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

An open question in autonomous driving is how best to use simulation tovalidate the safety of autonomous vehicles. Existing techniques rely onsimulated rollouts, which can be inefficient for finding rare failure events,while other techniques are designed to only discover a single failure. In thiswork, we present a new safety validation approach that attempts to estimate thedistribution over failures of an autonomous policy using approximate dynamicprogramming. Knowledge of this distribution allows for the efficient discoveryof many failure examples. To address the problem of scalability, we decomposecomplex driving scenarios into subproblems consisting of only the ego vehicleand one other vehicle. These subproblems can be solved with approximate dynamicprogramming and their solutions are recombined to approximate the solution tothe full scenario. We apply our approach to a simple two-vehicle scenario todemonstrate the technique as well as a more complex five-vehicle scenario todemonstrate scalability. In both experiments, we observed an increase in thenumber of failures discovered compared to baseline approaches.

Scalable Autonomous Vehicle Safety Validation through Dynamic Programming and Scene Decomposition