English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Motion forecasting is a key module in an autonomous driving system. Due tothe heterogeneous nature of multi-sourced input, multimodality in agentbehavior, and low latency required by onboard deployment, this task isnotoriously challenging. To cope with these difficulties, this paper proposes anovel agent-centric model with anchor-informed proposals for efficientmultimodal motion prediction. We design a modality-agnostic strategy toconcisely encode the complex input in a unified manner. We generate diverseproposals, fused with anchors bearing goal-oriented scene context, to inducemultimodal prediction that covers a wide range of future trajectories. Ournetwork architecture is highly uniform and succinct, leading to an efficientmodel amenable for real-world driving deployment. Experiments reveal that ouragent-centric network compares favorably with the state-of-the-art methods inprediction accuracy, while achieving scene-centric level inference latency.

ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals