 超参数设置
Table 2. Hyperparameter settings
参数 取值 说明 测试范围 设置依据 Δt/s 0.2 仿真步长 0.05，0.10，0.20，0.50，1.00 对应5 Hz的更新频率，是微观交通仿真中平衡计算效率与捕捉车辆动态细节的设置 M 2 无人机数量 1，2，3 根据仿真路段长度(230 m加速车道+前后缓冲区)和单机典型视场，2架无人机可实现对关键区域的全覆盖 Tgap/s 2.5 允许的跨视场到达时间差上界 1.0，1.5，2.0，2.5，3.0 根据自由流车速与无人机视场重叠区的长度估算。允许2.5 s的误差，可应对波速的轻微波动，保证同一波在跨越视场时的连续追踪 Sthr 0.60 拥堵触发阈值 0.50，0.52，0.54，0.56，0.58，0.60 通过对无控制仿真场景下拥堵严重度的大量观测与统计分析，选定该值以灵敏地捕捉真实的拥堵波，同时滤除日常的轻微速度波动 Srel 0.52 拥堵释放阈值 0.50，0.52，0.54，0.56，0.58，0.60 设置Srel＜Sthr形成滞回比较，其核心目的是防止因拥堵严重度S在阈值附近小幅震荡而导致拥堵状态的误判和频繁切换，增强判别的鲁棒性 Ton/s 4 触发持续时间 1，2，3，4，5 要求拥堵状态必须持续一定时间才被确认。4 s约为2~3辆车以较慢速度通过一个观察点所需的时间，用于滤除瞬时的、非持续性的交通扰动 Toff/s 4 释放持续时间 1，2，3，4，5 同理，要求拥堵缓解状态持续一定时间才确认波的结束，避免过早解除警报 Tblk/s 2 空窗时间阈值 1，2，3，4，5 允许在持续拥堵中存在短暂的数据缺失，2 s的设定提供了必要的容错空间，保证识别的连续性 Nmin 7 最小样本阈值 4，5，6，7，8 统计学上的小样本要求，确保计算得到的宏观量(如平均速度、密度)具有一定的统计代表性 ϕmin 0.4 慢车占比下限 0.1，0.2，0.3，0.4，0.5 仅当区域内有超过40%的车辆为“慢车”时才考虑触发，确保拥堵是群体性行为而非个别慢车导致 g 0.6 慢车阈值系数 0.60，0.62，0.64，0.66，0.68，0.70 在交通流理论中，当车速降至自由流速度的60%~70%以下时，通常认为交通进入了拥挤或不稳定状态。0.6是一个具有明确物理意义的常用分界点 Tcool/s 10 视场冷却时间 8，9，10，11，12 其时长大于一条典型走停波穿过无人机视场边界所需的时间，确保同一条波进入新视场时不会被错误地识别为一条新波
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表 3 走停波识别结果
Table 3. Identification results for the congestion waves
识别波ID 开始时间/s 持续时间/s 波速/(m·s-1) 车速中位数/(m·s-1) 拥堵严重度S的中位数 1 63.4 39.6 -9.82 3.21 0.67 2 133.8 35.8 -9.71 3.44 0.58 3 217.4 35.6 -8.79 1.16 0.65 4 351.2 42.4 -3.96 2.64 0.58 5 375.6 42.0 -7.04 2.74 0.65 6 450.6 182.8 -5.37 2.77 0.60 7 541.0 61.8 -4.58 2.15 0.58 8 655.4 136.2 -4.17 1.18 0.60 9 675.8 139.0 -6.50 2.45 0.60 10 833.6 65.6 -4.57 1.65 0.60 11 856.8 54.2 -4.53 1.82 0.62 12 933.2 16.2 -5.61 2.46 0.56 13 943.4 57.4 -5.40 1.47 0.58
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表 4 效率指标
Table 4. Efficiency indexes
对照方案 平均通行时间/s 平均时间损失/s 平均等待时间/s 空间平均速度/(m·s-1) 算数平均速度/(m·s-1) 平均CO2强度/(g·km-1) 平均百公里油耗/L B0 65.78 29.18 0.684 14.39 16.85 277.26 11.78 B1(变化率) 64.80(-1.5%) 31.00(+6.2%) 0.677(-1.0%) 13.96(-3.0%) 16.82(-0.2%) 276.98(-0.1%) 11.60(-1.5%) B2(变化率) 64.53(-1.9%) 31.12(+6.6%) 0.673(-1.6%) 14.04(-2.4%) 16.88(+0.2%) 276.90(-0.1%) 11.53(-2.1%) B3(变化率) 63.71(-3.1%) 30.73(+5.3%) 0.670(-2.0%) 14.16(-1.6%) 16.94(+0.5%) 276.89(-0.1%) 11.37(-3.5%)
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表 5 分车型效率指标
Table 5. Vehicle type efficiency indexes
车型 平均通行时间/s 平均时间损失/s 平均等待时间/s 空间平均速度/(m·s-1) 小汽车 B0: 65.09 / B3: 60.91 B0: 32.56 / B3: 29.32 B0: 0.66 / B3: 0.53 B0: 14.59 / B3: 15.16 慢速小汽车 B0: 64.73 / B3: 65.60 B0: 27.91 / B3: 28.69 B0: 0.68 / B3: 0.89 B0: 13.93 / B3: 13.73 货车 B0: 72.11 / B3: 68.92 B0: 33.40 / B3: 30.38 B0: 0.67 / B3: 0.58 B0: 13.18 / B3: 13.73 慢速货车 B0: 70.99 / B3: 72.38 B0: 28.16 / B3: 29.40 B0: 0.65 / B3: 0.88 B0: 13.23 / B3: 13.02 注：粗体数值表示对应指标下的更优结果。
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