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Streaming perception is a critical task in autonomous driving that requiresbalancing the latency and accuracy of the autopilot system. However, currentmethods for streaming perception are limited as they only rely on the currentand adjacent two frames to learn movement patterns. This restricts theirability to model complex scenes, often resulting in poor detection results. Toaddress this limitation, we propose LongShortNet, a novel dual-path networkthat captures long-term temporal motion and integrates it with short-termspatial semantics for real-time perception. LongShortNet is notable as it isthe first work to extend long-term temporal modeling to streaming perception,enabling spatiotemporal feature fusion. We evaluate LongShortNet on thechallenging Argoverse-HD dataset and demonstrate that it outperforms existingstate-of-the-art methods with almost no additional computational cost.

LongShortNet: Exploring Temporal and Semantic Features Fusion in Streaming Perception