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 planning in dynamically changing environments is one of the mostcomplex challenges in autonomous driving. Safety is a crucial requirement,along with driving comfort and speed limits. While classical sampling-based,lattice-based, and optimization-based planning methods can generate smooth andshort paths, they often do not consider the dynamics of the environment. Sometechniques do consider it, but they rely on updating the environment on-the-gorather than explicitly accounting for the dynamics, which is not suitable forself-driving. To address this, we propose a novel method based on the NeuralField Optimal Motion Planner (NFOMP), which outperforms state-of-the-artapproaches in terms of normalized curvature and the number of cusps. Ourapproach embeds previously known moving obstacles into the neural fieldcollision model to account for the dynamics of the environment. We alsointroduce time profiling of the trajectory and non-linear velocity constraintsby adding Lagrange multipliers to the trajectory loss function. We applied ourmethod to solve the optimal motion planning problem in an urban environmentusing the BeamNG.tech driving simulator. An autonomous car drove the generatedtrajectories in three city scenarios while sharing the road with the obstaclevehicle. Our evaluation shows that the maximum acceleration the passenger canexperience instantly is -7.5 m/s^2 and that 89.6% of the driving time isdevoted to normal driving with accelerations below 3.5 m/s^2. The driving styleis characterized by 46.0% and 31.4% of the driving time being devoted to thelight rail transit style and the moderate driving style, respectively.

DNFOMP: Dynamic Neural Field Optimal Motion Planner for Navigation of Autonomous Robots in Cluttered Environment