Why this course?

Description

Overview

practical exercises and simulation-based projects to reinforce core concepts in autonomous vehicle (AV) design. Drawing on MATLAB and Simulink tools, learners implement scenario modeling, sensor integration, vehicle behavior simulation, and motion planning in urban environments.

Objectives

• Design and simulate real-world autonomous vehicle scenarios
• Utilize driving scenario designer tools for urban and highway models
• Implement and test AV sensor configurations, object detection, and multi-object tracking
• Generate scenario data from recorded or imported files
• Develop parking maneuvers and dynamic occupancy-based path planning

Simulation Projects

Project 1: Multi-Sensor Vehicle Simulation

Objective: Simulate vehicle sensor fusion using multiple AV sensor models.

Topics Covered:

• Sensor coverage analysis
• Sensor fusion for object tracking
• Ground truth labeling and data visualization

Project 2: Scenario-Based Motion Planning

Objective: Develop and simulate dynamic driving scenarios with traffic actors.

Tools Used:

• Driving Scenario Designer
• Actor programming and trajectory planning
• ASAM OpenDrive import for realistic layout

Project 3: Object Detection and Tracking

Objective: Implement detection algorithms on urban driving videos.

Features:

• GMM-based vehicle tracking
• Pedestrian tracking from moving vehicle
• Use of labeled data in real-time simulations

Project 4: AV State Logic and Control

Objective: Design vehicle behavior logic using Simulink Stateflow.

Focus Areas:

• Traffic rule decision modeling
• Reversing and turning maneuver logic
• Integration of MUX/DEMUX, bus creators, and counters

Final Internship Report Structure

Title Page

• Project title and topic
• Student name, program, institution

Abstract

• Summary of AV system design, tools, outcomes

Introduction

• Scope and relevance of AV systems
• Project objectives

Literature Review

• Existing tools, methodologies, and research

Methodology

• Scenario creation tools and simulation environment
• State logic development and actor dynamics

Simulation and Results

• Visual output of scenarios
• Path tracking, obstacle avoidance, pedestrian detection

Discussion

• Result interpretation
• Challenges and refinement areas

Conclusion

• Overall summary and potential applications

Appendices

• Screenshots, codes, scenarios used