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/pytorch-auto-drive-master/configs/statics.py

@@ -0,0 +1,46 @@
+# Define a series of shortcuts for commandline use of main_*.py
+# a_b equals --a-b in commandline
+
+SHORTCUTS = dict(
+    exp_name=dict(keys=['train.exp_name', 'test.exp_name'], type=str,
+                  help='Name of experiment'),
+    checkpoint=dict(keys=['train.checkpoint', 'test.checkpoint'], type=str,
+                    help='Continue/Load from a previous checkpoint'),
+    device=dict(keys=['train.device', 'test.device'], type=str,
+                help='CPU is not recommended!'),
+    workers=dict(keys=['train.workers', 'test.workers'], type=int,
+                 help='Number of workers (threads) when loading data.'
+                      'Recommend value for training=~ batch size'),
+    batch_size=dict(keys=['train.batch_size', 'test.batch_size'], type=int,
+                    help='input batch size. Recommend 4 times the training batch size in testing'),
+    save_dir=dict(keys=['train.save_dir', 'test.save_dir'], type=str,
+                  help='Path prefix to save all files excluding tensorboard log.'),
+
+    val_num_steps=dict(keys=['train.val_num_steps'], type=int,
+                       help='Validation frequency'),
+    world_size=dict(keys=['train.world_size'], type=int,
+                    help='Number of distributed processes'),
+    dist_url=dict(keys=['train.dist_url'], type=str,
+                  help='url used to set up distributed training'),
+
+    thresh=dict(keys=['test.thresh'], type=float,
+                help='Threshold for detection tasks.'),
+
+    lr=dict(keys=['optimizer.lr'], type=float,
+            help='Learning rate'),
+    weight_decay=dict(keys=['optimizer.weight_decay'], type=float,
+                      help='Weight decay'),
+
+    warmup_steps=dict(keys=['lr_scheduler.warmup_steps'], type=int,
+                      help='Learning rate warmup steps.'),
+    epochs=dict(keys=['lr_scheduler.epochs', 'train.num_epochs'], type=int,
+                help='Number of epochs')
+)
+
+DEPRECATION_MAP = dict(
+    continue_from=dict(valid='checkpoint', message=''),
+    do_not_save=dict(valid=None, message='Please delete the .pt files yourself!'),
+    method=dict(valid=None, message='Please use the config files to define models!'),
+    model=dict(valid=None, message='Please use the config files to define models!'),
+    backbone=dict(valid=None, message='Please use the config files to define models!')
+)