Artificial Intelligence-Based Pedestrian Detection and Trajectory Prediction Framework for Autonomous Vehicles.

Abstract

The safe and reliable operation of autonomous vehicles (AVs) in dynamic urban environments hinges on their ability to accurately detect pedestrians and anticipate their future movements. This research presents a comprehensive artificial intelligence-based framework for pedestrian detection and trajectory prediction, aiming to enhance situational awareness and proactive decision-making in AVs. Leveraging advanced deep learning models, including convolutional neural networks (CNNs) for pedestrian detection and recurrent neural networks (RNNs) with long short-term memory (LSTM) units for motion forecasting, the proposed system integrates real-time sensory data to identify pedestrians with high accuracy and predict their paths in complex, crowded, and cluttered scenarios. The framework is trained and validated on benchmark datasets such as the JAAD and ETH/UCY, incorporating environmental context, pedestrian intention cues, and temporal dependencies to ensure robust performance across varying conditions. Additionally, a hybrid data fusion strategy is implemented to combine visual information from cameras with LiDAR-based spatial data, significantly improving detection reliability under occlusion and low-visibility conditions. Extensive experimental results demonstrate that the proposed model achieves state-of-the-art performance in both detection precision and trajectory forecasting accuracy, outperforming traditional approaches in terms of responsiveness and generalizability. To further improve prediction fidelity, scene understanding is incorporated through semantic segmentation and map-aware modeling, allowing the system to infer pedestrian constraints like crosswalks, curbs, and road edges. Attention mechanisms and social pooling modules are integrated to handle multi-agent interactions and shared space dynamics. The end-to-end system demonstrates real-time processing capability suitable for onboard deployment in AVs, maintaining a balance between computational efficiency and predictive accuracy. Comparative analysis with baseline models highlights the superiority of the proposed approach in complex traffic environments and crowded urban settings. This study underscores the critical role of artificial intelligence in bridging perception and prediction in autonomous driving systems, contributing to safer navigation, reduced collision risk, and more human-like behavior understanding. The insights from this work not only push the boundaries of pedestrian intention modeling but also offer practical implications for the deployment of intelligent autonomous mobility systems in real-world urban infrastructures.