Project Overview:

This project addresses the challenge of developing and testing autonomous driving systems by creating a high-fidelity simulation environment based on Grand Theft Auto V. By leveraging the game’s photorealistic graphics, complex physics, and diverse urban environments, the platform enables research on perception, decision-making, and control algorithms for autonomous vehicles.

Key Features:

Realistic sensor simulation including cameras, LiDAR, radar, and ultrasonic sensors
Full access to ground truth data including semantic segmentation, depth maps, and object positions
Programmable traffic scenarios with customizable vehicle behaviors
Weather and lighting condition control for testing robustness
Python API for integration with popular machine learning frameworks
Data collection pipeline for creating training datasets
Benchmarking suite for comparing different autonomous driving approaches

Technical Implementation:

The system consists of three main components:

Game Integration: A native plugin that interfaces with the game engine to extract rendering information, control vehicles, and modify the game environment.
Sensor Simulation: A physics-based simulation layer that generates realistic sensor outputs based on the game state, including camera images with proper distortion, LiDAR point clouds with appropriate noise characteristics, and radar returns.
Research Interface: A Python API that provides access to simulation controls, sensor data, and ground truth information, with built-in support for reinforcement learning environments and computer vision datasets.

The modification was implemented using a combination of C++ for the game integration and Python for the research interface, with optimized data transfer between the two.

Applications:

The platform has been used for various autonomous driving research applications:

Training and evaluating perception algorithms in diverse and challenging conditions
Developing reinforcement learning agents for urban driving scenarios
Benchmarking decision-making algorithms for complex traffic situations
Generating synthetic training data for machine learning models
Testing edge cases and rare events that are difficult to encounter in real-world testing

Impact:

By providing an accessible, high-fidelity simulation environment, this project helps accelerate autonomous driving research without the high costs and safety concerns associated with real-world testing. The open-source nature of the platform has enabled researchers from various institutions to contribute and build upon the framework.