| --- |
| license: apache-2.0 |
| library_name: pytorch |
| pipeline_tag: object-detection |
| tags: |
| - autonomous-driving |
| - adas |
| - cipo |
| - yolo |
| - onnx |
| - autoware |
| --- |
| |
| # AutoSpeed - In-path objects detection |
|
|
| <video src="https://huggingface.co/AutowareFoundation/auto_speed/resolve/main/assets/auto_speed_demo_1.mp4" |
| poster="https://huggingface.co/AutowareFoundation/auto_speed/resolve/main/assets/thumbnail.jpg" |
| controls muted loop playsinline width="100%"></video> |
|
|
| For autonomous cruise control applications, it is crucial to maintain a safe following distance from the vehicle in front, known as the closest-in-path object. Detecting the closest-in-path object therefore becomes a very important task for any self-driving or driver assitance application, as it also supports important safety features such as forward collision warning and autonomous emergency braking. The AutoSpeed network is a bounding box detection model inspired by the YOLOv11 architecture, in which the backbone c3k2 blocks are substitued by a custom designed 'context' block for improved overall scene understanding. |
|
|
| The AutoSpeed model detects all foreground objects and classifies objects into three categories depending on the object's position with respect to the predicted future driving path of the ego-car: |
|
|
| - objects directly within the future driving path of the ego-car |
| - objects cutting-in/cutting-out of the future driving path of the ego-car |
| - objects outside of the future driving path of the ego-car |
|
|
| ### Performance Results |
|
|
| The AutoSpeed 2.0 network is trained on the [OpenLane](https://github.com/OpenDriveLab/OpenLane) dataset on the CIPO detection task. We modify the ground-truth labels by merging 'Level 3' and 'Level 4' category objects in the ground truth dataset into a single class. We use a 90:10 ratio for train:val split. The evaluation metrics are summarized in the table below: |
|
|
| | | mAP@50 | mAP | |
| |------|--------|------| |
| | FP32 | 0.74 | 0.56 | |
| | INT8 | 0.73 | 0.54 | |
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|
|
| ## Model variants |
| The AutoSpeed network is trained in two variants, the original AutoSpeed 1.0 network processes frames in square aspect ratio with size 640px by 640px. AutoSpeed 2.0 processes frames in a 2:1 aspect ratio with size 1024 px by 512px, allowing for objects to be detected further away and a wider viewing angle of the scene which is more suited to autonomous driving applications. We recommend you work with AutoSpeed 2.0 for new applications and developments. |
|
|
| | | Input frame | PyTorch | ONNX FP32 | ONNX INT8 | |
| |---------------|-----------------------------|-----------------------------------------|-----------------------------------------------|-----------------------------------------------| |
| | AutoSpeed 2.0 | 1024x512 (2:1) | [model weights](models/autospeed_2.pth) | [model weights](models/autospeed_2_fp32.onnx) | [model weights](models/autospeed_2_int8.onnx) | |
| | AutoSpeed 1.0 | 640x640 (1:1) | [model weights](models/autospeed_1.pth) | [model weights](models/autospeed_1_fp32.onnx) | | |
|
|
| ## Quick start |
|
|
| ``` |
| from PIL import Image |
| from huggingface_hub import hf_hub_download |
| from Models.inference.auto_speed_infer import AutoSpeedNetworkInfer |
| |
| repo_id = "AutowareFoundation/auto_speed" |
| ckpt = hf_hub_download(repo_id, "models/autospeed_2.pth") |
| model = AutoSpeedNetworkInfer(ckpt) # uses CUDA if available |
| TEST_IMG = hf_hub_download(repo_id, "assets/highway.jpg") |
| |
| img = Image.open(TEST_IMG).convert("RGB") |
| detections = model.inference(img) |
| print(detections) |
| ``` |
|
|
| <video src="https://huggingface.co/AutowareFoundation/auto_speed/resolve/main/assets/auto_speed_demo_2.mp4" |
| controls muted loop playsinline width="100%"></video> |
|
|
| ## Citation |
|
|
| ```bibtex |
| @software{autoware_auto_speed_2026, |
| author = {The Autoware Foundation}, |
| title = {AutoSpeed: In-path object detection model}, |
| year = {2026}, |
| version = {2.0}, |
| url = {https://github.com/autowarefoundation/auto_speed}, |
| note = {Checkpoints: https://huggingface.co/AutowareFoundation/auto_speed} |
| } |
| |
