Attention-Based Deep Learning Approach for Pedestrian Detection in Self-Driving Cars

Autonomous vehicle safety relies heavily on the ability to accurately detect pedestrians, as this capability is crucial for preventing accidents and saving lives. Pedestrian recognition is particularly challenging in the dynamic and complex environments of urban areas. Effective pedestrian detection is crucial for ensuring road safety in autonomous vehicles. Current pedestrian identification systems often fall short in capturing the nuances of pedestrian behavior and appearance, potentially leading to dangerous situations. These limitations are mainly due to difficulties in various conditions, such as low-light environments, occlusions, and intricate urban settings. This paper proposes a novel solution to these challenges by integrating an attention-based convolutional bi-GRU model with deep learning techniques for pedestrian recognition. This method leverages deep learning to provide a robust solution for pedestrian detection. Convolutional layers are utilized to extract spatial features, attention mechanisms highlight semantic details, and Bidirectional Gated Recurrent Units (Bi-GRU) capture the temporal context in the proposed model. The process begins with data collection to build a comprehensive pedestrian dataset, followed by preprocessing using min-max normalization. The key components of the model work together to enhance pedestrian detection, ensuring a more accurate and comprehensive understanding of dynamic pedestrian scenarios. The implementation of this unique approach was carried out using Python, employing libraries such as TensorFlow, Keras, and OpenCV. The proposed attention-based convolutional bi-GRU model outperforms previous models by an average of 17.1%, achieving an accuracy rate of 99.4%. The model significantly surpasses Random Forest, Faster R-CNN, and SVM in terms of pedestrian recognition accuracy, which is critical for autonomous vehicle safety.