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

Autonomous driving involves complex decision-making in highly interactiveenvironments, requiring thoughtful negotiation with other traffic participants.While reinforcement learning provides a way to learn such interaction behavior,efficient learning critically depends on scalable state representations.Contrary to imitation learning methods, high-dimensional state representationsstill constitute a major bottleneck for deep reinforcement learning methods inautonomous driving. In this paper, we study the challenges of constructingbird's-eye-view representations for autonomous driving and propose a recurrentlearning architecture for long-horizon driving. Our PPO-based approach, calledRecurrDriveNet, is demonstrated on a simulated autonomous driving task inCARLA, where it outperforms traditional frame-stacking methods while onlyrequiring one million experiences for efficient training. RecurrDriveNet causesless than one infraction per driven kilometer by interacting safely with otherroad users.

Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representations