Sensor-Fusion-Based Autonomous Navigation

-20%

Sensor-Fusion-Based Autonomous Navigation

0 Orders 0 Wish listed

₹4,999.00

Qty

Total price:

₹4,999.00

Overview
Reviews

Detail Description

1. Abstract

Cooperative Perception for Connected Vehicles is an AI-based intelligent transportation project designed to improve road safety and environmental awareness by sharing perception information between connected vehicles and roadside infrastructure. The system uses computer vision, object detection, and vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication to exchange information about nearby vehicles, pedestrians, obstacles, and road conditions. By combining local sensor information with data received from other vehicles, the system can help identify objects that may be hidden from an individual vehicle's sensors. A dashboard displays detected objects, vehicle positions, communication status, and perception results. The project supports research into connected autonomous vehicles, cooperative driving, and smart transportation systems.

2. Objectives

To develop a cooperative perception system for connected vehicles.
To detect vehicles, pedestrians, and obstacles using AI and computer vision.
To exchange perception information through simulated V2V and V2I communication.
To combine local detections with information received from nearby vehicles.
To improve awareness of objects outside a vehicle's direct field of view.
To analyze communication latency and packet delivery.
To visualize shared perception information through an interactive dashboard.

3. Existing System

Traditional autonomous vehicles rely mainly on onboard cameras, radar, LiDAR, and other sensors to understand their surroundings. Although these sensors provide useful local information, their visibility can be limited by obstacles, road geometry, weather, and sensor range. Independent vehicles may not share their environmental observations with one another.

Disadvantages:

Limited awareness of objects outside the onboard sensor range.
Difficulty detecting vehicles or pedestrians hidden by obstacles.
Reduced visibility in certain weather and lighting conditions.
Limited collaboration between independent vehicles.
Communication delays may affect the usefulness of shared data.

4. Proposed System

The proposed system integrates local object detection with cooperative information sharing between connected vehicles. Each vehicle detects nearby objects using camera images or simulated sensor data and shares selected information with other vehicles or roadside infrastructure. A fusion module combines local and received detections using object positions, timestamps, and confidence scores. The system visualizes the shared environment and evaluates how communication conditions affect perception quality.

Key Features:

AI-based vehicle, pedestrian, and obstacle detection.
Simulated V2V and V2I communication.
Cooperative object detection and data fusion.
Shared environmental visualization.
Object position and confidence tracking.
Communication latency and packet-loss monitoring.
Dashboard for vehicle and perception status.
Support for simulation-based cooperative driving experiments.

5. Implementation Procedure

Data Collection: Obtain road images, videos, or suitable autonomous-driving datasets containing vehicles, pedestrians, and other road objects.
Simulation Setup: Create a road network with multiple connected vehicles using SUMO or another suitable simulator.
Object Detection: Use a pretrained YOLO model or another object detector to identify vehicles, pedestrians, and obstacles in images or video.
Local Perception: Store each vehicle's detected objects, positions, object classes, and confidence scores.
Communication Module: Simulate V2V and V2I messages that share selected perception information between vehicles and roadside infrastructure.
Coordinate Alignment: Transform object locations into a common coordinate system using vehicle positions and orientations.
Data Fusion: Combine local and shared detections while accounting for duplicate objects, timestamps, confidence, and communication delays.
Shared Environment Visualization: Display the combined detections on a map or virtual road scene, distinguishing locally detected objects from shared detections.
Dashboard Development: Create a dashboard showing vehicle locations, detected objects, network status, and communication metrics.
Testing and Evaluation: Compare local-only perception with cooperative perception using detection coverage, precision, recall, latency, and performance under simulated 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
Network Simulation (Optional)	OMNeT++ or ns-3
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 faster 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 dashboard
Operating System	Windows or Linux

Note: Multiple connected vehicles can be simulated on one computer for a student project. Physical vehicles, sensors, and roadside communication units are optional.

8. Advantages of the Project

Combines environmental information from multiple connected vehicles.
Can improve awareness of objects hidden from individual sensors.
Supports cooperative detection of vehicles, pedestrians, and obstacles.
Demonstrates V2V and V2I information exchange.
Helps analyze the effects of communication delays and packet loss.
Provides visual monitoring of shared road conditions.
Supports connected and autonomous vehicle research.
Can be extended with sensor fusion, edge computing, and AI-based risk prediction.

No review given yet!

Fast Delivery all across the country

Safe Payment

7 Days Return Policy

100% Authentic Products

Shopping cart