Trailer Angle Measurement: LIDAR, Radar, and IMU Comparison

Project Overview

This MATLAB simulation compares three sensor technologies (LIDAR, Radar, and IMU) for measuring the hitch angle between a truck and trailer. The project features:

• Real-time animation of truck-trailer dynamics
• Modular sensor simulation with mathematical foundations
• Performance comparison using RMSE metrics
• Visualization with live error plots

Block Diagram

graph TD
    A[Simulation Parameters] --> B[Truth Model]
    B --> C[IMU Sensor Model]
    B --> D[LIDAR Sensor Model]
    B --> E[Radar Sensor Model]
    C --> F[Visualization System]
    D --> F
    E --> F
    F --> G[Performance Analysis]

    style A fill:#f9f,stroke:#333
    style B fill:#bbf,stroke:#333
    style C fill:#f96,stroke:#333
    style D fill:#6f9,stroke:#333
    style E fill:#9cf,stroke:#333
    style F fill:#ff9,stroke:#333
    style G fill:#f99,stroke:#333

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How to Run the Simulation

1. Requirements:

   • MATLAB R2020a or newer

2. Execution Steps:

% 1. Load project to MATLAB workspace
% 2. Run "simulation.m" script (F5 or click Run)

1. Adjustable Parameters:

Simulation parameters can be adjusted from the "parameters.m" script.

Results Interpretation

The simulation generates three key outputs:

1. Main Animation:
   • Red rectangle: Truck
   • Blue rectangle: Trailer (ground truth)
   • Magenta dashed: LIDAR-estimated trailer
   • Green arrow: Radar heading estimate
2. Comparison Plot:
   • Black: Ground truth angle
   • Magenta: LIDAR estimate
   • Green: Radar estimate
   • Blue: IMU estimate
3. Error Plot:
   • Shows absolute error for each sensor over time
4. Performance Summary (Console Output):

### PERFORMANCE COMPARISON ###
LIDAR: RMSE = 1.866°
Radar: RMSE = 1.915°
IMU  : RMSE = 14.906°

5. Simulation Output

Key Findings

1. LIDAR:
   • Highest accuracy (lowest RMSE)
   • Sensitive to point cloud density
   • Performance degrades in fog/rain
2. Radar:
   • Moderate accuracy
   • Robust in adverse weather
   • Simple vector-based calculation
3. IMU:
   • Fastest update rate
   • Suffers from drift over time
   • Requires sensor fusion for long-term stability

Recommendations for Real-World Use

graph LR
    A[Application] --> B{Environment}
    B -->|Clear Weather| C[LIDAR]
    B -->|Adverse Weather| D[Radar]
    A --> E{Required Accuracy}
    E -->|High Precision| C
    E -->|Moderate Precision| D
    A --> F{Cost Constraints}
    F -->|Low Cost| G[IMU]
    F -->|Medium Budget| D
    F -->|High Budget| C

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License

This project is licensed under the MIT License - see the LICENSE file for details.