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

We present a novel approach for risk-aware planning with human agents inmulti-agent traffic scenarios. Our approach takes into account the wide rangeof human driver behaviors on the road, from aggressive maneuvers like speedingand overtaking, to conservative traits like driving slowly and conforming tothe right-most lane. In our approach, we learn a mapping from a data-drivenhuman driver behavior model called the CMetric to a driver's entropic riskpreference. We then use the derived risk preference within a game-theoreticrisk-sensitive planner to model risk-aware interactions among human drivers andan autonomous vehicle in various traffic scenarios. We demonstrate our methodin a merging scenario, where our results show that the final trajectoriesobtained from the risk-aware planner generate desirable emergent behaviors.Particularly, our planner recognizes aggressive human drivers and yields tothem while maintaining a greater distance from them. In a user study,participants were able to distinguish between aggressive and conservativesimulated drivers based on trajectories generated from our risk-sensitiveplanner. We also observe that aggressive human driving results in more frequentlane-changing in the planner. Finally, we compare the performance of ourmodified risk-aware planner with existing methods and show that modeling humandriver behavior leads to safer navigation.

Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers