Distance Estimation in Outdoor Driving Environments Using Phase-Only Correlation Method with Event Cameras

With the global proliferation of autonomous driving technology, the advancement of sensor technology is essential to ensure its safety. In particular, sensor fusion plays a crucial role in autonomous driving systems. However, equipping vehicles with multiple sensors leads to increased costs, making it necessary to have a single sensor that can perform multiple roles. This study focuses on event cameras, exploring their potential among various sensor technologies. Event cameras possess characteristics such as high dynamic range, low latency, and high temporal resolution, and they can also leverage visible light communication. This enables high visibility in low-light and backlit environments, as well as excellent performance in detecting pedestrian movements and acquiring traffic information between traffic lights and vehicles. These characteristics are particularly beneficial for autonomous driving systems. Additionally, if distance estimation functionality can be provided by the event camera, it allows a single sensor to perform multiple roles, offering significant advantages in terms of cost efficiency. In this study, we achieved distance estimation based on triangulation using an event camera and two points on an LED bar installed along a road. Furthermore, by employing the phase-only correlation method, we achieved sub-pixel precision in estimating the distance between two points on the LED bar, enabling even more accurate distance estimation. This approach performed monocular distance estimation in outdoor driving environments at distances ranging from 20 to 60 meters, achieving a success rate of over 90 % with errors of less than 0.5 meters. We are considering implementing position estimation in the future, with the current distance estimation technology forming the foundation for this. By achieving high-precision distance estimation, the vehicle's position relative to surrounding ITS smart poles can be accurately determined, enabling more precise position estimation. This will allow autonomous vehicles to know their exact position in real-time and select the optimal driving route based on surrounding traffic conditions and road conditions. Ultimately, we believe that this technology can contribute to the development of a smart transportation system in the city.