Autonomous Vehicle Navigation Using Sensor Fusion Algorithms
TL;DR
This study examines low-, mid-, and high-level fusion approaches and proposes a hybrid framework using GPS/IMU for localization and LiDAR-camera fusion for obstacle detection, designed for real-time performance and robustness against noise and sensor failures.
Abstract
Autonomous vehicle navigation is a key component of modern intelligent transportation systems, relying on the integration of multiple sensors such as LiDAR, radar, cameras, GPS, and IMUs. Sensor fusion techniques combine data from these sources to improve perception, localization, and reliability. This paper reviews classical pre-2018 sensor fusion methods, including Kalman Filters, Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), and particle filters. Different sensors have individual limitations—cameras are affected by lighting, LiDAR is costly, and radar has lower resolution—but fusion enhances overall system performance by leveraging their complementary strengths. The study examines low-, mid-, and high-level fusion approaches and proposes a hybrid framework using GPS/IMU for localization and LiDAR-camera fusion for obstacle detection. The system is based on probabilistic and Bayesian models, designed for real-time performance and robustness against noise and sensor failures. Key challenges such as synchronization, calibration, and computational complexity are discussed. Results show that sensor fusion significantly improves navigation accuracy, highlighting the importance of selecting appropriate algorithms based on application needs.Overall, the paper emphasizes that multi-sensor fusion is essential for safe and reliable autonomous driving and provides a foundation for future advancements in the field.