Integrating Ensemble Methods for Dynamic Obstacle Avoidance in Autonomous Vehicle Navigation

Autonomous vehicles represent a transformative shift in transportation, promising enhanced mobility and safety. However, the deep learning models currently used in this field frequently fail to achieve the real-time, robust performance required for safe and dependable operation. To overcome these limitations we introduce in this study, a novel real-time obstacle avoidance framework that leverages an ensemble deep learning approach combining the strengths of EfficientNet B4 and DenseNet201 architectures. A comprehensive dataset of 30,336 images was curated using data from three strategically placed cameras in the VSim-AV simulator, capturing diverse environmental conditions and various obstacles. Data augmentation and meticulous pre-processing techniques were employed to enrich the dataset, thereby enhancing the model’s adaptability to real-world driving challenges. The proposed ensemble was rigorously evaluated across multiple scenarios in autonomous mode, achieving a testing accuracy of 97.9% and an average reaction time of 0.15 seconds. These results not only validate the effectiveness of the ensemble model but also surpass the performance metrics of existing obstacle detection and avoidance studies in autonomous vehicles.