Reachability Estimation in Dynamic Driving Scenes for Autonomous Vehicles

Abstract

Autonomous vehicles will find an infinite number of possible scenarios while driving in urban environments and need to react in a proper manner. For that reason, it is important to have algorithms that can propose driving alternatives for different type of scenarios in a global and unified way instead of using rule-based algorithms which depend on the driving scene. This paper presents a reachability estimation algorithm designed to obtain a safe and comfort-optimized trajectory set for different driving scenarios. First, a finite number of path candidates are created using Bézler curves. Then, all valid path candidates are combined with the reachable sets of dynamic obstacles to generate speed profiles consistent with safety and comfort requirements. The output of this algorithm would allow a decision-making strategy to select the optimum candidate depending on different criteria.