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

As a core part of autonomous driving systems, motion planning has receivedextensive attention from academia and industry. However, real-time trajectoryplanning capable of spatial-temporal joint optimization is challenged bynonholonomic dynamics, particularly in the presence of unstructuredenvironments and dynamic obstacles. To bridge the gap, we propose a real-timetrajectory optimization method that can generate a high-quality whole-bodytrajectory under arbitrary environmental constraints. By leveraging thedifferential flatness property of car-like robots, we simplify the trajectoryrepresentation and analytically formulate the planning problem whilemaintaining the feasibility of the nonholonomic dynamics. Moreover, we achieveefficient obstacle avoidance with a safe driving corridor for unmodelledobstacles and signed distance approximations for dynamic moving objects. Wepresent comprehensive benchmarks with State-of-the-Art methods, demonstratingthe significance of the proposed method in terms of efficiency and trajectoryquality. Real-world experiments verify the practicality of our algorithm. Wewill release our codes for the research community

An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments