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Add YOLOv9 as a proper Git submodule
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‎.gitmodules‎

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[submodule "yolov9"]
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path = yolov9
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url = https://github.com/WongKinYiu/yolov9.git

‎adaptive_carla_rl.egg-info/PKG-INFO‎

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Metadata-Version: 2.4
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Name: adaptive-carla-rl
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Version: 0.1
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Requires-Dist: torch
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Requires-Dist: torchvision
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Requires-Dist: torchaudio
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Requires-Dist: numpy
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Requires-Dist: opencv-python
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Requires-Dist: gymnasium
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Requires-Dist: stable-baselines3
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Requires-Dist: transformers
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Requires-Dist: pyyaml
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Requires-Dist: matplotlib
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Requires-Dist: pandas
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Requires-Dist: tensorboard
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Requires-Dist: scipy
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Requires-Dist: tqdm
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Requires-Dist: pillow
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Requires-Dist: requests
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Requires-Dist: cloudpickle
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Requires-Dist: typing-extensions
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Requires-Dist: jupyter
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Requires-Dist: ipython
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Requires-Dist: pytest
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Requires-Dist: black
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Requires-Dist: flake8
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Requires-Dist: seaborn
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Dynamic: requires-dist

‎adaptive_carla_rl.egg-info/SOURCES.txt‎

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setup.py
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adaptive_carla_rl.egg-info/PKG-INFO
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adaptive_carla_rl.egg-info/SOURCES.txt
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adaptive_carla_rl.egg-info/dependency_links.txt
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adaptive_carla_rl.egg-info/requires.txt
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adaptive_carla_rl.egg-info/top_level.txt
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core/__init__.py
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core/adaptive_roi.py
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core/hybrid_detector.py
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core/marl_env.py
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core/multi_detector.py
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core/nas_controller.py
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core/ssd_detector.py
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core/vit_encoder.py
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training/__init__.py
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training/callbacks.py
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training/curriculum.py
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training/meta_trainer.py

‎adaptive_carla_rl.egg-info/dependency_links.txt‎

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‎adaptive_carla_rl.egg-info/requires.txt‎

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torch
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torchvision
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torchaudio
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numpy
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opencv-python
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gymnasium
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stable-baselines3
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transformers
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pyyaml
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matplotlib
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pandas
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tensorboard
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scipy
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tqdm
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pillow
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requests
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cloudpickle
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typing-extensions
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jupyter
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ipython
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pytest
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black
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flake8
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seaborn

‎adaptive_carla_rl.egg-info/top_level.txt‎

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core
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training

‎assets/carla.png‎

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‎configs/defaults.yaml‎

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# Default config for the RL agent in the autonomous driving environment
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training:
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total_timesteps: 1000000
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batch_size: 64
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n_steps: 2048
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gamma: 0.99
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learning_rate: 0.0003
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ent_coef: 0.01
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clip_range: 0.2
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n_epochs: 10
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gae_lambda: 0.95
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environment:
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frame_skip: 1
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max_episode_steps: 1000
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obs_stack: 3
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reward_config:
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detection_reward: 0.1
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fps_reward: 0.05
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collision_penalty: -1.0
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success_reward: 1.0
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detector:
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ssd:
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conf_thresh: 0.5
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model: "ssd300_vgg16"
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weights: "DEFAULT"
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yolo:
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conf_thresh: 0.5
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iou_thresh: 0.45
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weights: "yolov9-c.pt"
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img_size: 640
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roi:
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min_size: 0.2
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max_size: 0.8
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overlap_thresh: 0.5

‎configs/highway.yaml‎

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# Highway driving scenario configuration
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environment:
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scenario: "highway"
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town: "Town07"
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weather:
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- "ClearNoon"
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- "ClearSunset"
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- "WetNoon"
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- "SoftRainNoon"
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# Traffic parameters
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num_vehicles: 80
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num_pedestrians: 0
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vehicle_types:
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- "vehicle.audi.a2"
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- "vehicle.audi.etron"
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- "vehicle.audi.tt"
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- "vehicle.bmw.grandtourer"
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- "vehicle.chevrolet.impala"
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- "vehicle.dodge.charger_police"
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- "vehicle.ford.mustang"
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- "vehicle.jeep.wrangler_rubicon"
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- "vehicle.lincoln.mkz2017"
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- "vehicle.mercedes.coupe"
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- "vehicle.mini.cooperst"
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- "vehicle.nissan.micra"
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- "vehicle.nissan.patrol"
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- "vehicle.seat.leon"
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- "vehicle.tesla.cybertruck"
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- "vehicle.tesla.model3"
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- "vehicle.toyota.prius"
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- "vehicle.volkswagen.t2"
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# Road characteristics
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junction_density: 0.1
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lane_width: 3.5
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speed_limit: 120 # km/h
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# Object detection challenges
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occlusion_probability: 0.1
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small_object_scale: 0.3
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high_speed_objects: true
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# Reward configuration
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reward_config:
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detection_reward: 1.2
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fps_reward: 0.7
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collision_penalty: -20.0
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pedestrian_detection_bonus: 0.0
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traffic_light_penalty: 0.0
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lane_invasion_penalty: -5.0
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success_reward: 30.0
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time_penalty: -0.05
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high_speed_detection_bonus: 1.5
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# Episode configuration
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max_episode_steps: 3000
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success_conditions:
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distance_traveled: 5000
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time_without_collision: 300
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min_detection_accuracy: 0.65
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# Perception parameters
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perception:
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# Detection thresholds
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min_pedestrian_confidence: 0.0
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min_vehicle_confidence: 0.3
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min_traffic_light_confidence: 0.0
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# Region of interest defaults
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roi:
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default: [0.1, 0.3, 0.9, 0.7]
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attention_scale: 2.0
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# Detector switching thresholds
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switch_to_yolo_when:
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fps_below: 20
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vehicles_above: 15
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switch_to_detr_when:
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accuracy_below: 0.6
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small_objects_above: 1
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# Computational constraints
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computational:
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max_inference_time: 30
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target_fps: 20
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energy_budget: 8000

‎configs/urban.yaml‎

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# Urban driving scenario configuration
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environment:
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scenario: "urban"
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town: "Town10HD"
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weather:
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- "ClearNoon"
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- "WetNoon"
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- "HardRainNoon"
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- "ClearSunset"
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# Traffic parameters
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num_vehicles: 50
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num_pedestrians: 100
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vehicle_types:
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- "vehicle.audi.tt"
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- "vehicle.bmw.grandtourer"
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- "vehicle.citroen.c3"
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- "vehicle.dodge.charger_police"
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- "vehicle.ford.mustang"
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- "vehicle.mercedes.coupe"
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- "vehicle.mini.cooperst"
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- "vehicle.nissan.micra"
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- "vehicle.nissan.patrol"
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- "vehicle.seat.leon"
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- "vehicle.tesla.model3"
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- "vehicle.toyota.prius"
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- "vehicle.volkswagen.t2"
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pedestrian_types:
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- "walker.pedestrian.0001"
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- "walker.pedestrian.0002"
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- "walker.pedestrian.0003"
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- "walker.pedestrian.0004"
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- "walker.pedestrian.0005"
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- "walker.pedestrian.0006"
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- "walker.pedestrian.0007"
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- "walker.pedestrian.0008"
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- "walker.pedestrian.0009"
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- "walker.pedestrian.0010"
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# Road complexity
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junction_density: 0.8 # High density of intersections
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lane_width: 3.0 # Narrower lanes
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speed_limit: 50 # km/h
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# Object detection challenges
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occlusion_probability: 0.3
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small_object_scale: 0.7
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# Reward configuration
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reward_config:
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detection_reward: 1.0
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fps_reward: 0.3
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collision_penalty: -15.0
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pedestrian_detection_bonus: 2.0
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traffic_light_penalty: -5.0
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lane_invasion_penalty: -3.0
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success_reward: 20.0
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time_penalty: -0.1
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# Episode configuration
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max_episode_steps: 2000 # ~5 minutes at 10fps
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success_conditions:
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distance_traveled: 1000 # meters
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time_without_collision: 180 # seconds
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min_detection_accuracy: 0.75 # mAP
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# Perception parameters
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perception:
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# Detection thresholds
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min_pedestrian_confidence: 0.5
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min_vehicle_confidence: 0.4
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min_traffic_light_confidence: 0.6
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# Region of interest defaults
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roi:
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default: [0.2, 0.2, 0.8, 0.8] # [left, top, right, bottom]
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attention_scale: 1.5
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# Detector switching thresholds
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switch_to_yolo_when:
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fps_below: 15
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vehicles_above: 10
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pedestrians_above: 5
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switch_to_detr_when:
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accuracy_below: 0.65
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small_objects_above: 3
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# Computational constraints
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computational:
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max_inference_time: 50 # ms
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target_fps: 15
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energy_budget: 5000 # mJ per episode

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