Inaccurate or incomplete labeling of sensor data leads to failures in autonomous vehicle perception. Improving label generation ensures safer navigation in complex real-world environments.

Delayed or incorrect identification of surface conditions and environmental events. Improving detection speed and accuracy prevents system failures in autonomous driving and safety warnings.

High resource consumption and latency in deep learning models prevent deployment on edge devices. Reducing complexity enables real-time processing and lower power consumption.

Ambiguous spatial context and visual clutter lead to incorrect object classification. Accurate scene parsing ensures safe vehicle control transitions and obstacle avoidance.