DeepSignal-S1 Simulation Scenes
GitHub · Source Code & Reproduction
GitHub · Model · Reproduction · 简体中文
This repository supplies simulation assets for DeepSignal-S1-1.0 under access approval. Model weights require separate approval from AIMS2025/DeepSignal-S1. Demo media and result data are hosted separately without approval requirements.
Assets: keep v3, update the code
The release uses DeepSignal-S1-Scenes-v3.tar.gz. Downloading the fusion media does not require downloading the scene package again. The archive contains Nansha and Town10HD UE5 assets, the CARLA 0.9.16 Town runtime, matching Python clients, traffic plans, YOLO11m baseline assets and component licenses.
Use the v3 assets with the latest GitHub code. The code snapshot inside the archive predates the final fusion evaluation entrypoint and must not be used alone to claim current reproduction.
pip install -U huggingface_hub
hf download AIMS2025/DeepSignal-S1-Scenes DeepSignal-S1-Scenes-v3.tar.gz \
--repo-type dataset --local-dir downloads
git clone https://github.com/AIMS-Laboratory/DeepSignal-S1.git
cd DeepSignal-S1
python3.10 -m venv .venv
.venv/bin/pip install -e '.[test,reproduction,download]'
.venv/bin/deepsignal-s1 install-assets \
--archive ../downloads/DeepSignal-S1-Scenes-v3.tar.gz
Five Town10HD Traffic-Demand Scenarios
All five regimes use the same four-way junction, varying directional demand, load and peak timing rather than road geometry. The table gives the settings used in the current evaluation.
| Demand scenario | Flow per approach (vehicles/hour) | Temporal pattern | Control challenge |
|---|---|---|---|
| Balanced flow | All four: 360 each | Constant throughout | Basic control with similar demand in all directions |
| Sustained directional imbalance | E and W: 600 each N and S: 120 each | Constant major/minor ratio 5:1 | Prioritize persistently busier approaches |
| Higher-load directional imbalance | E and W: 750 each N and S: 150 each | Constant still 5:1 | 25% more total demand than the preceding regime |
| Time-varying peak | All approaches: 200 → 700 → 200 | 0-60 s 60-120 s from 120 s | Adapt to rising demand and peak recovery |
| Shifted peak timing | All approaches: 200 → 200 → 700 | 0-60 s 60-120 s from 120 s | Adapt to changes in peak timing |
Flows are expected arrival rates per approach, not per lane or exact hourly vehicle counts; arrivals are sampled randomly. All regimes use 70% straight, 15% left and 15% right turns. Time starts at simulation startup, with the final rate held until demand generation ends. Shifted peak timing reorders the time-varying rates by seed; the table shows the configuration behind the current results. Controllers receive identical vehicle plans.
Fusion 1.0 results
The following tables use the same fixed-time plan and traffic metrics as the GitHub README. This simulation-asset repository retains its existing approval requirements.
Videos and GIFs are not embedded on this homepage. The original recordings remain available in the public media repository and on GitHub; no media or simulation archives have been deleted.
This evaluation examines the traffic-control performance of direct visual decisions and the effects of changes in visual input. YOLO+MP combines YOLO vehicle detection with Max Pressure signal control; the comparison measures vehicle waiting, queues and journey time.
Clean views: comparable to YOLO+MP, with small gains on some metrics
Across 72 cases at the nine junctions, S1 reduces mean journey time by 1.36% relative to YOLO+MP; across five standard simulation regimes, mean journey time is essentially tied. Overall, S1 delivers comparable control in clean views, with small gains on some metrics.
The fixed baseline is a reasonable fixed-time plan optimized through engineering design, with parameters determined and frozen before evaluation and held unchanged throughout. Against this plan, S1 reduces mean journey time by 18.17% and 33.77% in the two settings, respectively.
Visual perturbations: control performance under input changes
Defocus, blur, camera shifts and shake can affect vehicle detection or lane-region alignment, with expected downstream effects on count-based signal control. In the two settings and four perturbations tested here, S1 records lower mean waiting, queues and journey time than YOLO+MP. Full data and charts covering all eight groups are provided in the results below.
Each perturbation covers one case at each of the nine junctions and five cases in standard simulation, using one seed per scene. YOLO lane regions are calibrated on clean views and frozen; fixed camera shifts are not recalibrated. These results apply to the tested setup and do not establish real-road, night-time or adverse-weather performance.
Clean views: three controllers — full values and three-metric chart
| Scene | Condition | Cases | Controller | Mean wait (s/veh) | Mean queue (veh) | Mean journey (s/veh) |
|---|---|---|---|---|---|---|
| Nine-junction test | Clean | 72 | S1 | 45.823 | 10.473 | 107.316 |
| Fixed-time plan | 68.545 | 15.525 | 131.149 | |||
| YOLO+MP | 46.890 | 10.559 | 108.792 | |||
| Standard simulation | Clean | 5 | S1 | 20.450 | 3.565 | 38.215 |
| Fixed-time plan | 40.456 | 5.398 | 57.699 | |||
| YOLO+MP | 20.215 | 3.586 | 38.220 |
Perturbed views: S1 vs YOLO+MP — full values and three-metric chart
| Scene | Condition | Cases | Controller | Mean wait (s/veh) | Mean queue (veh) | Mean journey (s/veh) |
|---|---|---|---|---|---|---|
| Nine-junction test | Defocus σ=9 | 9 | S1 | 76.082 | 15.795 | 141.033 |
| YOLO+MP | 104.862 | 31.040 | 167.484 | |||
| Nine-junction test | 31 px motion blur | 9 | S1 | 67.382 | 17.232 | 131.573 |
| YOLO+MP | 124.591 | 30.184 | 188.343 | |||
| Nine-junction test | Fixed yaw ±15° / pitch ±8° | 9 | S1 | 63.681 | 15.445 | 129.042 |
| YOLO+MP | 87.008 | 21.179 | 154.362 | |||
| Nine-junction test | Dynamic shake 5° | 9 | S1 | 63.398 | 16.171 | 128.251 |
| YOLO+MP | 72.153 | 17.905 | 137.608 | |||
| Standard simulation | Defocus σ=9 | 5 | S1 | 27.511 | 4.101 | 44.431 |
| YOLO+MP | 92.970 | 9.754 | 109.153 | |||
| Standard simulation | 31 px motion blur | 5 | S1 | 20.276 | 3.581 | 38.102 |
| YOLO+MP | 111.256 | 10.239 | 127.707 | |||
| Standard simulation | Fixed yaw ±15° / pitch ±8° | 5 | S1 | 19.226 | 3.455 | 37.107 |
| YOLO+MP | 205.091 | 13.668 | 217.405 | |||
| Standard simulation | Dynamic shake 5° | 5 | S1 | 19.133 | 3.321 | 37.203 |
| YOLO+MP | 21.487 | 3.533 | 39.882 |
Metrics: lower is better for all three. Mean wait includes stopped and insertion waiting; mean queue counts halted vehicles; journey time starts at planned departure, with unfinished vehicles contributing elapsed time at the evaluation cutoff. Detailed definitions.
Means in the tables and clean-view charts are shown to three decimal places to preserve small differences lost to rounding; this does not imply millisecond measurement accuracy or statistical significance.
Detailed interpretation and real-world context
What the Results Show
Clean Views: Assessing the Feasibility of Direct Visual Decisions
In clean views, S1 is broadly comparable to YOLO+MP, with small gains on some metrics; standard-simulation mean journey time is essentially tied. This supports the feasibility of producing signal actions directly from images. Broader evaluation is needed to establish whether the small differences observed across these limited scenes and seeds are consistent.
Visual Perturbations: Understanding the Effects of Input Changes
Traffic demand may stay the same while camera input changes because of lighting, maintenance or vibration. The four tests target distinct imaging problems: can S1 continue making effective signal decisions when images lose sharpness or stability, or no longer match the original viewpoint?
- Defocus: inaccurate focus softens vehicle outlines. Mixed lighting, including headlights at night, can disrupt autofocus; manual focus adjustment or lens replacement can also leave the intersection out of focus. Softer vehicle and lane edges make recognition and counting harder.
- Motion blur: movement during exposure creates directional smearing. In low light at dusk or night, a camera may use longer exposures. Moving vehicles or camera movement during exposure can then smear outlines and obscure separation between vehicles.
- Fixed camera-angle shifts: a stable view from a changed direction. For example, maintenance-related re-aiming or a shifted mount can move lane and stop-line positions in the image, even after the camera becomes steady. Previously drawn lane regions may no longer align. Our comparison calibrates YOLO at the original pose, then holds the changed camera angle fixed without recalibration.
- Dynamic shake: the viewpoint keeps moving over time. Wind gusts or vibrations from nearby heavy vehicles can shake a pole-mounted camera, shifting vehicle and lane positions across successive frames. Unlike a fixed angle shift, this disturbance continues over time.
These tests examine how visual input changes affect the complete control system. In particular, fixed camera shifts can combine changes in detection quality with lane-region misalignment, so the results should be interpreted in light of the no-recalibration setting. Richer traffic-scene understanding requires dedicated traffic-event evaluations.
The tests simulate visual changes that these real-world problems can produce; they are not completed night-time field or real-world adverse-weather validation. Real-world context and technical sources.
Sub-Second Decisions From a Visual Model
Across 6823 unquantized RTX 5090 decision requests, latency averages 485 ms, with 99.08% below one second; steady-state requests average 477 ms. System One emits structured control actions directly, without waiting for a long textual response, combining visual understanding, traffic-control performance and fast decisions.
Evaluation policy
- DeepSignal-S1-1.0 is trained on simulation scenes from nine Nansha intersections and five traffic-demand scenarios at one four-arm intersection in CARLA's official Town10HD map.
- The sole fixed baseline is the fixed-time plan, with timing parameters held constant throughout evaluation: Nansha uses the published per-intersection plans and Town uses
31/6/31/6 s. - Clean views compare S1, the fixed-time plan and YOLO+MP. Perturbed views compare S1 with YOLO+MP only.
- Conditions are defocus
σ=9, horizontal motion blur31 px, fixed yaw±15°/ pitch±8°, and dynamic shake with yaw/pitch peak5°and roll peak2.5°. - YOLO lane regions are calibrated on the clean pose and frozen. The fixed-angle condition moves the cameras after calibration and does not recalibrate them.
- Nansha and Town use different finite-horizon metrics and are not pooled.
The public scripts/run_fusion_matrix.py and reproduction/fusion-release-matrix.json define the current entrypoint. CPU-only checks and the completed 6/6 fixed action fixtures validate packaging behavior, not a fresh-download visual closed-loop reproduction. Such a full public replay has not yet been certified.
Historical material
The earlier DeepSignal-S1-1.0-candidate Nansha checkpoint and separate historical Town checkpoint used independent weights and protocols. Their records remain historical and must not be attributed to the fused 1.0 model. Historical Town timing that omitted the committed five-second green tail is also not a current result.
Licenses
This is not a tabular dataset for datasets.load_dataset. Preserve all CARLA, Unreal Engine, operating-system, map/material, Ultralytics and other component notices. No blanket license replaces the licenses bundled with individual components. Synthetic traffic plans are not real-world traffic measurements.
简体中文
本数据集提供 DeepSignal-S1-1.0 的仿真资产，资产下载保留审批要求，模型权重在独立HF模型仓库另行审批下载。展示媒体与结果数据在DeepSignal-S1-Media公开，无需审批。
场景继续沿用 DeepSignal-S1-Scenes-v3.tar.gz；只下载融合版媒体时，无需重复下载场景包。v3包含南沙和Town10HD的UE5资产、Town CARLA 0.9.16运行环境、匹配客户端、固定交通计划、YOLO11m基线材料及各组件许可。运行融合1.0评测必须配合GitHub最新代码；压缩包内旧代码快照不能单独作为当前复现依据。
Town10HD的5类交通需求
五类车流均设置在同一个十字路口，区别在方向分配、流量大小和高峰出现时点，而不是五个不同路口。下表列出当前评测使用的设置。
| 交通需求场景 | 每个进口方向的流量 （辆/小时） | 时间变化 | 考察重点 |
|---|---|---|---|
| 均衡车流 | 东、西、南、北各360 | 全程恒定 | 四向需求相近时 的基本控制能力 |
| 持续方向不均衡 | 东、西各600 南、北各120 | 全程恒定 主次方向流量比5:1 | 持续向高需求方向 分配绿灯 |
| 更高流量的不均衡 | 东、西各750 南、北各150 | 全程恒定 仍为5:1 | 比上一类总需求增加25% 考察较高负荷 |
| 时变高峰 | 四向均为 200 → 700 → 200 | 0-60秒 60-120秒 120秒起 | 适应车流上升 及高峰消退 |
| 高峰时序变化 | 四向均为 200 → 200 → 700 | 0-60秒 60-120秒 120秒起 | 改变高峰出现时点 考察时序适应能力 |
流量是每个进口方向的期望到达率，不是每车道流量，也不是每小时固定生成的车辆数；车辆按该流率随机到达。五类均采用直行70%、左转15%、右转15%。时间从仿真启动计，末档流量保持至需求生成结束。“高峰时序变化”按种子重排时变流量，上表为当前结果对应的配置。各控制器使用相同的车辆计划。
以下表格与 GitHub 中英文 README 使用同一套固定配时方案和交通指标。本主页不嵌入视频及 GIF；原始素材仍保留在公开媒体仓库和 GitHub 展示中，不删除媒体文件或仿真资产。
本次评测关注直接视觉决策的交通控制效果，以及视觉输入变化对控制表现的影响。YOLO+MP 指由YOLO车辆检测与最大压力（Max Pressure）信号控制组成的方案；比较指标为车辆等待、排队和行程。
正常画面：与YOLO+MP相当，部分指标略优
在9个路口独立测试的72个案例中，S1相对YOLO+MP的平均行程降低 1.36%；标准仿真场景的五类车流中，两者平均行程基本持平。正常画面下，S1的整体控制表现可与YOLO+MP媲美，部分指标略优。
固定对照采用经工程优化的合理固定配时方案，参数在评测前确定并冻结，评测期间保持不变。与该方案相比，S1在上述两组场景的平均行程分别降低 18.17% 和 **33.77%**。
视觉干扰：输入变化下的控制表现
失焦、模糊、机位偏移和抖动可能影响车辆检测或车道区域匹配，继而影响基于检测计数的信号控制，这类影响可以预期。在本次两组场景、四类干扰的测试中，S1的平均等待、排队和行程均低于YOLO+MP。完整数据与全部八组图表见下方结果。
每类干扰在9个路口各测试1个案例，在标准仿真场景测试5个案例，均为单种子仿真。YOLO车道区域在正常机位标定后冻结，固定偏角测试不重新标定。结果适用于本次设置，尚不代表真实道路、夜间或恶劣天气验证。
正常视觉：三方比较 — 完整数值与三指标图
| 场景 | 条件 | 案例数 | 控制方法 | 平均等待（秒/车） | 平均排队长度（辆） | 平均行程（秒/车） |
|---|---|---|---|---|---|---|
| 9个路口独立测试 | 正常 | 72 | S1 | 45.823 | 10.473 | 107.316 |
| 固定配时方案 | 68.545 | 15.525 | 131.149 | |||
| YOLO+MP | 46.890 | 10.559 | 108.792 | |||
| 标准仿真场景 | 正常 | 5 | S1 | 20.450 | 3.565 | 38.215 |
| 固定配时方案 | 40.456 | 5.398 | 57.699 | |||
| YOLO+MP | 20.215 | 3.586 | 38.220 |
特殊视觉：S1与YOLO+MP — 完整数值与三指标图
| 场景 | 条件 | 案例数 | 控制方法 | 平均等待（秒/车） | 平均排队长度（辆） | 平均行程（秒/车） |
|---|---|---|---|---|---|---|
| 9个路口独立测试 | 失焦σ=9 | 9 | S1 | 76.082 | 15.795 | 141.033 |
| YOLO+MP | 104.862 | 31.040 | 167.484 | |||
| 9个路口独立测试 | 31像素运动模糊 | 9 | S1 | 67.382 | 17.232 | 131.573 |
| YOLO+MP | 124.591 | 30.184 | 188.343 | |||
| 9个路口独立测试 | 固定偏航±15° / 俯仰±8° | 9 | S1 | 63.681 | 15.445 | 129.042 |
| YOLO+MP | 87.008 | 21.179 | 154.362 | |||
| 9个路口独立测试 | 动态抖动峰值5° | 9 | S1 | 63.398 | 16.171 | 128.251 |
| YOLO+MP | 72.153 | 17.905 | 137.608 | |||
| 标准仿真场景 | 失焦σ=9 | 5 | S1 | 27.511 | 4.101 | 44.431 |
| YOLO+MP | 92.970 | 9.754 | 109.153 | |||
| 标准仿真场景 | 31像素运动模糊 | 5 | S1 | 20.276 | 3.581 | 38.102 |
| YOLO+MP | 111.256 | 10.239 | 127.707 | |||
| 标准仿真场景 | 固定偏航±15° / 俯仰±8° | 5 | S1 | 19.226 | 3.455 | 37.107 |
| YOLO+MP | 205.091 | 13.668 | 217.405 | |||
| 标准仿真场景 | 动态抖动峰值5° | 5 | S1 | 19.133 | 3.321 | 37.203 |
| YOLO+MP | 21.487 | 3.533 | 39.882 |
指标说明： 三项指标均越低越好。平均等待包括停车等待和入网等待；平均排队长度以停车车辆数计；平均行程从计划出发计时，未完成车辆计入评估截止时的已耗时。详细指标定义。
表格与正常视觉图表的均值统一保留三位小数，以免舍入掩盖细小差异；这不代表测量具有毫秒级精度，也不表示差异具有统计显著性。
详细结果解读与现实场景对应
结果解读
正常视觉：验证直接视觉决策的可行性
正常画面下，S1的控制表现与YOLO+MP总体相当，部分指标略优；标准仿真场景的平均行程基本持平。这支持了直接从画面生成信号动作的可行性。当前有限场景与种子下的小幅差异，还需要更广泛评测来判断其稳定性。
视觉干扰：理解输入变化对控制的影响
现实路口的车流未必改变，摄像头输入却可能因光照变化、设备维护或风振而发生变化。四类测试分别对应不同的成像问题，考察S1在画面不再清晰、稳定或保持原机位时，能否继续作出有效的信号决策。
- 失焦：对焦不准，车辆轮廓变软。 夜间车灯等复杂光源可能干扰自动对焦，人工调焦或更换镜头后也可能没有重新调好焦点。车辆边缘和车道细节随之变得模糊，增加识别与计数的难度。
- 运动模糊：曝光期间的移动形成拖影。 傍晚或夜间光照不足时，摄像头可能采用更长曝光；车辆运动或曝光期间的相机晃动便可能形成方向性拖影，使车辆形状和相邻车辆的边界更难分辨。
- 固定角度偏移：调整后稳定在新的机位。 例如检修后重新调整视角，或支架发生偏转：画面已经稳定，但停车线和车道在图像中的位置改变了，原先画好的车道区域可能不再对应。我们的对比先在正常机位为YOLO画线，再固定改变相机角度，不重新画线。
- 动态抖动：视角随时间来回变化。 高杆摄像头可能受到阵风或邻近重型车辆引起的振动影响，车辆和车道在连续画面中的位置随之摆动；这与调整后保持不动的固定偏角不同。
这组测试观察的是视觉输入变化对整套控制方案的影响。尤其在固定偏角条件下，检测质量和车道区域错位的影响可能叠加，结果应结合不重新标定的设置理解。更丰富的交通场景理解能力，需要通过专门的交通事件任务来验证。
上述测试模拟的是这些现实问题可能造成的视觉变化，不等同于已经完成夜间现场或真实恶劣天气验证。现实场景对应与资料依据。
亚秒级决策：视觉模型也能快速响应
未量化模型在RTX 5090上的 6823 次决策请求平均耗时 485毫秒，99.08% 的请求低于1秒；稳态请求平均 477毫秒。System One直接输出结构化控制动作，无需等待长篇文字生成，让视觉理解、交通控制效果与快速响应能够同时兼顾。
正常与特殊视觉完整结果统一展示平均每车等待时间、平均排队车辆数和平均每车行程时间。
DeepSignal-S1-1.0 的训练覆盖南沙9个路口的仿真场景，以及 CARLA 官方 Town10HD 地图中1个十字交叉口的5类交通需求场景。唯一固定基线是固定配时方案，评测期间保持配时参数不变；正常视觉三方比较，特殊视觉只比较S1与YOLO+MP。固定角度测试在正常机位画线后改变相机，整回合固定且不重新标定。两个场景的有限时域指标不能合并。
公开入口为 scripts/run_fusion_matrix.py 和 reproduction/fusion-release-matrix.json。CPU检查和6/6固定动作样例通过，只说明发布包接口与动作匹配正确，不代表已完成从公开下载开始的视觉闭环认证。
旧candidate南沙权重与历史Town专用权重移入历史说明，不能把其成绩归给融合1.0。使用或再分发前请查看包内各组件许可和GitHub第三方说明。
Contact: dalei@scu.edu.cn
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