A Novel Lane Merging Framework with Probabilistic Risk based Lane Selection using Time Scaled Collision Cone

Abstract

Conventionally, planning frameworks for au- tonomous vehicles consider large safety margins and pre- defined paths for performing the merge maneuvers. These considerations often increase the wait time at the intersec- tions leading to traffic disruption. In this paper, we present a motion planning framework for autonomous vehicles to perform merge maneuver in dense traffic. Our framework is divided into a two-layer structure, Lane Selection layer and Scale Optimization layer. The Lane Selection layer computes the likelihood of collision along the lanes. This likelihood represents the collision risk associated with each lane and is used for lane selection. Subsequently, the Scale Optimization layer solves the time scaled collision cone (TSCC) constraint re- actively for collision-free velocities. Our framework guarantees a collision-free merging even in dense traffic with minimum disruption. Furthermore, we show the simulation results in different merging scenarios to demonstrate the efficacy of our framework.