Why this course?

Description

comprehensive outline for internship and project-based learning in the field of Autonomous Vehicle (AV) Design. With a focus on practical implementation through MATLAB Simulink and sensor modeling, this final segment allows students to apply their knowledge in realistic and industry-relevant contexts.

Objectives

• Design and simulate autonomous driving scenarios using MATLAB Simulink.
• Integrate sensors such as LIDAR, radar, and cameras for vehicle perception.
• Model and implement motion planning, object detection, and vehicle control algorithms.
• Build a professional internship report highlighting their technical skills and findings.

Capstone Projects

Below are suggested projects that integrate core concepts covered in the AV curriculum.

Project 1: Multi-Sensor Fusion and Object Tracking

Objective: Fuse multiple sensor inputs (camera, radar, LIDAR) to track multiple objects in a dynamic driving scenario.

Topics Covered:

• Sensor coverage modeling
• Detection and tracking algorithms
• Kalman filtering
• Ground truth labeling and validation

Parking Assistance System

Objective: Simulate and validate an autonomous parking system using the Driving Scenario Designer and vehicle control blocks.

Applications Include:

• Reverse motion modeling
• Vehicle maneuvering logic
• Obstacle avoidance

Urban Motion Planning using Occupancy Grid Maps

Objective: Implement and test motion planning algorithms for urban driving using dynamic occupancy grid maps.

Key Focus Areas:

• Lane change and overtaking logic
• Path generation
• Obstacle handling

AV Scenario Generation from Recorded Data

Objective: Use recorded traffic data to generate realistic AV driving scenarios.

Methodology:

• Data import and signal labeling
• Actor and trajectory modeling
• Scenario validation and visualization

Final Internship Report Structure

Students are expected to compile their internship experience into a formal report, demonstrating their technical acumen and application skills.

Report Format

• Title Page
• Project title
• Student name and ID
• Institutional and internship details

Abstract

• Summary of project scope, tools, and findings

Introduction

• Context for autonomous vehicle technologies
• Problem definition and relevance

Literature Review

• Overview of AV design methods
• Existing work in motion planning, sensor fusion, etc.

Methodology

• Tools and technologies used: MATLAB, Simulink, Sensor blocks
• Scenario setup and sensor integration
• Algorithms used for detection, tracking, or planning

Simulation and Results

• Scenario snapshots
• Output plots and metrics
• Performance analysis

Discussion

• Insights from simulations
• Limitations encountered
• Possible improvements

Conclusion

• Project achievements and learning outcomes
• Scope for future enhancement

References

• Articles, MATLAB documentation, research papers

Appendices

• Code snippets
• Block diagrams and setup configurations
• Ground truth data samples