Optimization of Velocity Ramps with Survival Analysis for Intersection Merge-Ins
This addresses safer and more efficient autonomous vehicle merging at intersections, but is incremental as it builds on existing risk and motion planning concepts.
The paper tackled the problem of motion planning for T-intersection merge-ins by optimizing velocity ramps using survival analysis to estimate gap success chances, showing lower absolute risk and better risk-utility tradeoff compared to heuristic or IDM-based methods.
We consider the problem of correct motion planning for T-intersection merge-ins of arbitrary geometry and vehicle density. A merge-in support system has to estimate the chances that a gap between two consecutive vehicles can be taken successfully. In contrast to previous models based on heuristic gap size rules, we present an approach which optimizes the integral risk of the situation using parametrized velocity ramps. It accounts for the risks from curves and all involved vehicles (front and rear on all paths) with a so-called survival analysis. For comparison, we also introduce a specially designed extension of the Intelligent Driver Model (IDM) for entering intersections. We show in a quantitative statistical evaluation that the survival method provides advantages in terms of lower absolute risk (i.e., no crash happens) and better risk-utility tradeoff (i.e., making better use of appearing gaps). Furthermore, our approach generalizes to more complex situations with additional risk sources.