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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Automated Vehicles require exhaustive testing in simulation to detect as manysafety-critical failures as possible before deployment on public roads. In thiswork, we focus on the core decision-making component of autonomous robots:their planning algorithm. We introduce a planner testing framework thatleverages recent progress in simulating behaviorally diverse trafficparticipants. Using large scale search, we generate, detect, and characterizedynamic scenarios leading to collisions. In particular, we propose methods todistinguish between unavoidable and avoidable accidents, focusing especially onautomatically finding planner-specific defects that must be corrected beforedeployment. Through experiments in complex multi-agent intersection scenarios,we show that our method can indeed find a wide range of critical plannerfailures.

Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation