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metadata
license: mit
tags:
  - autonomous-driving
  - carla
  - yolo
  - lane-detection
  - ufld
  - pytorch
  - computer-vision
  - self-driving-car
datasets:
  - custom
language:
  - en

Autonomous Driving CARLA Models

Pre-trained models for autonomous driving in the CARLA 0.9.15 simulator.

Models

Model File Size Description
YOLO11n yolo_carla_best.pt ~6MB Object detection (vehicles, pedestrians, traffic lights, speed signs)
UFLD ufld_carla_best.pth ~735MB Lane detection fine-tuned on CARLA
TuSimple tusimple_18.pth ~245MB Pre-trained lane detection backbone

YOLO11n Object Detection

Custom-trained on CARLA simulator data.

Classes:

  • 0: Vehicle
  • 1: Pedestrian
  • 2: Traffic Light
  • 3: Speed Limit Sign

Training:

  • Epochs: 250
  • Batch Size: 128
  • Optimizer: AdamW
  • Learning Rate: 0.005
  • Image Size: 640×640

YOLO Results Confusion Matrix

UFLD Lane Detection

Fine-tuned Ultra Fast Lane Detection model.

Architecture:

  • Backbone: ResNet-18
  • Grid: 100×56
  • Input: 800×288

Training:

  • Epochs: 50
  • Batch Size: 32
  • Optimizer: Adam
  • Learning Rate: 1e-4
  • Loss: SoftmaxFocalLoss + ParsingRelationLoss

UFLD Loss Curve

Usage

from huggingface_hub import hf_hub_download

# Download YOLO model
yolo_path = hf_hub_download(
    repo_id="jkdxbns/autonomous-driving-carla",
    filename="yolo_carla_best.pt"
)

# Download UFLD model
ufld_path = hf_hub_download(
    repo_id="jkdxbns/autonomous-driving-carla",
    filename="ufld_carla_best.pth"
)

GitHub Repository

Full source code: github.com/jkdxbns/autonomous-driving-carla

Author

Justin Mascarenhas
CMPE 789 - Robot Perception
Rochester Institute of Technology

License

MIT License