feat: initial HSAP platform

Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>

algorithms/lane_ufld/code/UFLD/configs/culane.py

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+# DATA
+dataset = 'CULane'
+data_root = 'C:\\data\\Tusimple\\test_set'
+
+# TRAIN
+epoch = 50
+batch_size = 32
+optimizer = 'SGD'  #['SGD','Adam']
+learning_rate = 0.1
+weight_decay = 1e-4
+momentum = 0.9
+
+scheduler = 'multi' #['multi', 'cos']
+steps = [25,38]
+gamma  = 0.1
+warmup = 'linear'
+warmup_iters = 695
+
+# NETWORK
+use_aux = True
+griding_num = 200
+backbone = '18'
+
+# LOSS
+sim_loss_w = 0.0
+shp_loss_w = 0.0
+
+# EXP
+note = ''
+
+log_path = None
+
+# FINETUNE or RESUME MODEL PATH
+finetune = None
+resume = None
+
+# TEST
+test_model = './model/culane_18.pth'
+test_work_dir = './tmp'
+
+num_lanes = 4
+
+
+
+

algorithms/lane_ufld/code/UFLD/configs/mufld_lane_culane.py

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+# MUFLD lane pack in CULane-style layout for UFLD training.
+# Data root layout:
+#   <data_root>/images/...
+#   <data_root>/annotations/segmentation_masks/...
+#   <data_root>/list/train_gt.txt   (two columns: training split only)
+#   <data_root>/list/val_gt.txt     (validation pairs, optional custom loop)
+#   <data_root>/list/test.txt       (held-out test images, one per line)
+
+dataset = 'CULane'
+data_root = '/home/chengfanglu/DATA/lane0_copy/DATASET'
+
+epoch = 50
+batch_size = 16
+optimizer = 'SGD'
+learning_rate = 0.1
+weight_decay = 1e-4
+momentum = 0.9
+
+scheduler = 'multi'
+steps = [25, 38]
+gamma = 0.1
+warmup = 'linear'
+warmup_iters = 695
+
+use_aux = True
+griding_num = 200
+backbone = '18'
+
+sim_loss_w = 0.0
+shp_loss_w = 0.0
+
+note = 'lane_training_pack_v1'
+log_path = './log'
+
+finetune = None
+resume = None
+
+test_model = './model/culane_18.pth'
+test_work_dir = './tmp'
+
+num_lanes = 4

algorithms/lane_ufld/code/UFLD/configs/mufld_lane_culane_cpu.py

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+# CPU 单机训练示例：batch 需显著减小；学习率可按 batch 相对 16 做线性缩放（可选）。
+#
+# layout 同 configs/mufld_lane_culane.py；
+# lane_light 环境与安装说明见 TRAIN_ENV_CPU.md
+
+dataset = "CULane"
+data_root = "/home/chengfanglu/DATA/lane0_copy/DATASET"
+
+epoch = 50
+batch_size = 4
+optimizer = "SGD"
+# 若在 CPU 上不收敛可先试更小 lr，例如 batch=4 时约 0.1 * (4 / 16) = 0.025
+learning_rate = 0.025
+weight_decay = 1e-4
+momentum = 0.9
+
+scheduler = "multi"
+steps = [25, 38]
+gamma = 0.1
+warmup = "linear"
+# warmup 与原配置按 batch 比例对齐（原为 695 @ bs=16）
+warmup_iters = 174
+
+use_aux = True
+griding_num = 200
+backbone = "18"
+
+sim_loss_w = 0.0
+shp_loss_w = 0.0
+
+note = "lane_training_pack_cpu_bs4"
+log_path = None
+
+finetune = None
+resume = None
+
+test_model = "./model/culane_18.pth"
+test_work_dir = "./tmp"
+
+num_lanes = 4

algorithms/lane_ufld/code/UFLD/configs/mufld_lane_multi_pack.py

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+# Multi-pack training — control merged packs in this config.
+# data_root = parent of DATASET / DATASET-AddBy-* / DATASET-A (alias).
+
+from pathlib import Path
+
+dataset = 'CULane'
+data_root = str(Path(__file__).resolve().parents[5] / "datasets" / "lane")
+
+# Pack names: directory under data_root, or alias from datasets_registry.json
+train_packs = [
+    'lane_v1',
+]
+
+# Default list inside each pack (relative to pack root)
+pack_list_name = 'list/train_gt.txt'
+
+# Cached merged list (auto filename from pack names if merged_train_list is None)
+merged_list_dir = 'lists_merged'
+merged_train_list = None   # e.g. 'lists_merged/train_all_v2.txt'
+remerge_train_list = False # True to rebuild merged list every run
+
+# Single-pack fallback (ignored when train_packs is set)
+train_list = 'list/train_gt.txt'
+
+epoch = 50
+batch_size = 16
+optimizer = 'SGD'
+learning_rate = 0.1
+weight_decay = 1e-4
+momentum = 0.9
+
+scheduler = 'multi'
+steps = [25, 38]
+gamma = 0.1
+warmup = 'linear'
+warmup_iters = 695
+
+use_aux = True
+griding_num = 200
+backbone = '18'
+
+sim_loss_w = 0.0
+shp_loss_w = 0.0
+
+note = 'multi_pack_v2'
+log_path = './log'
+
+finetune = None
+resume = None
+
+test_model = './model/culane_18.pth'
+test_work_dir = './tmp'
+
+num_lanes = 4

algorithms/lane_ufld/code/UFLD/configs/mufld_lane_smoke.py

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+# Smoke test: few samples, 1 epoch, small batch (CPU or GPU).
+dataset = "CULane"
+data_root = "/home/chengfanglu/DATA/lane0_copy/DATASET"
+train_list = "list/train_gt_smoke.txt"
+
+epoch = 1
+batch_size = 2
+optimizer = "SGD"
+learning_rate = 0.025
+weight_decay = 1e-4
+momentum = 0.9
+
+scheduler = "multi"
+steps = [1]
+gamma = 0.1
+warmup = "linear"
+warmup_iters = 10
+
+use_aux = True
+griding_num = 200
+backbone = "18"
+
+sim_loss_w = 0.0
+shp_loss_w = 0.0
+
+note = "dataset_smoke_test"
+log_path = "./log"
+
+finetune = None
+resume = None
+
+test_model = "./model/culane_18.pth"
+test_work_dir = "./tmp"
+
+num_lanes = 4

algorithms/lane_ufld/code/UFLD/configs/tusimple_res18.py

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+# DATA
+dataset = 'Tusimple'
+data_root = '/mnt/HDisk2T/liuxy51/ganxian/data_luojk'
+
+# TRAIN
+epoch = 500 # 10
+batch_size =  32 # 4
+optimizer = 'Adam'    #['SGD','Adam']
+learning_rate = 1e-5
+weight_decay = 1e-4
+momentum = 0.9
+scheduler = 'cos'     #['multi', 'cos']
+
+# steps = [50,75]
+gamma = 0.1
+warmup = 'linear'
+warmup_iters = 100
+
+# NETWORK
+backbone = '18'
+griding_num = 100
+use_aux = False
+
+# LOSS
+sim_loss_w = 1.0
+shp_loss_w = 0.0
+
+# EXP
+note = '_ufld_2lanes_res18'
+
+log_path = './log'
+
+# FINETUNE or RESUME MODEL PATH
+finetune = None
+resume = None
+
+# TESTNone
+test_model = './model/lane_m599_all.pth'
+test_work_dir = './tmp'
+
+num_lanes = 2

algorithms/lane_ufld/code/UFLD/configs/tusimple_res18_4lane_v1.py

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+# DATA
+dataset = 'Tusimple'
+data_root = '/mnt/HDisk2T/liuxy51/ganxian/train_2025_03_13_mufld' # 针对clrnet数据集8.1w帧多车道数据
+
+# TRAIN
+epoch = 500 # 10
+batch_size =  32 # 4
+optimizer = 'Adam'    #['SGD','Adam']
+learning_rate = 1e-5
+weight_decay = 1e-4
+momentum = 0.9
+scheduler = 'cos'     #['multi', 'cos']
+
+# steps = [50,75]
+gamma = 0.1
+warmup = 'linear'
+warmup_iters = 100
+
+# NETWORK
+backbone = '18'
+griding_num = 100
+use_aux = False
+
+# LOSS
+sim_loss_w = 1.0
+shp_loss_w = 0.0
+
+# EXP
+note = '_ufld_2lanes_res18'
+
+log_path = './log'
+
+# FINETUNE or RESUME MODEL PATH
+finetune = None
+resume = None
+
+# TESTNone
+test_model = './model/lane_m599_all.pth'
+test_work_dir = './tmp'
+
+num_lanes = 4

algorithms/lane_ufld/code/UFLD/configs/tusimple_res18_nbg.py

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+# DATA
+dataset = 'Tusimple'
+data_root = '/mnt/HDisk2T/liuxy51/ganxian/train_2024_03_06'
+
+# TRAIN
+epoch = 500 # 10
+batch_size =  32 # 4
+optimizer = 'Adam'    #['SGD','Adam']
+learning_rate = 1e-5
+weight_decay = 1e-4
+momentum = 0.9
+scheduler = 'cos'     #['multi', 'cos']
+
+# steps = [50,75]
+gamma = 0.1
+warmup = 'linear'
+warmup_iters = 100
+
+# NETWORK
+backbone = '18'
+griding_num = 100
+use_aux = False
+
+# LOSS
+sim_loss_w = 1.0
+shp_loss_w = 0.0
+
+# EXP
+note = '_ufld_2lanes_res18'
+
+log_path = './log'
+
+# FINETUNE or RESUME MODEL PATH
+finetune = None
+resume = '/mnt/HDisk2T/liuxy51/ganxian/UFLD/log/20240607_162111_lr_1e-05_b_32_ufld_2lanes_res18/ep304.pth' # None
+
+# TESTNone
+test_model = './model/lane_m599_all.pth'
+test_work_dir = './tmp'
+
+num_lanes = 2

algorithms/lane_ufld/code/UFLD/configs/tusimple_res18_nbg2.py

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+# DATA
+dataset = 'Tusimple'
+data_root = '/mnt/HDisk2T/liuxy51/ganxian/train_2024_03_06_1'
+
+# TRAIN
+epoch = 500 # 10
+batch_size =  32 # 4
+optimizer = 'Adam'    #['SGD','Adam']
+learning_rate = 1e-5
+weight_decay = 1e-4
+momentum = 0.9
+scheduler = 'cos'     #['multi', 'cos']
+
+# steps = [50,75]
+gamma = 0.1
+warmup = 'linear'
+warmup_iters = 100
+
+# NETWORK
+backbone = '18'
+griding_num = 100
+use_aux = False
+
+# LOSS
+sim_loss_w = 1.0
+shp_loss_w = 0.0
+
+# EXP
+note = '_ufld_2lanes_res18'
+
+log_path = './log'
+
+# FINETUNE or RESUME MODEL PATH
+finetune = None
+resume = None
+
+# TESTNone
+test_model = './model/lane_m599_all.pth'
+test_work_dir = './tmp'
+
+num_lanes = 2

algorithms/lane_ufld/code/UFLD/configs/tusimple_res18_nbg3.py

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+# DATA
+dataset = 'Tusimple'
+data_root = '/mnt/HDisk2T/liuxy51/ganxian/train_2024_03_06_2'
+
+# TRAIN
+epoch = 500 # 10
+batch_size =  32 # 4
+optimizer = 'Adam'    #['SGD','Adam']
+learning_rate = 1e-5
+weight_decay = 1e-4
+momentum = 0.9
+scheduler = 'cos'     #['multi', 'cos']
+
+# steps = [50,75]
+gamma = 0.1
+warmup = 'linear'
+warmup_iters = 100
+
+# NETWORK
+backbone = '18'
+griding_num = 100
+use_aux = False
+
+# LOSS
+sim_loss_w = 1.0
+shp_loss_w = 0.0
+
+# EXP
+note = '_ufld_2lanes_res18'
+
+log_path = './log'
+
+# FINETUNE or RESUME MODEL PATH
+finetune = None
+resume = None
+
+# TESTNone
+test_model = './model/lane_m599_all.pth'
+test_work_dir = './tmp'
+
+num_lanes = 2

algorithms/lane_ufld/code/UFLD/configs/tusimple_res34.py

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+# DATA
+dataset = 'Tusimple'
+# data_root = 'C:\\data\\Tusimple\\test_set'
+# data_root = 'C:\\data\\anno\\324lane'
+# data_root = 'C:\\data\\Tusimple\\train_set'
+data_root = '/data/panh28/yk_syj/data/train_0306'
+# TRAIN
+epoch = 600
+batch_size = 64
+optimizer = 'Adam'    #['SGD','Adam']
+# learning_rate = 0.1
+learning_rate = 1e-5
+weight_decay = 1e-4
+momentum = 0.9
+
+scheduler = 'cos'     #['multi', 'cos']
+# steps = [50,75]
+gamma = 0.1
+warmup = 'linear'
+warmup_iters = 100
+
+# NETWORK
+backbone = '34'
+griding_num = 100
+use_aux = False
+
+# LOSS
+sim_loss_w = 1.0
+shp_loss_w = 0.0
+
+# EXP
+note = 'lane_res34_2ch_syj_0906_minilearn'
+
+log_path = './log'
+
+# FINETUNE or RESUME MODEL PATH
+finetune = None
+resume = "/data/panh28/yk_syj/code/UFLD/log/20230906_161808_lr_1e-04_b_64lane_res34_2ch_syj_0906/ep068.pth"
+# TESTNone
+test_model = './model/lane_m599_all.pth'
+# test_model = './model/tusimple_18.pth'
+test_work_dir = './tmp'
+
+num_lanes = 2

algorithms/lane_ufld/code/UFLD/configs/tusimple_vov19slim_4lane_v1.py

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+# UFLD + VoVNet-19-slim-eSE backbone (train from scratch; no torchvision weights).
+
+from configs.tusimple_res18_4lane_v1 import *
+
+backbone = 'vov19slim'
+note = '_ufld_4lanes_vov19slim'