Design and Development of Autonomous Driving Car Using Nvidia Jetson Nano Developer Kit

The JetRacer framework provides an ideal platform for experimenting with autonomous driving technologies. By harnessing the GPU and AI capabilities of Jetson Nano the setup incorporates sensors, like cameras, LiDAR and IMUs to perceive surroundings in time. Advanced neural networks manage tasks such as recognizing objects staying within lanes and avoiding obstacles with decision-making aided by GPU acceleration. The software structure includes combining sensor data, planning movement and implementing control strategies. Assessments of performance showcase how the system functions reliably, across different scenarios. This study presents an intriguing design for a self-driving car model that utilizes the Jetracer AI framework, specifically crafted for autonomous driving cars. This model integrates various AI and ML frameworks. These frameworks are essential components used to implement object detection and recognition systems, enabling real-time capturing and classification of obstacles (Naveen et al in Memory optimization at edge for distributed convolution neural network (2022)). By leveraging the capabilities of these frameworks, the model can effectively detect and recognize obstacles on the road. This information is crucial for making informed decisions and responding appropriately, and the model utilizes the Jetson Nano interface to communicate responses to these obstacles. This integration enables seamless integration between the model and interface, allowing for smooth and efficient interactions (Febbo et al Autonomous vehicle control using a deep neural network and Jetson Nano (2020)). Overall, the design demonstrates the integration of various AI and ML frameworks within the Jetracer framework to achieve real-time object detection, recognition, and appropriate responses. This approach demonstrates the potential for creating effective autonomous driving models using these technologies, and the implementation of autonomous driving cars is a challenging task in the field of robotic control. However, autonomous driving systems have numerous benefits, particularly in terms of environmental impact. The use of self-driving vehicles has the potential to significantly reduce fuel consumption globally.