AI-based Car Collision Detection and Autonomous Emergency Response System: Integration of Real-time Computer Vision and Machine Learning

This project proposes a real-time vehicle collision detection system that combines OpenCV computer vision and random forest machine learning algorithms. It can achieve collision risk identification and automated emergency response using only a camera. The system features low cost, high efficiency, and a modular architecture, making it suitable for scenarios such as autonomous driving, Advanced Driver Assistance Systems (ADAS), and fleet management, providing a practical software solution for vehicle safety.

With the development of autonomous driving technology, the importance of vehicle safety systems has become increasingly prominent. Traditional collision detection relies on hardware such as radar and lidar, which are costly and complex to install. This project adopts a software solution combining computer vision and machine learning, enabling real-time detection with only a camera and lowering the deployment threshold. Applicable scenarios include autonomous vehicles, ADAS, fleet management, driving training simulation, etc., with good practicality and deployability.

Core Component Design

The system consists of four modules:

1. Feature Extraction Module: Extracts 7 features from video frames, including edge density, number of contours, maximum object area, object aspect ratio, histogram mean/standard deviation, and optical flow magnitude.
2. Collision Risk Detection Module: Inputs features into a pre-trained random forest classifier to generate risk probability; a value exceeding the 0.7 threshold is判定 as a risk.
3. Alarm System: Generates timestamped alerts, triggers multi-threaded emergency responses, and supports SMTP email notifications.
4. Real-time Processing Module: Supports camera, video file, and network stream inputs; a multi-threaded architecture ensures detection does not block.

Machine Learning Model

The random forest algorithm is selected for its advantages: ensemble learning reduces overfitting, supports feature importance analysis, outputs risk probability, enables efficient CPU inference, and has an interpretable decision process. Data preprocessing uses StandardScaler for normalization, and the model is serialized in pickle format.

OpenCV Visual Processing

• Canny edge detection (thresholds 100/200) for contour extraction;
• findContours to identify object contours;
• calcOpticalFlowPyrLK to calculate optical flow for motion estimation;
• Histogram analysis to adapt to lighting changes.

Multi-threaded Architecture

• Main thread: Video capture and feature extraction;
• Detection thread: Model inference and risk judgment;
• Alarm thread: Emergency response and notification sending.

Configuration Parameters

Parameter	Default Value	Description
alert_threshold	0.7	Risk threshold for triggering alerts
n_estimators	100	Number of decision trees in the random forest
random_state	42	Random seed to ensure reproducibility
Canny threshold	(100,200)	Upper and lower thresholds for edge detection

Deployment Methods

• Real-time camera detection: collision_detector.run_real_time_detection(0)
• Video file analysis: collision_detector.run_real_time_detection("path/to/video.mp4")
• Network camera: collision_detector.run_real_time_detection("http://camera-ip:port/stream")
• Custom training: Train and save the model via the train_model method.

Current Limitations

1. Performance depends on the quality and diversity of training data;
2. Real-time processing requires certain CPU/GPU resources;
3. Monocular vision cannot directly obtain depth information, leading to errors in distance estimation;
4. False positives may occur in complex scenarios.

Improvement Directions

1. Replace random forest with lightweight CNNs (e.g., MobileNet);
2. Introduce binocular/multi-view vision to enhance depth perception;
3. Add LSTM/Transformer to handle video temporal information;
4. Optimize model quantization and acceleration for embedded devices;
5. Integrate GPS, IMU, and other sensor data to improve robustness.

This project demonstrates a complete prototype of an AI-driven vehicle safety system, covering feature engineering to model deployment. Its core values include:

1. Low cost: Only a camera is needed to lower the ADAS deployment threshold;
2. Interpretability: Based on traditional machine learning, easy to understand and debug;
3. Modularity: Clear component division, easy to extend and maintain;
4. Practicality: Provides a complete training and deployment process.

For developers getting started with computer vision and machine learning applications in autonomous driving, this project is a good learning case. The code structure and documentation also provide a reference template for the development of similar systems.