TRAP: Traffic-Based Adaptive Ramp Packing for Blind Cancellation in Autonomous Vehicles

Abstract

Autonomous Vehicles (AVs) rely on a set of radar sensors used to map surrounding environment. Most commonly used radar sensors for AVs use Frequency Modulated Continuous Wave (FMCW) ramps for object parameter (i.e., range and relative velocity) estimation. Due to large bandwidth requirement of FMCW radar, only a limited number of AVs can be operated in the available spectrum. Consequently, the co-existence of large number of AVs may lead to the problem of radar-to-radar interference, also referred to as radar blindness. Moreover, the problem becomes more severe in the higher traffic scenarios. In this work, we propose a Traffic-based Adaptive Ramp Packing (TRAP) scheme, which adapts radar range and assigns FMCW ramp parameters on the basis of inter-vehicular distance among AVs. Specifically, TRAP scheme allows to make effective use of the available time-frequency resource, and enables to pack more ramps in the dense traffic scenarios. Further, it is shown that adaptive radar range adoption may provide significantly more number of ramps in the given bandwidth. Furthermore, through simulation results, it is shown that TRAP significantly reduces the blind probability against state-of-the-art fixed range schemes.