Simulating Vehicle Driving Using CARLA

Abstract

The demand for autonomous vehicles is driven by a combination of factors that are shaping the future of transportation. One significant aspect of the demand for autonomous vehicles comes from consumers who are increasingly interested in the potential benefits of self-driving technology. The convenience, safety, and potential cost savings associated with autonomous vehicles are appealing to many individuals, especially in urban areas where traffic congestion and parking challenges are prevalent. Consumers see autonomous vehicles as a way to improve their daily commute, reduce the stress of driving, and enhance overall mobility. Autonomous vehicles are incorporating a variety of sensors, including cameras, LiDAR, radar, and ultrasonic sensors, to improve perception capabilities. Sensor fusion techniques are being used to combine data from multiple sensors for more accurate and reliable object detection and tracking. Among these, the CARLA (Car Learning to Act) simulator has emerged as a leading open-source solution, offering a realistic and customized virtual environment for autonomous driving research. The increasing autonomy of vehicles necessitates a paradigm shift in testing methodologies. Traditional real-world testing is resource-intensive, time-consuming, and often constrained by safety concerns. Simulation offers a viable alternative, allowing researchers and developers to iterate and experiment rapidly in a controlled virtual environment. CARLA, as an open-source and extensible simulator, provides a valuable platform to address these challenges.