Pedestrian Crossing Intention Prediction Using Multimodal Fusion Network
PositiveArtificial Intelligence
- A new study presents a multimodal fusion network designed for pedestrian crossing intention prediction, which is crucial for the safe deployment of autonomous vehicles (AVs) in urban settings. This network integrates seven modality features from visual and motion branches to enhance the prediction accuracy by leveraging depth information and spatial feature interactions.
- The development of this predictive model is significant as it aims to provide AVs with essential environmental cues, thereby reducing the risk of pedestrian-related accidents and improving overall traffic safety.
- This advancement aligns with ongoing efforts in the autonomous vehicle sector to enhance perception capabilities through various datasets and technologies, such as 4D radar and collaborative perception, which are essential for navigating complex urban environments and ensuring safety.
— via World Pulse Now AI Editorial System
