CARLA Simulator
Integrating Object Detection with CARLA Simulator on Jetson Nano
Introduction
In the pursuit of advancing vehicle safety systems, the CARLA simulator proves to be an invaluable tool for testing and refining innovative algorithms.
CARLA, an open-source simulator for autonomous driving research, offers a sophisticated and dynamic environment for testing and validating algorithms in a virtual setting. Developed by the Computer Vision Center at the Universitat Autònoma de Barcelona, CARLA provides a high-fidelity simulation of urban and suburban environments, complete with realistic road networks, traffic scenarios, and dynamic weather conditions. Its modularity allows developers to customize various aspects of the simulation, such as vehicle models, sensors, and traffic density, ensuring a diverse range of testing scenarios.
CARLA's key features include a realistic physics engine, which accurately models vehicle dynamics and interactions, making it an ideal platform for testing algorithms that require precise vehicle behavior. The simulator supports a variety of sensors, including cameras, lidar, and radar, enabling developers to emulate the sensory input of real-world autonomous vehicles. The Python API facilitates easy integration with external systems and algorithms, making CARLA a versatile tool for research and development in autonomous driving. Overall, CARLA's realism and extensibility make it an indispensable resource for testing and refining algorithms before deploying them in actual driving scenarios.
We leverage CARLA's capabilities to evaluate the effectiveness of an object detection model, specifically MobileNetV2. The goal is to enhance real-world applications by deploying this model on an edge device, the Jetson Nano, to enable real-time decision-making and alert systems based on the detected environment.
Algorithm and Decision-Making Process
The algorithm implemented in this project follows a multi-step process. CARLA is installed on the host PC to simulate realistic driving scenarios. The front-view camera captures images, while additional data, such as the steering wheel angle and car velocity, is also acquired. These images and auxiliary data are then transmitted to the Jetson Nano for real-time processing. The MobileNetV2 object detection model is deployed on the Jetson Nano, providing accurate and swift identification of objects in the scene.
Utilizing the steering wheel angle and car velocity, the algorithm calculates the time to headway, a critical metric for assessing the risk of a collision. A bird's-eye-view window is generated, highlighting areas of potential danger based on the calculated time to headway. When the bounding boxes of detected cars intersect with the danger zone, the system triggers a Forward Collision Warning (FCW) alarm. This integrated decision-making process demonstrates the real-time, proactive nature of the system, showcasing its potential to prevent potential collisions.
Conclusion
This project illustrates the seamless integration of the CARLA simulator, MobileNetV2 object detection, and the Jetson Nano for enhancing vehicle safety. By leveraging the realistic simulations in CARLA, developers can thoroughly test and refine algorithms in a controlled environment before deploying them in real-world scenarios. The decision-making process, incorporating object detection results, steering wheel angle, and car velocity, showcases the potential of this system to provide timely warnings, contributing to the development of advanced driver-assistance systems. The combination of simulation and edge computing technologies holds promise for the continued improvement of vehicle safety features.
You can review my CARLA Python codes on GitHub: (click here)
You can see below a demo video of my project: