Spaces:
Runtime error
Runtime error
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
@@ -1,10 +1,211 @@
|
|
1 |
---
|
2 |
-
title: Baseer
|
3 |
-
emoji:
|
4 |
colorFrom: blue
|
5 |
-
colorTo:
|
6 |
sdk: docker
|
7 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
8 |
---
|
9 |
|
10 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
---
|
2 |
+
title: Baseer Self-Driving API
|
3 |
+
emoji: π
|
4 |
colorFrom: blue
|
5 |
+
colorTo: red
|
6 |
sdk: docker
|
7 |
+
app_port: 7860
|
8 |
+
pinned: true
|
9 |
+
license: mit
|
10 |
+
short_description: A RESTful API for an InterFuser-based self-driving model.
|
11 |
+
tags:
|
12 |
+
- computer-vision
|
13 |
+
- autonomous-driving
|
14 |
+
- deep-learning
|
15 |
+
- fastapi
|
16 |
+
- pytorch
|
17 |
+
- interfuser
|
18 |
+
- graduation-project
|
19 |
+
- carla
|
20 |
+
- self-driving
|
21 |
---
|
22 |
|
23 |
+
# π Baseer Self-Driving API
|
24 |
+
|
25 |
+
| Service | Status |
|
26 |
+
|---|---|
|
27 |
+
| **API Status** | [](https://BaseerAI-baseer-server.hf.space) |
|
28 |
+
| **Model** | [](https://huggingface.co/BaseerAI/Interfuser-Baseer-v1) |
|
29 |
+
| **Frameworks** | [](https://fastapi.tiangolo.com/) [](https://pytorch.org/) |
|
30 |
+
|
31 |
+
## π Project Description
|
32 |
+
|
33 |
+
**Baseer** is an advanced self-driving system that provides a robust, real-time API for autonomous vehicle control. This Space hosts the FastAPI server that acts as an interface to the fine-tuned **[Interfuser-Baseer-v1](https://huggingface.co/BaseerAI/Interfuser-Baseer-v1)** model.
|
34 |
+
|
35 |
+
The system is designed to take a live camera feed and vehicle measurements, process them through the deep learning model, and return actionable control commands and a comprehensive scene analysis.
|
36 |
+
|
37 |
+
---
|
38 |
+
|
39 |
+
## ποΈ Architecture
|
40 |
+
|
41 |
+
This project follows a decoupled client-server architecture, where the model and the application are managed separately for better modularity and scalability.
|
42 |
+
|
43 |
+
```
|
44 |
+
+-----------+ +------------------------+ +--------------------------+
|
45 |
+
| | | | | |
|
46 |
+
| Client | -> | Baseer API (Space) | -> | Interfuser Model (Hub) |
|
47 |
+
|(e.g.CARLA)| | (FastAPI Server) | | (Private/Gated Weights) |
|
48 |
+
| | | | | |
|
49 |
+
+-----------+ +------------------------+ +--------------------------+
|
50 |
+
HTTP Loads Model Model Repository
|
51 |
+
Request
|
52 |
+
```
|
53 |
+
|
54 |
+
## β¨ Key Features
|
55 |
+
|
56 |
+
### π§ **Advanced Perception Engine**
|
57 |
+
- **Powered by:** The [Interfuser-Baseer-v1](https://huggingface.co/BaseerAI/Interfuser-Baseer-v1) model.
|
58 |
+
- **Focus:** High-accuracy traffic object detection and safe waypoint prediction.
|
59 |
+
- **Scene Analysis:** Real-time assessment of junctions, traffic lights, and stop signs.
|
60 |
+
|
61 |
+
### β‘ **High-Performance API**
|
62 |
+
- **Framework:** Built with **FastAPI** for high throughput and low latency.
|
63 |
+
- **Stateful Sessions:** Manages multiple, independent driving sessions, each with its own tracker and controller state.
|
64 |
+
- **RESTful Interface:** Intuitive and easy-to-use API design.
|
65 |
+
|
66 |
+
### π **Comprehensive Outputs**
|
67 |
+
- **Control Commands:** `steer`, `throttle`, `brake`.
|
68 |
+
- **Scene Analysis:** Probabilities for junctions, traffic lights, and stop signs.
|
69 |
+
- **Predicted Waypoints:** The model's intended path for the next 10 steps.
|
70 |
+
- **Visual Dashboard:** A generated image that provides a complete, human-readable overview of the current state.
|
71 |
+
|
72 |
+
---
|
73 |
+
|
74 |
+
## π How to Use
|
75 |
+
|
76 |
+
Interact with the API by making HTTP requests to its endpoints. The typical workflow is to start a session, run steps in a loop, and then end the session.
|
77 |
+
|
78 |
+
### 1. Start a New Session
|
79 |
+
This will initialize a new set of tracker and controller instances on the server.
|
80 |
+
|
81 |
+
**Request:**
|
82 |
+
```bash
|
83 |
+
curl -X POST "https://BaseerAI-baseer-server.hf.space/start_session"
|
84 |
+
```
|
85 |
+
|
86 |
+
**Example Response:**
|
87 |
+
```json
|
88 |
+
{
|
89 |
+
"session_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef"
|
90 |
+
}
|
91 |
+
```
|
92 |
+
|
93 |
+
### 2. Run a Simulation Step
|
94 |
+
|
95 |
+
Send the current camera view and vehicle measurements to be processed. The API will return control commands and a full analysis.
|
96 |
+
|
97 |
+
**Request:**
|
98 |
+
```bash
|
99 |
+
curl -X POST "https://BaseerAI-baseer-server.hf.space/run_step" \
|
100 |
+
-H "Content-Type: application/json" \
|
101 |
+
-d '{
|
102 |
+
"session_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
|
103 |
+
"image_b64": "your-base64-encoded-bgr-image-string",
|
104 |
+
"measurements": {
|
105 |
+
"pos_global": [105.0, -20.0],
|
106 |
+
"theta": 1.57,
|
107 |
+
"speed": 5.5,
|
108 |
+
"target_point": [10.0, 0.0]
|
109 |
+
}
|
110 |
+
}'
|
111 |
+
```
|
112 |
+
|
113 |
+
**Example Response:**
|
114 |
+
```json
|
115 |
+
{
|
116 |
+
"control_commands": {
|
117 |
+
"steer": 0.05,
|
118 |
+
"throttle": 0.6,
|
119 |
+
"brake": false
|
120 |
+
},
|
121 |
+
"scene_analysis": {
|
122 |
+
"is_junction": 0.02,
|
123 |
+
"traffic_light_state": 0.95,
|
124 |
+
"stop_sign": 0.01
|
125 |
+
},
|
126 |
+
"predicted_waypoints": [
|
127 |
+
[1.0, 0.05],
|
128 |
+
[2.0, 0.06],
|
129 |
+
[3.0, 0.07],
|
130 |
+
[4.0, 0.07],
|
131 |
+
[5.0, 0.08],
|
132 |
+
[6.0, 0.08],
|
133 |
+
[7.0, 0.09],
|
134 |
+
[8.0, 0.09],
|
135 |
+
[9.0, 0.10],
|
136 |
+
[10.0, 0.10]
|
137 |
+
],
|
138 |
+
"dashboard_b64": "a-very-long-base64-string-representing-the-dashboard-image...",
|
139 |
+
"reason": "Red Light"
|
140 |
+
}
|
141 |
+
```
|
142 |
+
|
143 |
+
**Response Fields:**
|
144 |
+
- **`control_commands`**: The final commands to be applied to the vehicle.
|
145 |
+
- **`scene_analysis`**: Probabilities for different road hazards. A high `traffic_light_state` value (e.g., > 0.5) indicates a red light.
|
146 |
+
- **`predicted_waypoints`**: The model's intended path, relative to the vehicle.
|
147 |
+
- **`dashboard_b64`**: A Base64-encoded JPEG image of the full dashboard view, which can be directly displayed in a client application.
|
148 |
+
- **`reason`**: A human-readable string explaining the primary reason for the control action (e.g., "Following ID 12", "Red Light", "Cruising").
|
149 |
+
|
150 |
+
### 3. End the Session
|
151 |
+
|
152 |
+
This will clean up the session data from the server.
|
153 |
+
|
154 |
+
**Request:**
|
155 |
+
```bash
|
156 |
+
curl -X POST "https://BaseerAI-baseer-server.hf.space/end_session?session_id=a1b2c3d4-e5f6-7890-1234-567890abcdef"
|
157 |
+
```
|
158 |
+
|
159 |
+
**Example Response:**
|
160 |
+
```json
|
161 |
+
{
|
162 |
+
"message": "Session a1b2c3d4-e5f6-7890-1234-567890abcdef ended."
|
163 |
+
}
|
164 |
+
```
|
165 |
+
|
166 |
+
---
|
167 |
+
|
168 |
+
## π‘ API Endpoints
|
169 |
+
|
170 |
+
| Endpoint | Method | Description |
|
171 |
+
|---|---|---|
|
172 |
+
| `/` | GET | Landing page with API status. |
|
173 |
+
| `/docs` | GET | Interactive API documentation (Swagger UI). |
|
174 |
+
| `/start_session` | POST | Initializes a new driving session. |
|
175 |
+
| `/run_step` | POST | Processes a single frame and returns control commands. |
|
176 |
+
| `/end_session` | POST | Terminates a specific session. |
|
177 |
+
| `/sessions` | GET | Lists all currently active sessions. |
|
178 |
+
|
179 |
+
---
|
180 |
+
|
181 |
+
## π― Intended Use Cases & Limitations
|
182 |
+
|
183 |
+
### β
Optimal Use Cases
|
184 |
+
- Simulating driving in CARLA environments.
|
185 |
+
- Research in end-to-end autonomous driving.
|
186 |
+
- Testing perception and control modules in a closed-loop system.
|
187 |
+
- Real-time object detection and trajectory planning.
|
188 |
+
|
189 |
+
### β οΈ Limitations
|
190 |
+
- **Simulation-Only:** Trained exclusively on CARLA data. Not suitable for real-world driving.
|
191 |
+
- **Vision-Based:** Relies on a single front-facing camera and has inherent blind spots.
|
192 |
+
- **No LiDAR:** Lacks the robustness of sensor fusion in adverse conditions.
|
193 |
+
|
194 |
+
---
|
195 |
+
|
196 |
+
## π οΈ Development
|
197 |
+
|
198 |
+
This project is part of a graduation thesis in Artificial Intelligence.
|
199 |
+
- **Deep Learning:** PyTorch
|
200 |
+
- **API Server:** FastAPI
|
201 |
+
- **Image Processing:** OpenCV
|
202 |
+
- **Scientific Computing:** NumPy
|
203 |
+
|
204 |
+
## π Contact
|
205 |
+
|
206 |
+
For inquiries or support, please use the **Community** tab in this Space or open an issue in the project's GitHub repository (if available).
|
207 |
+
|
208 |
+
---
|
209 |
+
|
210 |
+
**Developed by:** Adam Altawil
|
211 |
+
**License:** MIT
|