Cooperative Perception for Connected Vehicles

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Cooperative Perception for Connected Vehicles

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Overview
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Detail Description

1. Abstract

Cooperative Perception for Connected Vehicles is an intelligent transportation project designed to improve road awareness by sharing perception information between connected vehicles and roadside infrastructure. Individual vehicles use cameras, sensors, or simulated perception data to identify nearby vehicles, pedestrians, obstacles, and road conditions. Through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, this information can be shared to help other vehicles understand objects that may be outside their direct sensor view. The system combines local and shared perception data to create a broader representation of the surrounding environment. A dashboard visualizes detected objects, vehicle positions, and communication status. The project supports research into connected mobility, cooperative driving, and intelligent transportation systems.

2. Objectives

To develop a cooperative perception framework for connected vehicles.
To share detected object information between vehicles and roadside infrastructure.
To simulate V2V and V2I communication.
To combine local perception with information received from nearby vehicles.
To improve awareness of objects outside a vehicle's direct field of view.
To monitor communication latency and shared perception data.
To visualize cooperative perception results through an interactive dashboard.

3. Existing System

Traditional vehicle perception systems mainly depend on onboard cameras, radar, LiDAR, and other sensors. These sensors provide information about the vehicle's immediate surroundings, but their field of view and detection range are limited. Obstacles may also be hidden by other vehicles, buildings, or road features.

Disadvantages:

Limited visibility beyond the onboard sensors' detection range.
Difficulty identifying objects hidden behind other vehicles.
Perception quality may decrease in poor weather or difficult lighting.
Limited sharing of environmental information between independent vehicles.
Communication delays can affect the usefulness of shared information.

4. Proposed System

The proposed system combines local vehicle perception with shared information from connected vehicles or roadside units. Each simulated vehicle detects or receives information about surrounding objects and transmits selected perception data to nearby participants. A fusion module combines local and received information using object positions, timestamps, and confidence values. The system displays the combined environmental view and evaluates how communication quality affects cooperative perception.

Key Features:

Local object detection using camera images or simulated data.
V2V and V2I perception information sharing.
Cooperative object detection and data fusion.
Visualization of vehicles, pedestrians, and obstacles.
Monitoring of message latency and packet delivery.
Detection of objects outside the local sensor view when shared data is available.
Dashboard for vehicle status and perception results.
Simulation of communication delays and packet loss.

5. Implementation Procedure

Requirement Analysis: Define the number of connected vehicles, object categories, communication range, and perception information to be shared.
Simulation Environment Setup: Create a road network and vehicle movement scenarios using a simulator such as SUMO.
Perception Data Preparation: Use a suitable public dataset, simulated object detections, or camera images to identify vehicles, pedestrians, and obstacles.
Local Object Detection: Apply a model such as YOLO to camera images or use pre-generated detections from the simulation.
Vehicle Communication: Develop a communication module that exchanges object positions, timestamps, object classes, and confidence scores between vehicles.
Coordinate Alignment: Convert local object positions into a common coordinate system using simulated vehicle poses and appropriate transformations.
Data Fusion: Combine local and received detections, accounting for duplicate objects, timestamps, confidence, and possible communication delays.
Cooperative Perception Display: Create a shared environmental view showing objects detected by each vehicle and objects contributed by other vehicles.
Dashboard Development: Display vehicle positions, detected objects, communication status, and latency statistics.
Testing and Evaluation: Compare local-only perception with cooperative perception using detection coverage, object-level precision and recall, communication latency, and performance under packet loss.

6. Software Requirements

Component	Recommended Technology
Programming Language	Python
Computer Vision	OpenCV
Object Detection	YOLO
Deep Learning	PyTorch
Traffic Simulation	SUMO
Simulation Interface	TraCI
Communication Simulation	OMNeT++ or ns-3 (optional)
Data Processing	NumPy, Pandas
Visualization	Plotly, Matplotlib
Dashboard	Streamlit or Flask
Development Environment	VS Code or Jupyter Notebook

7. Hardware Requirements

Component	Recommended Specification
Processor	Intel Core i5 or equivalent
RAM	8 GB minimum; 16 GB recommended
Storage	256 GB SSD or higher
GPU	Optional; recommended for real-time object detection
Camera	Webcam or recorded road video (optional)
Network	Local network or internet for communication testing
Edge Device	NVIDIA Jetson or Raspberry Pi (optional)
Display	Monitor for the cooperative perception dashboard
Operating System	Windows or Linux

Note: A student prototype can simulate multiple connected vehicles on one computer. Physical vehicles, cameras, and roadside communication units are not required unless real-world testing is planned.

8. Advantages of the Project

Combines perception information from multiple connected vehicles.
Can improve awareness of objects hidden from an individual vehicle's sensors.
Supports cooperative detection of vehicles, pedestrians, and obstacles.
Enables testing of V2V and V2I information exchange.
Helps evaluate the effects of communication delays and packet loss.
Provides a visual representation of the shared driving environment.
Supports research into connected and autonomous vehicles.
Can be extended with sensor fusion, edge computing, and AI-based risk prediction.

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