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